Optimisation of Biogas Production A Socio Economic Value Chain Evaluation. Master Thesis

Transcription

1 Optimisation of Biogas Production A Socio Economic Value Chain Evaluation Master Thesis Lau Linnet Andersen Supervisors: Marie Münster, Nina Juul August 2013

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3 Dansk titel English title Author Supervisors Optimering af biogasprodukion - En samfundsøkonomisk værdikæde analyse Optimisation of biogas production - A socio economic value chain evaluation Lau Andersen, s Marie Münster, Senior Researcher, DTU Management - Division of systems analysis Nina Juul, Researcher, DTU Management - Division of systems analysis Published: 24. August 2013 Class Public Edition 1. Note This thesis is handed in, as a part of the final examination, in order to obtain a Master degree (MSc in Engineering) at the Technical University of Denmark (DTU). The thesis represents 30 ECTS-points. Rights c Lau Andersen, 2013

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5 Abstract The Danish politicians have by political visions and agreements increased the focus on biogas and the biogas sector in Denmark. The reason for the increased focus on biogas is that the production of biogas seems to have several benefits. Initially the manure and waste based biogas is considered as a CO 2 neutral fuel. The right use of the biogas can therefore contribute to a decrease of the CO 2 emissions from the energy production. Further, manure based biogas production can improve the efficiency of the manure when it is applied to the fields as fertiliser, also decreasing the emitting of greenhouse gases from the applied manure. This project seeks to formulate a socio economic optimisation model that optimises the value chain in joint biogas production. This includes both the resource transportation, the biogas plant, process heat solutions, distribution and end use of the biogas, including upgrade facilities and externalities in terms of increased fertiliser effects, changed emissions from the biogas production and use and also distortion losses. The study has revealed that assumptions with regard to plant investment costs and value of produced excess heat and power has high impact on the result. Also the value of the used resources is of high importance for the result. Further, the study has revealed that it is not unrealistic to produce biogas with a socio economic gain. In the base case is obtained an annualised cost to society of 23 million kr equalling a CO 2 shadow price 685 kr per tonne. By varying and optimising, based on the same scenario, this socio economic cost is both reduced and transferred into a small socio economic gain. Furthermore, the obtained results have proven that it is possible to model the value chain of biogas production in a socio economic context, obtaining comparable results. The formulated model is based on the Maabjerg Bioenergy plant, but is formulate in a generic way, making it adaptable to other plants. i

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7 Resume De danske politikere har gennem politiske visioner og aftaler øget fokus på biogasproduktion og biogassektoren i Danmark. Årsagen til dette skal findes i, at biogas kan have flere fordele, både med hensyn til miljø og energiproduktion. Først og fremmest er gylle og affaldsbaseret biogas betragtet som et CO 2 neutralt brændsel, og brugen af denne kan derfor være med til at sænke CO 2 udslippet fra energiproduktionen i Danmark. Herudover kan bioforgasning af gylle højne gødningsværdien af gyllen, og hermed være med til at reducere både emissioner og udvaskning af nærringsstoffer til lokale åer og vandløb. Formålet med dette projekt er at opstille en samfundsøkonomisk model, der optimerer værdikæden i forbindelse med biogas fællesanlæg. Dette inkluderer både transporten af ressourcerne, omkostninger til biogasanlæg, proces varmeproduktion på biogasanlægget, distribution og forbrug af biogassen til forskellige formål inklusiv opgradering, og eksternaliteter som værdi af øget gødningseffektivitet, ændrede emissionsforhold for biogas produktion og brugen af denne og de samfundsøkonomiske dødvægtstab som biogassen medfører. Studiet har vist at antagelser med hensyn til anlægsinvesteringer og værdi af produceret overskudsvarme og strøm, har stor indflydelse på resultatet. Også værdien af de ressourcer som bruges i processen har stor betydning. Studiet har vist at det ikke er umuligt at producere biogas med et samfundsøkonomisk overskud. I den grundlæggende case opnås et annualiseret samfundsøkonomisk tab på 23 millioner kr, svarende til en CO 2 skyggepris på 685 kr pr ton. Ved at variere og optimere med udgangspunkt i den samme case, kan dette tab både sænkes markant, og vendes til et mindre samfundsøkonomisk overskud. Videre har de opnåede resultater også vist, at det er muligt at opstille en samfundsøkonomisk optimeringsmodel af biogas produktion, og opnå brugbare og sammenlignelige resultater. Modellen er opstillet med udgangspunkt i Måbjerg Bioenergy anlægget, men er formuleret på en generisk måde, således at den kan benyttes til andre anlæg også. iii

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9 Preface This thesis is a master thesis conducted at the Department of Management Engineering, Division for systems analysis, Risø campus, at Technical University of Denmark. The project has been carried out in connection to the Biochain research project and in cooperation with Måbjerg Bioenergy. The thesis has been produced as the final part of the Master in Sustainable Energy - Thermal Energy at the Technical University of Denmark - DTU, in the period from the March 25 to August 24, 2013, and represent 30 ECTS points. The purpose of the project has been to formulate a socio economic optimisation model for the value chain of biogas production. The intention has been to formulate a generic model based on the Måbjerg Bioenergy plant. The thesis has been supervised by Senior Researcher Marie Münster, and co-supervised by Nina Juul, both from Department of Management Engineering, Division for systems analysis. Lau Linnet Andersen August 24, 2013 v

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11 Acknowledgement Behind this thesis is a great amount of work and support from a large group of supervisors, supervisor colleagues, collaborators, friends and family, all need to be thanked. First of all, at the Division for Systems Analysis at DTU Risø, great thanks to Marie Münster, for general supervision and leaving the door open for professional discussion and applicable feedback in relation to the conduction of the thesis, and also providing great contacts in relation to the project. Also at Division for Systems Analysis at DTU Risø, thanks to Lise Nielsen for supervising with regard to some of the more challenging economic terms and energy production subsidy schemes. Thanks also to Nina Juul and Ida Jensen for supervision with respect to the linear programming. In the work with this thesis several collaborators has been involved, and in connection to that Allan Lunde, consultant at Vestforsyningen A/S has been a great help and good guide into the Måbjerg Bioenergy plant. Thanks also to Jørgen Sørensen, plant manager at Vinderup combined Heat and power plant for showing the biogas fuelled technology and introducing the basis for purchasing it. In addition to that, also thanks a lot to Jens Christian Pedersen, board member at Måbjerg Bioenergy amba, pig farmer and manure supplier to Måbjerg Bioenergy, for introducing the pros and cons for supplying to Måbjerg Bioenergy and further explaining the general logistics related to livestock farming. Further, also thanks to Søren Kræmer, consultant at Grontmij, for taking the time to introduce me to the consultant approach to biogas production. At last, but not least, I would like to thank Jesper Oxbøl, Kristian Kahle and Troels Hansen for providing priceless support with respect to Excel, GAMS and Latex, and affording useful discussions and feedback on relevant topics. Also thanks for serving superior coffee, every day within the last 5 months, and keeping the mood up, even in the late working hours. vii

12 Contents Contents List of Figures List of Tables Terminology, Notation and Abbreviation viii x x xii 1 Introduction The Aim of the Study Report structure The Maabjerg Case Maabjerg Bioenergy - the idea The set up Biogas output Socio Economic Evaluation Methodology / Procedure Other Studies Socio economic assumptions Technology Assumptions & Description Farm supply Transport technologies Biogas plant Heat supply & power producing technologies Biogas upgrade to Natural gas quality Substituted production and demand Model Description Economy The Objective Function Supply - Farms & Industry Transport Biogas Plant viii

13 CONTENTS ix 5.6 Distribution End use Emissions and Fertilizer reduction Distortion Losses Implementation Results The Maabjerg Case - As it is Maabjerg - optimisation Sensitivity analysis Discussion The Maabjerg Case General discussion on method and assumptions applied General Applicability of the model Conclusion 73 9 Further Work 75 Bibliography 77 Appendix A Visit in Holstebro 83 A.1 Vinderup visit A.2 Maabjerg visit A.3 Farm visit B Prices 93 B.1 Prices on electriciy, fuel and district heat, [4] C Subsidy 95 C.1 Subsidy rules, [3] D Technology data & Emissions 97 D.1 Truck technologies D.2 Heat supply technologies D.3 Upgrade technologies D.4 Emissions E Preproject 103 F Recources available & Biogas potentials 123 G Biogas plants in Denmark & Danish studies 127 H Model symbols 131 H.1 Sets H.2 Scalars H.3 Parameters

14 H.4 Variables List of Figures 2.1 Figure a illustrates the biomass input distribution and b shows the corresponding expected biogas production distribution Schematic illustration of the Maabjerg biogas setup Price development without inflation, [4] Illustration of the serviced farms Figure a is an illustration of the investment b shows the corresponding heat demand Illustration of different purchasers Annualised values of the simple socio economic analysis Annualised values of the total original analysis Resource use in optimisation scenarios including resources available Annualised cost in each step Sensitivity analysis of increasing the power price Sensitivity analysis of increasing the district heat price Sensitivity analysis of increasing the district heat price List of Tables 2.1 Biomass and biogas distribution in absolute numbers Socio economy vs. Private economy [28] Comparison of socio economic values and boundaries x

15 LIST OF TABLES xi 3.3 Comparison of socio economic values and boundaries Annualised prices[4] Economic calculation values Subsidies & fees [3] Value of subsidy & fee exemption - Biogas substituting Natural gas Biogas quality CO 2 equivalents [5] Emission economic annuity values Assumed average farm size applied in the model Heat use calculations and values Separation efficiency - Centrifuge [27] Supply nomenclature Transport nomenclature Biogas plant nomenclature Distribution nomenclature End use and upgrade nomenclature Emission nomenclature Distortion and dead weight losses nomenclature Suppliers & related investments Biomass resources, prices and expected biogas production Supplied farm distribution Transport economy Biogas plant economy Distribution economy Biogas and energy distribution Biogas end use Simple economic evaluation Fertilizer effects Emissions effects Total avoided CO 2 equivalence from entire chain Distortion and dead weight losses Total economic overview Constraints in optimisation scenarios Resource use & farm investments optimisation Optimisation transport costs Biogas plant optimisation economic values Allocation of biogas in optimisation scenarios Optimisation distribution cost Fertilising effects Emission values, [5] & [4] Distortion losses Optimisation scenarios total D.1 General Work assumptions, trucks [19] & [24] D.2 Truck data D.3 Heat supply technologies D.4 Water scrubber upgrade technology [18]

16 xii LIST OF TABLES D.5 Transport emission values D.6 Biogas emission values D.7 Natural gas emission values D.8 Other emission values F.1 Resources available F.2 Biogas potentials F.3 Nutrient Content

17 LIST OF TABLES xiii Terminology Dry part - describes the low viscous part of the separated manure End use - is used as a collective name describing heat plants, combined heat and power plants and industry, where the biogas is use to produce heat and power, or upgraded to natural gas quality. Purchaser - in this thesis purchaser is used to describe the biogas users in terms of heat and combined heat and power plants, despite the biogas plant itself. Shadow price - is used to describe the abatement cost, in this thesis the CO 2 abatement cost equals the CO 2 shadow price. Supplier - is used to describe the farmers, industry and waste water slurry suppliers. Treated - in this thesis treated is used to describe the anaerobic digestion process at the biogas plant, meaning that treated manure, is manure that has been digested at the plant. Wet part - is used to describe the high viscous part of the separated manure Abbreviations & Notation Bg - short for BioGas. Bng - short for Bio Natural Gas, equal to upgraded biogas. BGP - short for BioGas Plant. CHP - short for Combined Heat and Power. DCE - short for Danish Center for Environment and Energy at University of Aarhus. DH - short for District Heat. DWL - short for Dead Weight Loss, explained in subsection IFRO - short for Department of Food and Resource Econemy at University of Copenhagen. kr - is the Danish currency, kroner, could also be written Dkk GAMS - short for General Algebraic Modelling System, the optimisation software used for this thesis. NG - short for Natural Gas. NPV - short for Net Present Value, explained in subsection OM - short for Operation and Maintenance. Process - describes heat used for industrial purposes.

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19 CHAPTER 1 Introduction In Denmark approximately 4 PJ of biogas is produced every year. In a total yearly energy consumption of approximately 800 PJ, the biogas accounts for approximately 0,5% [1]. This biogas production origins from industrial waste products, waste water treatment and farm based resources. In 2012, 22 joint biogas plants and 60 farm based plants existed, treating approximately 2,5 million tonnes of manure and 0,5 million tonne of organic waste. This accounted for approximately 6% of the total manure production [29]. Induced by the vision presented by the Danish government "Grøn Vækst" from 2009, and the political energy settlement of 2012 containing a target of using 50% of the manure for biogas production in 2020, the biogas production in Denmark has experienced an increased and renewed interest. With these political visions and agreements, the biogas sector has received both new provisions and targets. To reach the target before 2020 demands a huge effort and large investments within short time. The reason for the increased focus on biogas, is that the production of biogas seems to have several benefits. Initially the manure and waste based biogas is considered as a CO 2 neutral fuel. The right use of the biogas can therefore contribute to a decrease of the CO 2 emissions from the energy production. Further, manure based biogas production can improve the efficiency of the manure, when it is applied to the fields as fertiliser. Furthermore is the emitting of greenhouse gases from the applied manure decreased, as the gas content in the manure is converted and utilised as biogas. The increased efficiency of the fertiliser induces a reduced demand for synthetic fertiliser and reduces the amount of nutrients leached to the local environment. The increased focus and attention to the biogas sector has also entailed a few research projects within the field. In the last few months two larger studies has been published from Danish Centre for Environment and Energy, Aarhus University(DCE) [25], and Department of Food and Resource Economics at University of Copenhagen(IFRO) [19]. The studies mainly focuses on the biogas production, and the relation to the agricultural sector, but does not concern the use of biogas very detailed. This project is done in relation to the Biochain research project, which is carried out by University of Southern Denmark, Technical University of Denmark, University of Aarhus, University of Copenhagen and Knowledge Centre for Agriculture. The general aim of the Biochain project is to provide scientifically based, sound solutions for resilient and sustainable large scale Danish biogas production. This is done by value chain analysis, computational models for biogas production and environmental impact, leading to new analytical procedures for dynamic assessments of economics and biomass flow [9]. The project presented here is a sketchy analysis of some of the 1

20 2 CHAPTER 1. INTRODUCTION main topics in the large scale project. 1.1 The Aim of the Study The object of this study is to formulate a socio economic optimisation model, that seeks to optimize the resource use, the transportation, the plant size, the distribution and the end use of the produced biogas, in a socio economic evaluation. This is done by identifying the important relevant technologies for the single steps related to the production and use. By adding socio economic prices and values, the goal is to formulate a model that can easily provide reasonable and accurate socio economic evaluations of a given case, and by adding or relaxing the constraints, also provide an optimal solution. The model will be used to identify which economic and physical conditions are necessary to provide a socio economic gain, when evaluating the entire biogas value chain. The model will be based on the recently build plant in Maabjerg, Holstebro in the western Denmark, but constructed in a generic way, implying that more general cases can be analysed with the model. It will be sought to validate the model by comparing the results obtained, with the presented basis for the Maabjerg project. The obtained economic values will be evaluated both in regard to the assumptions made, and compared to the results obtained in other relevant studies. Further, it is the intention to validate the flexibility of the model by investigating the possibilities and challenges attached to implement other biogas case scenarios, e.g. a projected plant in Tønder. The project is a continuation of a short introduction project conducted in the early The final report of that project can be seen in Appendix E Boundaries The intention is to model the effects of treating the manure at a biogas plant. This includes identifying the biogas potential in the used resources, leading to a heat and power potential. In combination with applied utilisation opportunities for the biogas, the optimal socio economic use is revealed. This is done by optimising the investment and operation costs related to both the production of the biogas, the transportation of the manure products, and the distribution and end use of the biogas. Also the resource combination is optimised, both with regard to the biogas production, but also the externalities in terms of increased fertiliser effects and change of emissions in relation to the entire chain. The biogas utilisation is also optimised, in order to cover applied demands, in terms of technology choice and biogas use, but also in order to abate emissions from traditionally heat and power production. The heat demand from the biogas production is covered by a biogas fuelled heat supply, at the plant. The main data regarding biogas potentials are adapted from the IFRO model, which are modified according to information gathered in relation to the project. Technology data is taken from the technological descriptions provided by Energistyrelsen in combination with information obtained from Maabjerg Bioenergy and Vinderup combined heat and power plant. The emission values are applied from Danish Centre for Environment and Energy, earlier DMU.

21 1.2. REPORT STRUCTURE Report structure Chapter 2 contains an introduction to the Maabjerg Bioenergy plant, describing the simple boundaries of the base case. Chapter 3 introduces the concept of socio economic evaluation and how it is applied in this study. Further are the main assumptions in the model explained here. Chapter 4 is a description of the different technologies and their characteristics applied in the single steps of the model. Chapter 5 contains an elaborate description of the optimisation model formulated and the structure of it. Further it is described how the assumptions explained in chapter 3 are applied. Chapter 6 reveals the results obtained with the model, including the optimisations and the sensitivity analyses. Chapter 7 discuss and evaluates the obtained results and the formulated model. Chapter 8 concludes on the obtained and discussed results. Chapter 9 presents possible extensions and ideas for further work in connection to the study. Sources are indicated with a number on the form [-] where they are used. These are used both to indicate the source of a single value or general assumptions from a specific source, or if a specific section is based on a certain source. A complete list of the sources can be seen at the end of the report.

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23 CHAPTER 2 The Maabjerg Case The underlying basis of the model developed for this report is the Maabjerg biogas plant.this chapter contains an introduction to the Maabjerg case, describing the general resource and biogas flows and details that distinguishes the Maabjerg plant from other biogas plants. A short description of crucial assumptions regarding the model formulation is also a part of the chapter. 2.1 Maabjerg Bioenergy - the idea The Maabjerg plant is located on the outskirts of Holstebro in Jutland, Denmark. The idea of making a biogas plant goes back to 2002, where the first initiatives for making a plant was taken. From here it took ten years before the plant was a reality, as the first load of manure was received in January 2012 leading to the official commissioning of the plant in August 2012, [22]. The motive behind the project was an environmental report revealing that the local fjords and streams were heavily affected by the local livestock production, demanding a reduction in the production of about 25%. In order to maintain the livestock production and hereby maintain local jobs, the idea of building a biogas plant emerged. That implies that the Maabjeg biogas plant has been intended more as a manure treatment plant than an energy producing plant, see section A.2 in appendix and [22]. The basic resource input of the plant is manure from the surrounding pig, cattle and mink production. Altogether the manure accounts for approximately 70% of the input resources, or about tonnes of a total of tonne see Table 2.1 and Figure 2.1. These numbers are roughly based on those obtained in appendix section A.2, with the fibre part applied later, as it was necessary in order to reach the required production. The remaining part is organic waste in terms of a whey product from a local dairy and slurry from the local wastewater treatment facility, [22]. Theoretically, this should lead to a production of about 18 million m 3 of biogas. 2.2 The set up A rough sketch of the set up at Maabjerg is displayed in Figure 2.2. From the sketch it appears how the different resources are delivered to the plant. At the plant they are separated in a "green-" and a "slurry" line respectively. The reason for that are strict regulations regarding the control with the fodder used for dairy cattle. Fields used for that kind of fodder, cannot receive fertiliser containing residues from waste water slurry. The produced biogas is brought to the customers, and used at the combined heat and power engines at the plant. The excess heat from the plant and the produced heat from the decentralised combined heat and power plants are delivered to the district heat network and the electricity is 5

24 6 CHAPTER 2. THE MAABJERG CASE (a) (b) Figure 2.1: Figure a illustrates the biomass input distribution and b shows the corresponding expected biogas production distribution send to the national grid. From the sketch it also appears that some of the manure is brought to the plant by a pipeline. This was the original idea, and an idea that was supported by the EU Ecostiler project [22]. The system it is not running at Maabjerg yet, which is why it initially has been left out from the modelling. The fertilizer from the green line of the plant, which is the treated and separated wet fraction of the manure, is brought back to the farmers. The general intention with the separated fibre part, was to dispose it to the local combined heat and power plant, Maabjergværket. Here it was supposed to be burned in a separate burner in order to produce heat and power. The produced ash would be very rich in phosphor and should therefore be sold as fertilizer. The required investments at Maabjergværket though has never been taken, which is why the fibre is instead sold to subcontractors that uses the fibre directly as fertilizer. The fibre input to the green line was not a part of the initial set up at Maabjerg, but has been added in order to produce the expected amount of biogas, [6]. The plant is designed as a mesophile facility, meaning that the temperature level in the reactor tanks is relatively low, about 37 degrees Celsius, see section A.2 in appendix. This means that the hydraulic retention time of the resources in the reactor tank is longer than it would have been, if the temperature level was higher, as in a thermophile facility. The lower temperature level therefore requires larger reactor tank volumes. The advantage of the low temperature level is that the process is much more stable at that level, meaning that the need for control systems is lower, and the process can adapt more biomasses, such as mink manure and fibre resources. A more thorough description of the processes at a biogas plant can be found in appendix Appendix E, and also in [32] and [33]. 2.3 Biogas output As mentioned above, the Maabjerg plant is designed to receive approximately tonne biomass, as displayed in Table 2.1. That should result in a biogas production of about 18 million m 3 of biogas. That biogas is partly distributed to Vinderup decentralised combined heat and power plant, where it is used to supply heat to the local district heat network. They purchase approximately 7 million m 3 biogas, see section A.1 and section A.2 in the appendix. The next priority purchaser is the near by placed combined heat and power plant Maabjegværket. They receive about 3,5 million m 3 biogas. The remaining 7 million m 3 biogas is used at the biogas plant to produce heat for the process, and to the district heat network, as well as electricity to the grid.

25 2.3. BIOGAS OUTPUT 7 Farmer Input Slurry input Truck transport Pipeline Pipeline Truck transport Truck transport Pipeline Manure Whey Potato Pulp Fibre Slurry / Sludge Wet reactor output Wet reactor output Green line input Slurry line Fertilizer Fertilizer slurry Green line digester Slurry line digester Fiber output Fiber output Separation Separation Biogas Heat Electricity MBE Gas engine Biogas Måbjergvær ket Flare Vinderup Kraftvarmeværk Fibre Fertilizer Pipeline Fertilizer Fibre Resource Storage District heating Electricity District heating Truck transport Truck transport Truck transport Hedeselskabet Company Process Varmeforsyningssel-skabet Holstebro/Struer Subcontractors Farmers Holstebro centralrenseanlæg Figure 2.2: Schematic illustration of the Maabjerg biogas setup

26 8 CHAPTER 2. THE MAABJERG CASE Table 2.1: Biomass and biogas distribution in absolute numbers Biomass input [24] Biogas output Biogas Output [tonne] [1000 m 3 ] [m 3 /tonne] Manure Cow ,8 Manure Pig ,0 Manure Mink ,2 Fibre parts ,2 Dairy waste ,2 Sludge ,8 Total

27 CHAPTER 3 Socio Economic Evaluation This chapter contains a short introduction to the concept socio economic evaluation, and how it is performed in this report. The evaluation follows the guidelines presented by Energistyrelsen in Vejledning i samfundsøkonomiske analyser på energiområdet, [16] which is further explained in [17]. The overall object of a socio economic analysis is to evaluate a project or an investment from a societal perspective by revealing pros and cons in order to perform a qualified suggestion for use of resources, both financial and environmental. It is important when conducting a socio economic evaluation, that the method used is clear. It should be evident what is accounted for and what has been omitted or neglected, in order to make a socio economic evaluation useful and comparable to other evaluations. A socio economic evaluation does not necessarily supply clear answers, and the results of a socio economic analysis cannot stand alone, but has to be evaluated to the assumptions and boundaries used in the specific analysis, and also the account of externalities and non quantifiable effects. Consequently the result of a socio economic evaluation can be separated into two parts- an initial part containing the actual investment- and operation costs and the simple income, in terms of avoided production elsewhere, induced by the investment, all cleared for taxes(the method presented in [28]). In this study this will be referred to as a simple socio economic evaluation. In order to obtain a more complete results, the other part concerning the externalities associated with the project, in terms of e.g. avoided emissions and fertilizer effects, should also be included. A simple way to distinguish between a simple socio economic evaluation and a private economic evaluation is displayed in Table 3.1. For example is saved emissions of CO 2 included as a saved cost, in terms of saved CO 2 quotas, in a private economic analysis. In a socio economic evaluation abated emissions are valued as an externality benefit, in terms of its value to society, and not a direct income or reduced cost. The same accounts for electricity and heat produced as explained in section In order to asses the validity of obtained results, a sensitivity analysis is an important part of a socio economic analysis, [16] and [28]. All economic evaluation of long term projects has challenges with long term predictions for fuel, energy and labour costs, which can be quite unpredictable. Therefore all analysis entails some uncertainties on the results. In and follows a more thorough description of these. For socio economic evaluations there is further an issue on the valuation of externalities related to a given project. These will be explained further in

28 10 CHAPTER 3. SOCIO ECONOMIC EVALUATION Table 3.1: Socio economy vs. Private economy [28] Socio Economics Private Economics Prices Real terms Nominal prices (no inflation) (including inflation) Discount rate Socio determined Market determined Real terms Nominal terms - here 4.0% [12] Including risk premium Revenues Normally only costs Payment schedules important Conditions Excluding taxes etc. Including taxes, depreciation rules etc. Lifetime Technical lifetime Economic lifetime (20 years) (<20 years) In general, a socio economic evaluation can tell the effects of a given project or investment on society, but it does not reveal whether a completely different technology or use, could achieve twice the gain, with the same investment or resource use. 3.1 Methodology / Procedure This section contains a description of how the reference case is modelled in this evaluation. This is followed by a section describing how the socio economic evaluation is performed Reference case In order to identify the changes from a given case, it is important to have a well defined reference case. The strategy of this report has not been to construct a complete reference scenario to compare to, but to identify the changes induced by investments and chosen technology. This means for example that only the change in nitrogen leaching, caused by the change in fertilizer efficiency from treating the slurry, is calculated. The answer does not clarify whether the change is only a small fraction of the total leach, only the absolute value of the change. Also in the case of analysing potential investments in new technology, only the actual investments are analysed, meaning that no optional investments at the plants are generally taken into account. This means that only the relevant biogas solutions are investigated and optimised in this report, not for example a solution of solar heating systems. The reference consequently represents a scenario with an unchanged continuation of the existing conditions. Along with the reference case, also the choice of boundaries are of high importance. The ambition of the conducted evaluation has been to include as much as possible of the biogas chain, see the description of the general case in chapter 2. This means that the project concerns both the collecting and transportation of the resources, the biogas plant, biogas distribution pipelines and heat and power production at decentralised plants, upgrade facilities and end use. These links are all analysed with respect to both investment and operation costs, which will be explained further in section The change in emissions from the entire chain is also calculated. These emissions origins from both the changed amount of transportation, the possible substitution of heat and power from both the biogas plant and the other purchasers and possible use in the transport sector. These will be explained further in The increased fertilizer effects from use of the treated slurry

29 3.2. OTHER STUDIES 11 is also included, along with the negative effects on the carbon content of the soil, when treating the slurry before returning it to the fields. This also includes the reduced demand for synthetic fertilizer, but also the decrease in nutrient leach, caused by the increased efficiency of the nutrients in the fertilizer, all further explained in section The losses caused by the subsidies to energy production from biogas is included as well, along with the changes in national income from fees from heat and power production, further explained in section Socio economy The outcome of a socio economic evaluation, is the total of the cost benefit analysis, which the socio economic analysis basically is. The outcome is the total net present value of the investigated scenario, compared to the reference case, in a given time period[16]. The socio economic evaluation can reveal which socio economic gains that can be achieved in a given case, with the specific technology and use, compared to the reference scenario. The costs related to a project of this type, is typically investment costs, operation costs, including both fuel costs, salaries, administration, maintenance etc. Negative effects on the environment but also in tax income is included in the costs. The benefits contains the value of the positive environmental effects, and also the positive value of the produced goods, in terms of gas, electricity and heat. NPV = T t=1 B t C t ( 1 + R) t (3.1) The total Net Present Value of the scenario can be calculated from (3.1)[16], where B t is the total benefits of year t, C t is the total costs of year t, and r is the discount rate. All prices used has to be at the same level, which is often the level of the base year, year 0. This means that the result of (3.1) also returns the net present value in price level of the base year. 3.2 Other Studies Recently, similar studies of biogas production in Denmark has been conducted, with quite different assumptions. This section contains a short introduction to the two large studies; 1) Socio- Economic Evaluation of Selected Biogas Technologies from Danish Centre for Environment and Energy(DCE) at University of Aarhus[25], published in June 2013, and 2) Biogas production in Denmark - evaluation of private and socio economics (Biogasproduktion i Danmark - Vurderinger af drifts og samfundsøkonomi, in Danish) from Department of Food and Resource Economics at University of Copenhagen[19], also published in June Both studies are theoretical studies with no relation to actual plants. Further, both studies contain several scenarios, but in this thesis is referred to two specific scenarios, most similar to that of Maabjerg Bioenergy. The main assumptions and boundaries of the two studies are displayed in Table 3.2. The referred DCE study is scenario 1a, consisting of 100% pig slurry, with a daily input of 800 tonnes, corresponding to a yearly resource use of tonne. The referred IFRO scenario is scenario 4b, with 77% manure and 23% deep litter, at a daily level of 700 tonne corresponding to as also illustrated in the table. The size of the two plants are in the same range, with the DCE plant being approximately 10 % larger than the IFRO plant. The corresponding relative plant investment cost per tonne is 10 % higher in the IFRO study compared to the DCE study. The two studies are similar in the way that they both have a high share of manure input. The deep litter fraction in the IFRO study, is also based on manure, but with a high fibre content. However

30 12 CHAPTER 3. SOCIO ECONOMIC EVALUATION this entails a big differences with regard to biogas potential. Therefore the two studies operates with very different biogas production per tonne input resource, 45.1 m 3 per tonne for the IFRO study, and 18,5 for the DCE study. Looking through Table 3.2 it is obvious that there are significant differences between the two studies. The DCE study does not include investments at the biogas end users, whereas the IFRO study include an investment of 2 million kr at one plant. Moreover, the IFRO study includes an investment of kr at 200 farmers, whereas no investments at the farmers appears in the DCE study. The kr used in the IFRO study does also seem very low, compared to the information obtained, when visiting the farmer supplying to Maabjerg Bioenergy, see appendix section A.3. Both studies includes production of electricity at the biogas plant in combination with the required process heat. However the value of the produced power differs significantly with a value of 0,47 kr per kwh and 0,72 kr per kwh applied in the DCE and the IFRO study, respectively. The end use of the produced biogas are in both studies valued in terms of the energy content in the biogas, compared to natural gas. None of the studies include upgrading of the gas. The transportation is modelled in details in the IFRO study. In the DCE study, the transportation cost is applied as a constant value multiplied with the number of kilometres driven. Here the constant value accounts for the investment, the maintenance, the operation and everything. The two values are displayed in Table 3.2. From the table it appears that the value obtained in the IFRO study is larger than the value used in the DCE study. Also the plant employee cost is significantly higher in the IFRO study than in the DCE study, with a value of 4,7 and 2,2 kr per tonne per year respectively, in the two studies. The two scenarios presented here, ends up with very different welfare economic costs and CO 2 shadow prices. The net taxation factor used in the DCE study represents an old value, which is much lower than the one used in the IFRO study. As the value is applied to almost all costs, the lower NTF value in the DCE study actually reduces the total socio economic cost, compared to the IFRO study. Further, also and old set of subsidies is applied in the DCE study, which is much lower than those applied now, which are also implemented in the IFRO model. However, reasons for the DCE study ending up with such huge socio economic cost are several. Initially, the study referred here, has a very low biogas production per tonne input, as presented in Table 3.2. This low production, combined with a high heat heat demand from the process, due to a low degree of heat recirculation, implies that approximately 30% of the energy content in the produced gas is used for process heat at the plant. In the IFRO study less than 10 percent is used for process heat. With regard to the externalities, the socio economic evaluation in the IFRO report is not very transparent, and it is not evident what the different economic values represents. An overview of the externalities included in the two studies, is presented in Table 3.3. Though both studies evaluates the fertiliser effects from treating the slurry, the DCE study contain a much more thorough evaluation of the fertilizing effects, compared to the IFRO study. This relates to all three sub topics within the fertiliser effects. The calculated emissions and what is included is also much more clear in the DCE report, compared to the study from IFRO, though the emission work in the DCE report neither seems complete. None of the two studies values the avoided emissions from the biogas use in the energy sector, but it is considered when finding the CO 2 abatement cost. The two studies take very different subsidies into consideration. In the DCE study an investment support is considered, whereas the support scheme from the energy agreement is implemented in

31 3.3. SOCIO ECONOMIC ASSUMPTIONS 13 Table 3.2: Comparison of socio economic values and boundaries Assumption Unit DCE [25] IFRO [19] Plant size [tonne/year] Plant price [mio. kr.] 67,00 84,00 Plant price [kr/tonne/year] 229,5 328,8 Gas production [mio. m 3 bg/year] Manure fraction [%] Fibre fraction [%] 0 23 Energy crops fraction [%] 25 0 Industrial waste fraction [%] 0 0 Waste water slurry fraction [%] 0 0 Total average dry matte content [%] Investment end use [yes/no] no yes Heat & Power sale plant [yes/no] yes yes Heat & Power sale end use [yes/no] no no Upgrade to NG quality [yes/no] no no Price development [yes/no] Yes Yes Net tax factor [%] Dead weight loss [%] Discount rate [%] 4 5 Time frame of project [Years] Energy agreement implementation [Year] Investment subsidy [yes/no] yes no Transport costs [kr/tonne/year] Heat use process [KWh/tonne] Employees [kr/tonne/year] Total costs [mio. kr] 72, Total welfare economic costs [mio. kr/year] -6,45-9,1 CO 2 abatement [tonne/year] Obtained CO 2 shadow price [kr/tonne] the IFRO study. The distortion losses from the subsidy schemes are determined with a pre determined allocation of the produced biogas in the IFRO study, with 75% used in biogas engines and 25% used in biogas boilers. In general none of the two studies take into account the effect from reduced obnoxious smell from the treated manure, when it is applied in the field. In the IFRO report the value of it is considered negligible, whereas it is not mentioned in the DCE study. The reason for the huge difference between the obtained CO 2 reduction cost in the two studies also relates to different things. Initially the large difference in the biogas potential in the used resources,combined with the higher heat use, is a major reason, as already mentioned. Further the use of deep litter used in the IFRO study represents a condensed amount of manure, which both entails a high positive effect on the fertiliser effects and also induces a high biogas production, which increase the difference. Together these differences are the main reasons for the difference in both CO 2 abatement costs and total annualised costs, between the two studies. 3.3 Socio economic assumptions This section contains an overview of the general prices and costs used in the model, along with descriptions of what the costs and benefits consist of.

32 14 CHAPTER 3. SOCIO ECONOMIC EVALUATION Table 3.3: Comparison of socio economic values and boundaries Externalities DCE [25] IFRO [19] Fertiliser effects Reduced fertiliser use Included Included Reduced nitrogen leaching Included Included Emissions from carbon reduction Included Included Emissions - from transportation Included Included - from biogas use at plant Included Included Distortion losses Investment subsidy Included - Energy agreement Included Prices & Net Tax Factor The costs and prices used in a socio economic evaluation is in general not the actual cash flow, but a calculated flow with welfare economic accounting prices. The reason for this, is that the socio economic value of a project, is an indication of the change in social welfare[16]. The prices of investments and costs of use and associated to the use of goods, are factor prices, meaning prices without fees and taxes. These represents the value that could have been produced in alternative use of the resources. This alternative use, would have been taxed from production and through to the use, giving the consumer or accounting price level. In order to account for that, the Net Taxation Factor is used(from now on NTF). The NTF expresses the average level of taxation, and is calculated from the ratio between GDP and BFI (gross value added), [16]. By multiplying the prices in factor level with the NTF, the prices ends up in accounting level. Typical prices and costs declared in factor prices are the investment prices, operation costs, maintenance costs, administration, fuel and resource prices. Also CO 2 prices from [4] are given in factor level, which means that the value of the emitted green house gasses, also has to be multiplied with the NTF according to [16]. On the other hand, the values of emitted SO 2 and NO 2, also from [4], are given at consumer level, meaning that they do not have to be multiplied with the NTF. In general, a socio economic evaluation does not include revenues from sale. In the studies mentioned in section 3.2, the produced gas has been valued according to the energy content, compared to natural gas. Both studies also include an income from electricity sale, induced by the excess electricity produced in combination with the heat produced at the plant. Both prices does not represent an actual income, but an avoided use of natural gas or a cost for abated electricity production elsewhere. As this study includes investments at the end use, the avoided electricity and district heat production is valued, instead of the gas. The Nordpool unweighed average factor electricity price, and the forecast of it, provided by Energistyrelsen and displayed in Figure 3.1 and appendix section B.1, has been used as an approximate value for the electricity production price, in the time frame of the analysis. In the same way is the avoided district heat production valued with the district heat price, also provided by Energistyrelsen and displayed both in Figure 3.1 and in section B Investment, Operation & Fuel prices The prices used for investments and operation, are a mixture of values from similar projects, own assumptions, and values from the technology description in [18], given by Energistyrelsen and

33 3.3. SOCIO ECONOMIC ASSUMPTIONS 15 Energinet.dk. The exact origin of the specific value, are all illustrated in the spreadsheet part of the model, and outlined in the relevant parts of the appendix. The majority of these prices are associated with great uncertainty, though it has been attempted to obtain as accurate values as possible. 700,00 600,00 kr 500,00 400,00 300,00 200,00 100,00 0, Year Natural gas price, socio economic kr/gj District heat price kr/mwh Nordpool electricity price kr/mwh Electricity price Industry kr/mwh Figure 3.1: Price development without inflation, [4] Some of the prices used may be expected to change significantly within the time frame of the analysis, besides the omitted inflation. It is important to allow for these developments, even though they posses a high rate of uncertainty. An example of prices that must be expected to change significantly throughout the time horizon is the price of emitting CO 2, the quota price. Also the heat, power and fuel prices must be expected to change significantly within the next 20 years. The price developments used for both heat, power, diesel and natural gas are taken from [4], and are displayed both in Figure 3.1 and in appendix section B.1. In Table 3.4 are shown the annualised prices. A similar important set of prices in the analysis is the fuel prices. This means both natural gas and diesel prices. The biogas price is expected to follow the natural gas price, when corrected for the energy content ratio. The applied natural gas price is also displayed in section B.1. A sensitivity analysis is performed on the important prices, e.g. heat and power prices. Table 3.4: Annualised prices[4] Electricity average 355 kr/mwh Electricity industry 558 kr/mwh District heat 245 kr/mwh CO 2 quota, [5] 210 kr/tonne Diesel 5,02 kr/l Natural gas price 67,8 kr/gj Discount Rate & Lifetime The time frame of the project is the technical lifetime, as mentioned in Table 3.1, which is set to 20 years as displayed in Table 3.5. This length expresses the expected minimum lifetime of the main components through the chain, but in this study it is also based on the knowledge of the length of contracts made at Maabjerg energy, see appendix section A.2. Here, all contracts for both supply of biomass and purchasing of biogas has a duration of 20 years. Buildings and other parts of the

34 16 CHAPTER 3. SOCIO ECONOMIC EVALUATION project may work for several further years, whereas e.g. trucks has to be replaced already during the investigated time frame. Extending the time horizon further may also increase the uncertainties on the price developments of fuel, labour and emissions. The discount rate is set to 4.00%, as also displayed in Table 3.5, based on the value given by Energistyrelsen in [12]. Table 3.5: Economic calculation values NTF 1.35 [-] Net Taxation Factor DWL 0.2 [-] Dead Weight Loss [13] R 4.00 [%] Discount rate [12] T 20 [year] Time frame of analysis Dead Weight & Tax Distorion Losses Subsidy schemes Initially, the last energy agreement from the Danish government has introduced a high degree of subsidy on power and heat produced from biogas. Even though the schemes are yet to be accepted by the EU, they are applied in this study. The subsidy schemes can be divided into four different cases; Heat production from boiler for either district heat or process heat. Combined heat and power in e.g. turbine or engine. Upgrading biogas to natural gas quality. Biogas used for transportation. The subsidy varies a bit from case to case, with the highest subsidies applied to electricity from combined heat and power production and upgrade of the biogas to natural gas quality, all displayed in Table 3.6. The base subsidy applied on the electricity can obtain two different values chosen by the producer; either a general electricity price of 0.79 kr per kwh electricity produced or a subsidy of kr per kwh electricity produced in addition to the market price. The last solution is the one investigated in this report, also displayed in Table 3.6. Heat production from boilers, also receive subsidy according to the purpose, either process or district heat. The subsidy given to biogas used directly for transport purposes has been omitted in this study. The extra subsidy of 26 kr per GJ and 0,26 kr per kwh respectively, depends on the price of natural gas. The subsidy is decreased each year corresponding to the difference in kr between the actual natural gas price and a base price of 53,2 kr per GJ. Is the actual price lower, the subsidy will increase. Accordingly is the electricity subsidy regulated with 0,01 kr per kwh. The subsidy of 10 kr per GJ and 0,10 kr per kwh respectively is decreased linearly each year, with 2 kr and 0,20 kr respectively, each year, from January 2016 ending in with 2019 [3]. Using biogas in a biogas engine with an electric efficiency of 40%, the base subsidy will corresponds to 47,89 kr per GJ thermal input, displayed in (3.2). Moreover should be added the two subsidies of 26 and 10 kr per GJ respectively, but as they are phased out, they are for simplicity left out here. A further explanation and illustration of the subsidy rules can be seen in the appendix, section C.1.

35 3.3. SOCIO ECONOMIC ASSUMPTIONS 17 Table 3.6: Subsidies & fees [3] Technology Base subsidy Extra 1 Extra 2 Unit Boiler - District heat - 26,0 10,0 kr/gj heat Boiler - Process heat 39,0 26,0 10,0 kr/gj heat Combined heat & power 0,431 0,26 0,10 kr/kwh power Upgrade 79,0 26,0 10,0 kr/gj upgraded gas subsidy base bg engine = 0, 413 kr kwh 1000 kr 40% = 47, 89 3, 6 GJ (3.2) Distortion losses Change in the energy production will induce a change in the proceeds of the society. When the biogas is used in the heat and power production, assumed to substitute natural gas, the increased subsidy payment will be a cost to the society. The financing of these subsidies can be considered as an increase in the taxation. This implies that the subsidy has a socio economic value in terms of a loss [19]. These changes are in general named distortion losses or dead weight losses. These changes will be accounted for as a cost for the project. The Danish ministry of finance estimates that this loss accounts for 20% of the taxed value[16] and [13], as also displayed in Table 3.5. If it is assumed that no change in the electricity or heat consumption is induced, the impact can be divided into two groups; increased subsidy costs, and decreased taxation income. The high degree of subsidy payment simply induces a dead weight loss to the society, equal to the total increased distribution of subsidy for the biogas multiplied with the dead weight loss and the NTF. The losses caused by the tax distortion are a bit different. In relation to biogas they consist of five parts; a CO 2 fee exemption, an energy fee exemption, a security of supply fee, a NO x fee and a methane fee, all summed up in Table 3.7. Initially the CO 2 fee exemption of the biogas compared to natural gas leads to a tax income loss. For 2013 the CO 2 fee is set to 0,364 kr/nm 3. With a lower heating value of 39,6 MJ per Nm 3 [15], that corresponds to a tax of 9,20 kr per GJ, see Table 3.7. Using biogas, that tax is omitted, brining a loss to the society, equal to 9,20 kr per GJ. In previous studies biogas has been assumed to be exempted from the energy fee. Further investigations and the governments work with the new taxation on security of supply has revealed, that due to the EU legislation, a fee has to be charged. The fee though, will probably be rather low compared to the energy fee on natural gas, [10] and [11]. Therefore it is assumed in this study, that the savings in terms of energy fee, achieved by the use of biogas, is equal to the energy fee on natural gas. That energy fee is defined as 2,395 kr per Nm 3 corresponding to 60,48 kr per GJ. The energy fee though, is only imposed on 80% (1/1,25) of the fraction of the fuel used for heat production[17]. The calculation for the saved energy fee when using biogas instead of natural gas is displayed in (3.3), when assuming a heat efficiency of 55%. That leads to a save of 26.6 kr per GJ biogas used instead of natural gas, for the user. Energy fee = 60, 48 kr GJ 55% kr = 26, 61 1, 25 GJ (3.3)

36 18 CHAPTER 3. SOCIO ECONOMIC EVALUATION The fee on emitting NO x is on the other hand higher on biogas, compared to natural gas, with 5,05 and 3,37 kr per GJ respectively. That induces a small tax income of 1,68 kr per GJ to the society. A small service obligation fee is also introduced on all fuels from now, developing until It is assumed that the fee, similar to the energy fee, is only applied on the fraction of the energy used for heat production, [19]. The fee on biogas compared to natural gas will be 7.5 kr per GJ lower, according to [19]. That corresponds to a difference of 3,30 kr per GJ thermal input, according to (3.4), assuming the same heat efficiency. Service fee = 7, 5 kr GJ 55% kr = 3, 30 1, 25 GJ (3.4) Finally a lower fee on emitting methane exist on biogas compared to natural gas, which also induces a loss. The fee is 1,60 and 1,10 kr per GJ for natural gas and biogas respectively, [19]. That leads to a loss of 0,50 kr per GJ thermal input. The size of the different values are displayed in Table 3.7, for a biogas engine, with a heat efficiency of 55%. Table 3.7: Value of subsidy & fee exemption - Biogas substituting Natural gas Engine Boiler - DH Unit (η power = 0, 4) (η heat = 0, 99) Base subsidy 47,89 0,0 kr/gj CO 2 tax exemption value 9,20 9,20 kr/gj Energy fee exemption 26,61 47,90 kr/gj Security of supply fee -3,30-5,94 kr/gj NO x fee - difference -1,68-1,68 kr/gj Methane fee - difference 0,50 0 kr/gj Fee exemption total 31,3 49,48 kr/gj Total 79,2 49,48 kr/gj The applied subsidy of 47,90 kr per GJ plus the tax exemption of 31,3 kr per GJ gives a total of kr per GJ thermal input biogas substituting natural gas, in a biogas engine with an electric efficiency of 40% and a heat efficiency of 55%. In that is left out the two extra subsidies of 0,26 and 0,10 kr per kwh respectively, applied to the electricity production. Corresponding does district heat production from boilers, only induce a loss of 49,5 kr per GJ thermal input. Process heat production contain some rather complicated taxation rules and possibilities for avoiding them, which are not included in the model. In order to find the actual effect to the society, the dead weight loss factor is used, combined with the net taxation factor, as displayed in (3.5), leading to a distortion loss of 21,39 kr per GJ thermal input in a biogas engine, when leaving out the last two subsidies. The last two subsidies are included in the model, but is left out here, for simplicity, as they are decreasing fast within the next years, hence having a lower impact on the total distortion loss. Distortion loss = 79, 22 kr kr 1, 35 0, 2 = 21, 39 GJ GJ (3.5)

37 3.3. SOCIO ECONOMIC ASSUMPTIONS Resources and gas potentials The manure resources are assumed to be available around the plant, according to the table displayed inappendix F. The resource allocation is approximated to fit the illustration of the farm locations in [31]. It is assumed that the amount of pig and mink manure is larger than the amount used. An average farm size is assumed, which is further described in section 4.1 The manure resources are assumed to be produced fairly even throughout the year. This means that the resources can be supplied to the plant in approximately the same quantities almost every day. The expected biogas production is very dependent on the values used for biogas potentials. In chapter 2 is a short overview of the expected production from the used type of resources. A more thorough description of the values used, can be seen in the spreadsheet part of the model and in Appendix F. These values are combined with a factor that accounts for a possible heat loss from e.g. descaling or in summer, as it is done in [19]. The factor can also account for an increased production entailed by degassing of the treated slurry, and both effects can be put in to the model. As default though, and in the primary investigations, both values are set to zero. In the evaluation, the gas quality presented in Table 3.8 has been used. The quality is used due to the information obtained from Vinderup combined heat and power plant, displayed in appendix section A.1. The value is a bit lower than the value used in general in biogas evaluations, which is typically 60 %. Table 3.8: Biogas quality Methane content biogas [34] 55 % Energy content biogas 5,45 kwh Emissions & leaching This section contains a description of the derivation, allocation and valuation of the different types of emissions, caused by the production and use of biogas. Further it contains a description of how the leaching of nitrogen and phosphor is calculated. Emissions All the energy used in the project is related to some sort of emissions. That includes both electricity use at the biogas plant and for gas distribution, fuel use in transportation, and the end use of the biogas. The emissions from transportation relates to the chosen technology, all described with a set of emissions related to the technology used and the use of fuel. The emissions related to transportation are directly proportional to the amount and type of fuel used. The increased use of electricity is assumed to emit emissions equal to the average emissions of electricity production in Denmark in 2012 to 2031, provided by Energistyrelsen, in [4], and displayed in appendix section D.4. The electricity and heat produced from biogas at combined heat power plants and in the industry is primarily allocated to either power, combined heat and power, process or district heat production. This means that excess power from process heat production, combined district heat and power, and boiler produced district heat is treated separately, in terms of substituted production. The substituted technology can be specified in the spreadsheet part of the model. The default technologies are the average electricity production, for the excess power, natural gas engines for the combine heat and power production, and natural gas boilers for the district heat production. The process heat at the plant is not set to substitute anything as the process heat demand arises from the project, whereas excess heat and power will substitute other production on the net. The

38 20 CHAPTER 3. SOCIO ECONOMIC EVALUATION upgraded gas can also be dedicated to substitute either natural gas fired gas or steam turbine, or diesel or gasoline in the transportation sector. The valuation of these emissions can be divided into two groups; the group of green house gases including CO 2, CH 4 and N 2 O, and the group of SO 2, NO x and particles. The green house gas emission are appraised from the CO 2 quota value and the CO 2 equivalents displayed in Table 3.9, whereas the other emissions are appraised from either the values of stationary facilities, given in [4], or from transportation values given in [5], and all displayed in Table According to [17], the effects from using the biogas for power production does not translate into an equal climate effect. That is due to the EU Emission Trading Scheme, ETS. The scheme entails that the reductions caused by the use of biogas entails a release of quotas for CO 2 emissions that will be used for production elsewhere, as the number of quotas is constant. This means that the total amount of CO 2 emissions from energy production will remain unaffected of the biogas use. It also means that the value of the avoided CO 2 emissions from the substituted energy production should be left out of the socio economic evaluation [17]. If the emitting of CO 2 from the total energy system is assumed constant, it must also be reasonable to assume, that an increased use of electricity will not induce any further emittance of CO 2. This general approach will be discussed further in chapter 7. Table 3.9: CO 2 equivalents [5] CO CH N 2 O Table 3.10: Emission economic annuity values Emission [kr/kg] Stationary use [4] Transport [5] CO CH N 2 O SO NO x particles Fertiliser effects The change of emissions entailed by the use of the treated slurry consist of several parts; Increased efficiency of Nitrogen, decreases demand for synthetic fertilizer Reduced emittance of N 2 O due to the reduced use of synthetic fertilizer and increased efficiency of the treated slurry Reduced methane emittance because of treatment Reduced leaching of Nitrogen and Phosphor because of increased efficiency Reduced Carbon content in the soil because of treatment

39 3.3. SOCIO ECONOMIC ASSUMPTIONS 21 Initially the increased efficiency of the applied treated manure, reduces the demand for synthetic nitrogen fertilizer. This reduced use of synthetic fertilizer induces a reduction of N 2 O emissions, as the synthetic fertilizer applied to the fields emits approximately 1 % of the applied nitrogen to the air, [25]. The changed viscosity of the slurry combined with the treatment of it, also leads to a reduction of emitted N 2 O. The reduction is from 1.4 to % for untreated and treated pig manure, and a reduction from 2.0 to 1.0 % of the nitrogen content of the slurry for manure from cows,[25]. The atomic weight ratio between nitrogen and N 2 O entails that the total reduction is the sum of the two effects times 44/28. The CH 4 emissions also decreases with the treatment of the slurry. The untreated manure is rather fast brought to the biogas plant. The return product does not at all emit methane in the same way. More exactly does the treatment of pig slurry leads to a reduction of 0.9 kg CH 4 per tonne treated slurry and about 0.95 kg per tonne with regard to manure from cattle. These changes in emissions are included in the total emission accounting of the scenario. The treatment of slurry also entails a reduction of carbon content in the slurry, caused by the methane production, which further induces a reduction of the carbon content in the soil. The reduction is set to 45 kg carbon per tonne of dry matter in pig slurry and kg per tonne dry matter for cattle slurry, [25]. The change does not represent a loss to the farmer, but is tantamount to the increased CO 2 content in the atmosphere, which is why it is also evaluated with the CO 2 quota value. In order to achieve the entailed CO 2 production, the obtained carbon reduction in tonne is multiplied with the atomic ratio of 44/12, equalling The reduced leaching of nitrogen and phosphors to the local environment are also highly economic valuable. These reductions of leaching can also be found from the characteristics of the used slurry. The reduction is calculated from the area on which the application of synthetic fertilizer is reduced. It is assumed that 140 kg of nitrogen is added to each hectare. This is divided with the nitrogen content of the treated slurry, in order to obtain the amount of slurry that has to be applied to each hectare, and further how many hectares are covered. According to [21], the resulting reduction is in the order of about 3 kg per hectare. This reduction is valued to 40 kr per tonne [19] and [25]. A possible separation of the treated slurry, before applying it to the fields, will also induce a reduction in the leaching of phosphor. The phosphor in general attach to the dry fraction of the slurry according to [27] and [26], which is why a separation of the dry fibre parts of the slurry, will remove an extensive fraction of the phosphor. The phosphor is important to the soil, but not in the amounts applied with the untreated slurry. As the phosphor attach to the solid parts, it stays on top of the soil when applied, where it is easily carried away by rain and wind,[23]. According to the information given by slurry supplier, see appendix section A.3, some of the farmers that supply manure and receive the treated end product suffer from a little shortage on phosphor, due to the separation. As explained in chapter 2 the reduction of the phosphor leaching was one of the key stones when projecting the Maabjerg plant. This means that the reduction of phosphor leaching is highly valuable to the society, though it has not been possible to find a useful economic value yet Externalities not included Some effects from investments are very difficult to predict and others are hard to give an economic value. Those are difficult to implement in a socio economic analysis and especially in a mathematical model, though they might be very important to the result. Not included in this analysis are the effects on the number of jobs maintained or created by increased investments and production of biogas.

40 22 CHAPTER 3. SOCIO ECONOMIC EVALUATION A dependency of buying hectares of new field in order to maintain the livestock production, could also be applied as a cost, which could be avoided with a biogas plant. It is though difficult to judge if that investment is necessary, or the manure could have been disposed elsewhere. Further the reduction of obnoxious smell from the manure, entailed by the treatment and the changed viscosity, is not included. According to the IFRO study, this value is not significant. The externality effect from increased road transportation has not been included as well, as it has been realized that the total value is not significant. The emissions related to the transportation of the litter resources and the separated fibre parts, has been left out. The retention of a farming industry is very important in peripheral regions, where other jobs might be hard to find. Therefore such projects might be of a high value to the local society, but this effect has also been omitted in this evaluation.

41 CHAPTER 4 Technology Assumptions & Description This chapter contains a short description of the technologies modelled for the different steps of the model. An introduction to the applied emissions is also given. The applied specific values can be found in Appendix D. 4.1 Farm supply In order to model the investments from the farmers, an average farm size has been assumed. The size is in terms of supplied manure in tonne per year and is displayed in Table 4.1. The numbers are assumed from the number of different farms displayed in the resource allocation figure in [31], in combination with the actual amount of manure supplied, and the approximate known number of suppliers, of about 140, [30]. The sizes are only applied for the wet manure farm suppliers as the litter suppliers are assumed to have no costs related to deliver. Table 4.1: Assumed average farm size applied in the model [tonne/year] Pig 4780 Cattle 3333 Mink Transport technologies The main transport issue, is the transportation of the resources and the return of the treated parts. This can initially be separated into two categories; pipeline and truck transportation. The manure is as a rule transported by truck, due to a lack of data on the possible pipeline transportation. The transport of the fibre parts is initially omitted, and incorporated in the purchasing price. The option between truck and pipeline therefore only exist for the industry and slurry resources, that can be pumped. The truck option is further separated into two possibilities. That includes a standard diesel truck and a dual fuel diesel and gas truck. The trucks are defined with investment costs, load size, fuel use and maintenance costs. The use of a gas fueled truck further requires a filling station, which is applied as a required co investment. The data of the trucks can be seen in appendix section D.1. 23

42 24 CHAPTER 4. TECHNOLOGY ASSUMPTIONS & DESCRIPTION A general set of information about work hour per day, work days per year and time constants for driving and emptying or filling is also applied, and can also be seen in the same appendix. 4.3 Biogas plant Pre-treatment All resources with a high content of fibre demands some sort of pre-treatment, before they can be added to the process. In this study, the pre-treatment has been left out. Regarding the fibre parts used in the model, it is assumed that the pre-treatment is included in the resource cost Biogas process heat demand The heat demand at the biogas plant is important in order to dimension the heat supply. The heat demand is simplified dependent of the temperature of the resource when it arrive, the process temperature and the degree of heat loss. A simple calculation can be performed, when it assumed that the heat capacity of the resource mix is equal to water and that the heat capacity is constant within the investigated temperature interval. The general and simplified heat demand correlation is displayed in Equation (4.1), where T represents temperature interval. The degree of heat reuse(η reuse ) and heat lossη loss can be combined to one number, as done in Table 4.2, where the total heat demand per tonne resource is also displayed. This variation is implemented along with an increased investment price per tonne resource treated per year, with increased heat reuse and decreased process temperature level, as also explained in section 5.5. The general investment with pre storage, reactor tanks, internal pipelines, gas storage and heat exchangers are all combined into one investment cost per tonne. MJ Heat demand = 4.18 tonne K T (1 (η 1 loss η reuse )) 3, 6 MJ kwh (4.1) Table 4.2: Heat use calculations and values Description Reuse Temperature difference Heat use - η loss η reuse T - [%] [K] [kwh/tonne] Mesophilic - high reuse ,97 Thermophilic - high reuse ,93 Thermophilic - low reuse , Subsequent separation In order to achieve the majority of the fertilizing benefits from treating the slurry, a separation of the treated resource mix is performed. This separation increases the viscosity which further improves the ability to penetrate the soil, of the produced fertilizer. Further the separation of the the treated slurry removes the majority of the phosphor, which is one of the main pollutant substances from the manure. The investment in separation technology is not implemented separately, as it is included in the general plant investment. In terms of modelling, the separation is included as percentage distribution of nutrients between the two end products from the separation process, and can be seen in Table 4.3. The implemented technology is a centrifuge separator.

43 4.4. HEAT SUPPLY & POWER PRODUCING TECHNOLOGIES 25 Table 4.3: Separation efficiency - Centrifuge [27] Name In fibre part Nitrogen (N) 23,6% Phosphor (P) 72,7% Potassium (K) 0,0% 4.4 Heat supply & power producing technologies The use of biogas is a major part of the optimisation, and the definition of technology is of course important in order to obtain reliable results. The heat supply technologies initially contained in the model, is a biogas boiler, a biogas engine and a biogas turbine. Further is applied a biogas engine combined with a heat pump, in order to model the solution at Vinderup. The boiler is characterised by the fact that it only produces heat whereas the engine and the turbine produces both heat and power. The technologies are assigned with possible size intervals, efficiencies, investment costs, operation and maintenance costs and a maximum runtime per year. The power consumption related to the heat pump, is applied as a lower electricity efficiency of the combined biogas engine. Further details can be seen in appendix, in section D Biogas upgrade to Natural gas quality An important part, when investigating the possibilities in biogas production is the upgrading process. In order to liberate the demand from local heat demand, upgrading of the gas is an obvious answer. In this model, two types of upgrading has been implemented. The difference between the two is more an indication of the benefits from up scaling, than an expression of two different technologies. The modelled technology is water scrubbers and the technologies are described by a size interval, a scalable investment cost, operation costs, power demand and a gas loss, and can be seen in appendix section D Substituted production and demand The produced biogas is allocated to different purchasers. In the model, the purchasers are assigned with a district or process heat demand, combined with a possible flexibility in the demand. Further a possible additional investment is assigned together with a pipeline distance. The optimisation decides which purchasers to supply, and with which type of technology, in order to achieve the best results. The possible upgraded gas can be allocated to different technologies within some given limits. These limitations are assigned to a local and a national grid respectively, in order to reflect the local and national possibilities. It might not be realistic to supply a huge amount of upgraded gas to a local gas network, in an area with no gas fuelled transportation. The only object of allocating the upgraded gas to different technologies, is to find the avoided emissions and the corresponding value of it.

44

45 CHAPTER 5 Model Description The formulated model is a mixed integer linear programming problem, intended to optimize the socio economic gain, related to biogas production in a given case. As described in chapter 1, the purpose is to optimize the entire chain in the biogas production, meaning both the resource use, the transportation technology used, the type of plant, and where and how to use the produced gas. This chapter contains a theoretical description of the formulated model. The boundaries and specifications of the case are outlined in the Excel spreadsheet part of the model, which holds both the given inputs and the results of the performed optimization. The GAMS part of the model imports the boundaries and specifications, carries out the optimisation and exports the relevant results to Excel, where they can be examined. Both parts of the model are divided into smaller sections according to the division used in this chapter from section 5.3 to 5.9, and linked through the relevant variables and limitations. This chapter will go through the optimisation model, both the relevant input to each section, but also how the optimisation model is organized in each section. The general idea is to sum up the total investment, operation and maintenance, emission costs and heat and power sale respectively, find the net present value and annualise these in order to make them comparable. Model Nomenclature The model contains four types of information; sets, scalars, parameters and variables. A set contain the names given to identify specific values in a string of data, e.g. a parameter or a variable. In this model the overall sets are named with a capital calligraphic letter, e.g. R. The use of a set is denoted with the corresponding lower case letter, for example r, assigned to the parameters and variables in the subscript as for example C r. A subset is given by the same name combined with two or three letters in superscript, describing the sub part, e.g. R ma. The scalars are individual constants describing e.g. the energy content of natural gas. The corresponding set of scalars are arranged in parameters. Both are given with one or two capital or Greek capital letters, often accompanied with a small description in the superscript. For the same type of parameters or scalars the same letter are used, with different superscripts, e.g. A truck t and A sup r. Parameters varying with more than one set, can be arranged in matrices. In this chapter they are all named parameters, regardless they contain several parameters. The variables are given by one, two ore three lower case letters, with a describing superscript. The majority of the variables can adopt every value, whereas some of the variables are subjected to some constraints. Some have to be larger than zero, others have to be an integer. The variables given by a v and a superscript are 27

46 28 CHAPTER 5. MODEL DESCRIPTION binary decision variables that can either be zero or one. These variable constraints are displayed along with the variables in the tables describing the variables. A complete list of the symbols used in the model formulation, can be seen in Appendix H. The tonne unit on all parameters and variables in this chapter, indicates tonne per year. 5.1 Economy The economic values in each section of the model are of very different kinds; some are one time initial investments, others are reinvestments in the middle of the examined project, some are uniform maintenance costs that has the same size throughout the project, and others are costs that develops throughout the project. In order to make these costs comparable in a formula like the one displayed in (3.1), some pre calculation has to be done. Initially the Net Present Value(NPV) of an investment is found, as shown in (5.1) with the investment (inv) and the discount rate(r). The annuity factor(an), used to annualise costs, is calculated from the length of the investment(t) and the discount rate, as displayed in (5.2). By multiplying the net present value of an investment with the annuity factor, the annualised cost of the given investment can be found. The reinvestments are treated similar by finding the net present value of the reinvestment, using the reinvestment year and multiplying it with the annuity factor, as shown in (5.3). inv NP V = AN = ( inv ) 1 + R ( inv inv an = inv NP V AN, inv re,an re = (1 + R) tre (5.1) R 1 (R) T (5.2) ) AN (5.3) As the overall project is a socio economic evaluation, the prices are kept at a constant level, as explained in chapter 3. Fuel and energy prices must be anticipated to change significantly throughout the evaluated time frame. To compensate for that, the net present value of the total price development is found from the formula displayed in (5.4). The obtained value is then multiplied with the annuity factor, in order to identify the total annualised price. The subsidies are treated in the same way. The reason why this is possible, is that a uniform production throughout the evaluated time frame, is considered. This means that both fuel and energy use, along with the production that can receive subsidy, are treated in this way. The value of a smaller production in e.g. the first operation year or a year with huge amount of maintenance, can be found from the net present value of the lost production subtracted the net present value of the total production in the evaluated time frame. PRI NP V = T t=1 PRI(t) ( 1 + R) t (5.4) The other prices that are kept at the same level throughout the time frame, are directly comparable to the obtained annualised costs. In this way is the expression shown in (3.1) reduced to a single year, which means that the total net present value can be found from (5.5). The optimisation is performed directly on the annualised costs, as the conversion to net present value, has no effects on the optimisation since the annuity factor is a constant. In the model formulation here, the net taxation factor is assumed applied to the relevant prices. NP V = Ban C an AN (5.5)

47 5.2. THE OBJECTIVE FUNCTION The Objective Function The overall objective function of the model is, as already mentioned, the maximisation of the socio economic gain in terms of the total annualised cost. The simple and final objective function is displayed in (5.6). Opposite the net present value equation, (5.5), this function is not split directly into benefits and costs. Instead the objective function is divided in accordance with the division of the model. The first term, z sup, is the total annualised cost from farm and industry investments related to supply to the biogas plant. Also any operation and maintenance costs related to the resource supply, excluding transportation, is contained here. The term z tra contains the similar expenses associated to the transportation of the resources, both truck and pipeline, and the return of the wet fraction of the treated manure to the farms. The z bgp contains the total annualised cost of the investments and operation of the biogas plant. This includes the value of excess heat and power, produced at the plant. The distribution of the gas, in terms of investment and electricity used for that, is contained in the z dis. The z end contains both investments at local purchasers, upgrade facilities and grid connection for the upgraded gas, and operation and maintenance of this. The term z fer covers all the benefits and inconveniences from treating the slurry, in terms of reduced nitrogen leaching and carbon reduction in the soil. The z emi term concerns the change in emissions from the entire chain, meaning both the change in emissions from the heat and power production from biogas, the use of the upgraded gas, but also from the increased transportation of the slurry and the power consumption throughout the chain. The last term, z loss, concerns the losses caused by fees and subsidies affected by the production and use of biogas. MAX z = z sup z tra z bgp z dis z end + z fer + z emi z loss (5.6) 5.3 Supply - Farms & Industry The supply part of the model regards the resource options and choices. The main input is the amount of resources available, see Table 5.1. These amounts are given both in terms of type, but also in terms of zones characterized by different distances to the investigated location for the biogas plant. The amounts are accompanied by an average farm size, related to the specific type. An investment cost for the different types of farms, e.g. mink, is also specified, which is required to deliver to the plant. The resources are further applied with properties in terms of biogas potential, nutrient content and a possible cost. The model does not distinguish and track the individual farms. The number of farms is only used to find an integral number of yearly trips to each farm, and obtain the farm investments. As mentioned in section 3.3.5, it is assumed that the resources are produced evenly throughout the year, and they can therefore also be supplied evenly. z sup = α sup + m sup (5.7) The supply term from the objective function, is rather simple, as displayed in (5.7). The term only contains costs as the environmental benefits from treating the slurry is accounted for in the emission and externalities. The costs are related to the investments at the farms and in the industry. The investments are in general denoted a whereas the corresponding annualised value is denoted α.

48 30 CHAPTER 5. MODEL DESCRIPTION Table 5.1: Supply nomenclature Sets Sr farm Farm size - [tonne] N Nutrient type Xr,z max Resource available - [tonne] R Resource type Z Zone Variables α sup Annualised supply invsetment - [kr] - R Parameters νr,n in Nutrient amounts in resources - [tonne] - R A farm r Investment cost farms - [kr/farm] a farm Total farm investments - [kr] - R A sup r Investment cost supply - [kr] m sup Total O&M costs farms - [kr] - R Ir dem Investment demand - [-] vr in Decision variable {0, 1} Mr farm Maintenance cost farms - [kr/farm] wr,z farm Number of farms used Z N r,n Nutrient content - [kg/tonne] x r,z Resources used - [tonne] - R + Or sup OM costs industry - [kr/tonne] z sup Total supply annuity - [kr] - R w farm r,z = x r,z Sr farm, r R, z Z, (5.8) a sup = r R z Z m sup = (x r,1 Or sup ) + r R r R z Z (w farm r,z A farm r ) + (vr in A sup r ) (5.9) r R (w farm r,z M farm ) (5.10) ν in r,n = z Z x r,z N r,n r R, n N (5.11) The number of farms are found as an integer individually of each type and in each zone, with (5.8). Here, an average farm size of each farm type is assumed, in terms of tonne resources supplied per year. The farm investment primarily concerns the liquid manure farms, as the additional supplying farms are believed to have very few or no investments related to supply biomass to the biogas plant. These investment costs are applied in A farm r, which covers all the resource types, and an investment for e.g. a litter farm, can therefore easily be added. The total contribution to the investment from the resource supply section is shown in (5.9). The industry and other types of biomass suppliers, are also defined with resources available, and with an investment cost according to the investments required to deliver to the plant. The resources available are assigned in z = 1, regardless of the distance between the plant and the supplier. The variable v in r, used in (5.9) and (5.13), is a binary decision variable used to add the investment cost related to the given industrial resource, if the resource is supplied. The corresponding operation and maintenance cost is calculated from (5.10). In the Maabjerg case, the operation and maintenance cost has been set to zero. Further, the nutrient content in the handled resources is calculated from (5.11). x r,z X max r,z, r R, z Z (5.12) x r,1 v in r X max r,1 r R (5.13) The limitations of the resource use, stating that the amount of resources used, x r,z, should be lover than the amount of resources available, Xr,z max, is given in (5.12). A similar constrained is set up for the industry resources, which is shown in (5.13).

49 5.4. TRANSPORT Transport Table 5.2: Transport nomenclature Sets Variables Q Truck technology α re,tra Annualised transport reinvestment cost [kr] - R R ot Other resources(industrial) - R α tra Annualised transport investment cost [kr] - R Z Zone δ q Travel distance - [km] - R φ q Truck time use - [hr] - R Scalars ζ di Diesel use - [l] - R + Φ wd Truck work days - [days] ζ ng Gas use for transport - [m 3 ] - R Φ wh Truck work hours - [hr] a truck Truck investment cost - [kr] - R C di,an Diesel price - [kr/l] o tr O&M cost truck - [kr] - R vr pi Decision variable pipeline {0, 1} Parameters wr,z,q farm,tr Number of farms used, with truck Z z Distance zone-plant - [km] wr,z farm Number of farms used Z ηq di Engine efficiency diesel - [-] wz,q trips,ot Number of trips, industry Z ηq ng Engine efficiency gas - [-] wz,q trips Number of trips, farms Z Γ tr r,q Trips per farm, integer - [-] wq truck,ot Number of trucks for inudstry supply Z Φ tc q Time constant per trip - [hr] wq truck Number of trucks for manure Z Φ tv q Time variable per trip - [hr/km] x pi r Resources in pipeline - [tonne] - R A truck q Truck investment price - [kr/truck] x re r Resources used per type- [tonne] - R + Cq driver Driver salary - [kr/hr] x tr r,q Truck transported resources - [tonne] - R Mq truck Maintenance truck - [kr/year] z tra Total transport annualised cost - [kr] - R Sq tr Xr max Truck size - [tonne] Resource available - [tonne] z tra = α tra + α re,tra + o tr (5.14) The truck term of the objective function consist of an initial investment, a possible re investment and the operation and maintenance cost associated with transporting the resources, as shown in (5.14). The object is to transport the applied resources with the best technology in terms of the lowest cost as possible. Biogas Plant Zone 1 Zone 2 Zone 3 Zone 4 Pig farms Mink farms Cattle farms Figure 5.1: Illustration of the serviced farms

50 32 CHAPTER 5. MODEL DESCRIPTION w farm r,z = q Q w farm,tr r,z,q r R, z Z (5.15) w trips z,q = r R ma Γ tr r,q w farm,tr r,z,q z Z, q Q (5.16) φ q = z Z (wz,q trips Φ tc q ) + Φ tv q δ q q Q (5.17) δ q = (wz,q trips z ) 2 q Q (5.18) z Z ζ di = q Q ζ ng = q Q δ q η di q (5.19) δ q η ng q (5.20) o tr = ζ di C di + (φ q Cq driver + δ q Mq truck ) (5.21) q Q w truck q x r = x pi r φ q Φ wd Φ wh q Q, (5.22) + x tr r,q r R ot, q Q (5.23) q Q w travel,ot r,q = xtr r,q S tr q a truck = (wq truck q Q x pi r v pi r q Q, r R ot (5.24) + wq truck,ot )A truck q (5.25) X max r r R ot (5.26) The number of serviced farms are brought from the supply section and used to determine which type of truck to service the farm. A schematic illustration of a farm distribution is shown in Figure 5.1. An average distance to the biogas plant from the different zones, are assumed for all the farms. The trucks are separated into different technologies both regarding the purpose but also regarding the fuel. The shown equation, (5.15) separates the serviced farms into the different truck technologies. The number of farms is used to determine the number of drives to each zone by (5.16). Here Γ tr r,q is an integer describing the number of trips to each type of farm in a year. The time used to collect the fresh manure and return the treated manure is calculated with a time constant for each drive, representing the time it takes to fill or empty the truck and other small services regarding each stop, plus a variable time depending on the distance representing the average speed of the truck, see (5.17). The fuel use is identified from the distance travelled (5.18), and an efficiency in terms of litre or m 3 needed per kilometre driven, calculated with (5.19) and (5.20). The gas is considered produced at the plant, which is why only the diesel is imposed a price. Together with the salary for the driver, and a yearly maintenance cost, this leads to the total operation and maintenance cost, displayed in (5.21). If the fuel price used is the annualised price and the salary and the maintenance cost of the trucks are expected not to change significantly through the time frame of the evaluation, the operation and maintenance cost obtained with (5.21), is also the annualised cost. The obtained truck time is used to determine the number of required trucks, in order to supply the biomass to the plant, when respecting the time limitation set up for the trucks, see (5.22).

51 5.5. BIOGAS PLANT 33 A biogas plant will probably also receive other type of biomass than manure, for example industrial waste of different types. These can, if possible, either be pumped to the plant or transported by truck. This means that each industrial resource received is divided into a truck transported part and a pipeline transported fraction as displayed in (5.23). The number of truck trips to a possible industry supplier is found a little different from the manure trips, as the truck transported part, x tr r,q divided with the truck size, Sq tr, as displayed in (5.24). The time used is found in exactly the same way as for the manure transportation. It is assumed that the same trucks can supply several of the industry suppliers, similar to the manure trucks, which is why the time used is also found in the same way as for the manure trucks. That means that the number of trucks needed, also can be found in the same way. The investment cost is proportional to the required number of trucks with an input given unit price dependent on the type of truck, calculated as in (5.25). The trucks purchased initially will not last as long as the plant. Therefore a reinvestment of the same size as the initial investment is performed. A limitation is securing the investment for the industrial resource pipeline if used, shown in (5.26). The litter used is imposed with a total price of treatment and delivering to the plant, which is why the transportation is not considered here. A discussion of implementation of litter transportation, can be found in chapter Biogas Plant In the biogas plant part of the model, the optimal size of the plant is found. That means that the amount of resources is connected to a plant investment price in kr per tonne resource supplied per year. The expected biogas production from the supplied resources is also found in this part of the model. In addition to the plant size, a heat supply technology for the heat demand in the process is identified. The biogas plant term from the objective function contains an income term, in terms of avoided other production, compared to the first two parts, see (5.27). This income origins from the produced excess electricity and heat. z bgp = α bgp + α re,bgp + o bgp inc bgp + c re (5.27) The constrained shown in (5.27) does not seem very complex. However the biogas plant modelling consists of many more parts than the first two, with the governing economic part being the investment. The investment consists of a plant investment and an investment for heat supply for the process and possibly district heat and electricity production for the net. A relation exists between the investment price and the process need for heat. With a greater investment cost comes a lower heat use and vice versa, as also explained in subsection This relation is represented by several different independent investment options with a coupled heat use. In Figure 5.2 is a figurative illustration of the correlation, which is modelled using (5.28). A bgp,max β is the maximum total investment cost, if all resources available, is supplied. β represents the different heat level options. For the options not chosen, the term (1 vβ b )Abgp,max β will equal A bgp,max β, making the total negative or zero. In the chosen temperature level, the term (1 v b β )Abgp,max β will be zero, leaving the investment, given by A bgp,max β λ.

52 34 CHAPTER 5. MODEL DESCRIPTION Table 5.3: Biogas plant nomenclature Sets B Reactor temperature level Mt hs Constant OM cost - [kr] H Heat allocation Ot hs Variable OM cost - [kr/mwh] J Heat and power St max Technology limit - [MW] L Biogas line St min Technology limit - [MW] R Resources Xr,z max Resource available - [tonne] R farm Resources from farms - R T hs Biogas use technologies - T Variables U Number of units α bgp Annualised bgp investment - [kr] - R Z Resouce zones α re,bgp Annualised bgp reinvestment - [kr] - R bg bgp,to Biogas use total bgp- [m 3 ] - R Scalars bg bgp t,u Biogas allocation - [m 3 ] - R Ϝ Biogas production change - [-] bg end,to Biogas use total end - [m 3 ] - R LHV Energy content biogas - [MWh/m 3 ] chp bgp CHP produced to sale - [MWh] - R D bgp,dh District heat demand - [MWh] inc bgp Value of produced heat and power - [kr] - R E bgp Electricity use in process - [MWh/tonne] λ Fraction of resources used [-] M adm Administration cost - [kr] π Biogas production - [m 3 ] - R O bgp O&M cost biogas plant - [kr/tonne] ρ Total amount of resources - [tonne] - R + P dh,an District heat price - [kr/mwh] ρ l Amount of resources at line - [tonne] - R + P po,an Annuity power price - [kr/mwh] τ bgp t,h Heat allocation - [MWh] - R + a bgp Biogas plant investment - [kr] - R Parameters a hs Investment heat supply - [kr] - R η j,t Efficiency - [-] c re Total ressource cost - [kr] - R Π r Biogaspotential - [m 3 /tonne] d pr,bgp Heat demand from process - [MWh] - R Υ r Dry matter content - [kg/tonne] e bgp Power used in the process - [MWh] - R RHR t max Max run hours - [hr] h bgp,dh District heat sold - [MWh] - R + A bgp,max β Max investment - [kr] o bgp Total OM cost - [kr] - R A hs t Technology price - [kr/mwh] o hs Total OM cost heat supply - [kr] - R Cr re Ressource costs - [kr/tonne] p bgp h,t,u Heat and Power production - [MWh] - R + D pr,max β Max heat use - [MWh] p excess,bgp Excess power from process heat - [MWh] - R L abs,min r Min. resource use - [tonne] s hs t,u Size - [MW] - R L dm l Max. dry matter content - [-] vβ b Temp. decision variable - [-] - {0, 1} L farm,min Min. Farm fraction - [-] vt,u hs [-] - {0, 1} L rel,max r Max. fraction size - [-] x r,z Resources used - [tonne] - R + L rel,min r Min. fraction size - [-] z bgp Total annualised cost bgp - [kr] - R a bgp A bgp,max β λ (1 vβ) b A bpg,max β β B (5.28) d pr,bgp D pr,max β λ (1 vβ) b D pr,max β β B (5.29) vβ b = 1, β B (5.30) β B ρ = x r,z (5.31) r R z Z ρ = λ Xr,z max (5.32) r R z Z 0 λ 1 (5.33) The heat demand from the process is calculated in the same way in (5.29), shifting the maximum total investment cost, with the maximum total heat use. The lowest investment cost is coupled to the highest heat demand. The equation displayed in (5.30), secures that only one temperature level

53 5.5. BIOGAS PLANT 35 (a) (b) Figure 5.2: Figure a is an illustration of the investment b shows the corresponding heat demand is used. In (5.31) and (5.32) is shown how λ, the fraction of resources available used, is linked to the use of resources. Further are the limits on λ shown in (5.33). A reinvestment in the plant is also considered. This is defined by a fraction of the obtained investment, a bgp, in a specific year. L rel,min r ρ x r,z L rel,max r ρ r R (5.34) z Z L abs,min r z Z x r,z r R (5.35) L farm,min ρ ρ l L dm l r R farm z Z r R l z Z x r,z (5.36) (x r,z Υ r ) l L, (5.37) The delivery of resources from single farms and industry are not modelled with a time variation, but assumed equal throughout the year. This might be a rough assumption. In order to account for a possible seasonal variation in resource availability, restrictions can be put on specific resources, limiting the use to maximum given fraction of the total amount of used resources. This limit is given as a parameter containing the maximum fraction of the specific resource, called the relative limit, shown in (5.34). A minimum use limit for a specific resource can also be implied, an absolute limit, displayed in (5.35). This limit could illustrate a must use resource or help modelling a specific scenario. The subsidy rules can also imply a limit of a given fraction of resources from e.g. farms to be used, displayed in (5.36). A limit on the dry matter content is further applied, in terms of a limit on the total dry matter fraction of the resource mix, see (5.37). In that equation ρ l represents the total amount of resources used in the specific line and R l represents the resources applied to the specific lines. Also in this equation, the assumption that the resource supply is evenly distributed throughout the year, is important.

54 36 CHAPTER 5. MODEL DESCRIPTION t T hs u U vt,u hs St min s hs t,u vt,u hs St max t T hs, u U (5.38) bg bgp t,u = s hs t,u LHV η heat,t RHR max t p bgp j,t,u = bgbgp t,u LHV η j,t t T hs, u U heat J, j J, t T hs, u U (5.39) (5.40) p bgp heat,t,u = hbpg,dh + d pr,bgp heat J (5.41) = u U d pr,bgp = h H τ bgp t,h h bgp,dh = p bgp heat,t,u t T hs (5.42) t T hs τ bgp t T hs τ bgp t,process process H (5.43) t,dh dh H (5.44) h bgp,dh D bgp,dh (5.45) p excess,bgp = chp bgp = τ bgp η power,t t,process η t T hs heat,t a hs = o hs = τ bgp t,dh η t T chp heat,t t T hs u t T hs u U {heat, power} J process H {heat} J {dh} H (5.46) (5.47) s hs t,u A hs t (5.48) (vt,u hs Mt hs + p bgp heat,t,u Ohs t ) {heat} J (5.49) The size of the heat supply technology at the plant is determined with the simple and general relation displayed in (5.38), from [14] and other general linear programming literature. The set U indicates that several units of the same technology can be implemented. The unit size is used to find the biogas used in the specific units in (5.39). RHRt max represents the maximum runtime in hours in a year, defined with the technology. The equation could be put up as an inequality as bg bgp t,u, but as the model seeks to minimise the investment cost, the result will be equal to, as the run time will be used fully. The total amount of heat and power is found from (5.40). Here p bgp j,t,u contain the production with j representing heat and power, t the technology use and u the unit producing it. A heat balance of produced heat and heat for district heat and process heat is set up in (5.41). The allocation of heat from specific technologies are handled in (5.42), where h contains the heat for district heat and process heat respectively, and in (5.43), where the restriction securing that sufficient heat is produced to at least cover the demand from the production. Finally the technology used for district heat production is found, along with the restriction securing that there is not produced more district heat, than demanded, in (5.44) and (5.45). In order to allocate the emissions, the excess power from process heat production is found in (5.46), and the energy used for combined heat and power production for the net, is found fro (5.47).

55 5.5. BIOGAS PLANT 37 The corresponding heat supply investment and operation and maintenance cost of the heat supply is modelled from (5.48) and (5.49). c re = r R z Z x r,z C re r (5.50) o bgp = O bgp ρ + v bgp M adm (5.51) e bgp = E bgp ρ (5.52) inc bgp = h bgp,dh P dh,an + p bgp power,t,u P po,an (5.53) t T hs u U π = (x r,z Π r )Ϝ (5.54) r R z Z bg bgp,to = bg bgp t,u (5.55) t T hs u U π = bg bgp,to + bg end,to (5.56) Some of the resources are applied with a cost. These are accounted for in (5.50). The operation and maintenance of the biogas plant, and also the administration expenses are applied in different ways. The power demand for the process is given as a power use per tonne resource treated. In the same way are both the maintenance cost, the staff salary and the insurance cost linearly dependent on the amount of resource treated, which is why they have all been merged to a single cost, O bgp. On the other hand the board, the manure administration, the offices and the accountancy are given as a constant cost if a plant is considered, M adm, see (5.51). Besides the cost of the power,the power use is calculated separately, in order to find emissions entailed by the increased use, in (5.52). The income from selling excess heat and power to the grid is calculated simply as displayed in (5.53), with the prices being the annualised prices, to make them representative. The biogas production from the resource mix used, is found from the expression displayed in (5.54). Here Π represents the potential production, and Ϝ represents losses as described in subsection This production is coupled to the total use by (5.56), where the use in the biogas plant heat supply is found as in (5.55). The connection between the total production and use of biogas is secured in (5.55) and (5.56).

56 38 CHAPTER 5. MODEL DESCRIPTION 5.6 Distribution Table 5.4: Distribution nomenclature Sets Parameters I Purchasers pur i Distance to purchasers -[km] T Technologies T hd Biogas technologies - subseteqqt Variables U Units α dis Annualised investment cost - [kr] - R bg i,u,t end Biogas use purchasers - [m 3 ] - R + Scalars a dis Investment storage and dist. - [kr] - R A dis Distribution pipe price - [kr/km] e dis Electricity used for distribution - [MWh] - R C po Annuity power price - [kr/mwh] o dis Cost of electricity to dist. - [kr] - R E dis Power use distribution - [MWh/m 3 /km] v pur i Decision variable purchasers - {0, 1} z dis Total annualised distribution cost - R z dis = α dis + o dis (5.57) a dis = (A dis v pur i pur i ) (5.58) i I e dis = i,u,t) E dis (5.59) i I u U t T hd (bg end o dis = e dis C po (5.60) The storage and distribution is initially modelled in a rather simple way. The investment in the storage is included in the plant investment, whereas the distribution investment cost is treated separately here. The investment cost is proportional to the distance to the purchaser, with a given kilometre pipeline price, see (5.58). The distribution of gas require a certain amount of electricity found in (5.59), which is proportional to the operation and maintenance costs of the distribution, see (5.60). The total cost of distributing the produced biogas is therefore simply calculated as displayed in (5.57).

57 5.7. END USE End use z end = α end + o end + α upg + o upg inc end inc upg Table 5.5: End use and upgrade nomenclature Sets S max,upg t Max. size upg. - [m 3 bg/hr] G Local and national S min,upg t Min. size upg. - [m 3 bg/hr] H Heat allocation I Purchasers Variables T bng Bio ng technology - T α end Annualised end investment cost - [kr] - R T hd Technology heat demand - T α upg Annualised upgrade invest - [kr] - R T su Substituted technology - T bg end,to Biogas for upgrade. - [m 3 ] - R + T upg Upgrade technology - T bg u,t end Biogas allocation - [m 3 ] - R + U Unit number bng g,t Bio ng allocation - [m 3 ] - R + inc end End use annualised sale - [kr] - R + Scalars inc upg Upgrade annualised sale - [kr] - R + Σ bg Energy content bg - [GJ/m 3 ] σ bng u,t Bio ng energy content - [GJ] - R Σ bng Energy content bng - [GJ/m 3 ] τi,u,t,h end Heat allocation - [MWh] - R + a end Investment end use - [kr] - R Parameters a upg Investment upgrade - [kr] - R η upg t Upg. Efficiency - [-] o end OM costs end - [kr] - R Ψ max t Max runtime - [hr] o upg OM costs upgrade - [kr] - R A end i Purchaser investments - [kr] p end h,i,t,u Heat and Power prod. - [MWh] - R + A grid g Grid connection price - [kr] s end i,u,t Size end use units - [MW] - R + A tech t End use technology prices - [kr/mw] s upg i,t Upg. facility size - [m 3 bg/hr] - R A upg t Upgrade techn. price - [kr] vg gr Grid connection decision var. - {0, 1} D end,he i,h Heat demand - [MWh] v pur i Decision variable purchasers - {0, 1} L bng g,t Bio ng use limit v upg i,t Upgrade tech. decision var. - {0, 1} z end Total end use cost annualised - [kr] - R t T hd u U τh,i,t,u end = v pur i D end,he i,h i I, h H (5.61) p end heat,i,t,u = τh,i,t,u end h H a end = (s end i,u,ta tech t ) + i I u U t T hd i I i I, t T hd, u U (5.62) heat J v pur i A end i (5.63) v upg u,t Smin,upg s upg u,t v upg u,t Smax,upg u U, t T hd (5.64) g G bg upg u,t t T bng bng g,t = = s upg u,t Ψmax t u U, t T upg (5.65) u,t ηupg t ) Σbg Σ bng (5.66) bng g,t v gr g σ bng (bg upg u U t T upg L bng g,t g G, t T upg (5.67) g,t = bng g,t Σ bng g G, t T upg (5.68) a upg = (s upg u,t Aupg t ) + (vg gr A grid g ) (5.69) u U t T up g G o upg = (bg upg u,t Oupg t ) (5.70) u U t T upg The end use of the biogas is given as different options. Local purchasers in terms of decentralised heat plants, combined heat and power plants and industry have rather well defined demands in

58 40 CHAPTER 5. MODEL DESCRIPTION terms of heat demand, which in some cases can be covered by biogas. In this model they are defined by a heat demand for either district or process heat, but it could as well be for power. The optimisation regards which purchasers to supply, and which technology to use the biogas in, in order to fulfil the demand, shown in Figure 5.3. The purchasers are besides the demand assigned a given distance and possible coupled investments. The technology use and size is determined as in section 5.5, with the heat supply for the process. That also means that the total demand of a supplied purchaser has to be fulfilled, see (5.61). The production is determined from (5.62). As with the heat produced at the biogas plant, it is also important here what the heat is used for(either process or district heat) and how it is produced, in order to find the subsidies to the production. The investments at a given purchaser can consist of other parts than the biogas unit, e.g. a demand for other buildings. The A end i term in (5.63) accounts for that. Unit 1 Heat demand vi pur Unit 2 Purchaser 1 Power demand Biogas Unit 1 Heat demand vi pur Unit 2 Purchaser 2 Power demand Figure 5.3: Illustration of different purchasers The gas can also be upgraded, which is implemented as different types of scalable plants, see (5.64), and as described in section 4.5. The corresponding use of biogas is calculated from (5.65). The implied production of bio natural gas is calculated from (5.66). The size of the upgrading plants implies different costs in terms of gas distribution. A small upgrade plant can probably supply gas to the local natural gas network, whereas a larger plant may have to supply to the national natural gas grid, in order to reach the storage facilities in the network. The limits in the two networks are used in (5.67), and the energy content of the supplied gas is calculated from (5.68). The upgrade investment consist both of an upgrade facility investment, but also a grid connection investment cost, dependent on the grid connected to, see (5.69). The operation and maintenance costs are directly proportional to the amount of gas treated with a given technology, as given in (5.70). Also the electricity use in the upgrade facilities is found from the amount of gas upgraded. 5.8 Emissions and Fertilizer reduction This section contains a description of how the emissions from the separate parts are calculated. The general idea is to calculate the emissions from the used technology and the corresponding emissions from the possible substituted technology with the same heat or power production. The emissions not only apply to the energy production but also to the fertilizing effects, as described in subsection These calculations will also be explained in the following.

59 5.8. EMISSIONS AND FERTILIZER REDUCTION 41 Table 5.6: Emission nomenclature Sets E Emissions Yɛ di Emissions diesel use - [kg/l] N Nutrients Yɛ NG Emissions gas transportation use - [kg/m 3 ] R Res. type Y po,sub Emissions substituted power production - [kg/mwh] R l Green line resources Y t,ɛ Emissions from techn. - [kg/gj] T Technology T chp CHP technologies - T Variable T sub Tech. Substituted - T am red Amonium reduction - [tonne] - R T use Techn. used - T ca red,val Reduction cost - [kr] - R Z Zone ca red Carbon reduction - [tonne] - R ch emi Methane emissions - [tonne] - R Scalars em bng t,ɛ Upgrade use emissions - [tonne] - R AM nn Amonium in Nitrogen - [tonne/tonne] em chp,to ɛ CHP emissions - [tonne] - R Θ le,sca Nitrogen leach scale - [-] em chp ɛ CHP emissions - [tonne] - R C ca,val Carbon in soil value - [kr/tonne] em red ɛ Farm reduced emissions - [tonne] - R C le,val Nirogen leach value - [kr/tonne] em sub ɛ Substituted chp emissions - [tonne] - R C red,val Nitrogen fertilizer reduction value - [kr/tonne] em trans ɛ Transport emissions - [tonne] - R E N Power for N. production - [kr/tonne] em upg,chp ɛ Upgrade use emissions - [tonne] - R Θ am,sca Scaling factor em upg,tr ɛ Upgrade use emissions - [tonne] - R em val,net CHP emission value - [kr] - R Parameters em val,tr Transport emission cost - [kr] - R AM r org Amonium original - [kg/tonne] inc red,le Leach reduction cost - [kr] - R AM r red Amonium treated - [kg/tonne] ν r,n Nutrients recieved from resources - [tonne] - R CA re,soil r Carbon red. soil - [tonne/tonne] νr,n w Nutrients recieved from plant - [tonne] - R CH r emi Methane emissions - [kg/tonne] θ fer,red Nitrogen fertiliser reduction - [tonne] - R η j,t Heat and power efficiency - [-] θ re,leach Nitrogen leach - [tonne] - R Θ fer,red ty Nitrogen amonium factor - [kg/tonne] ζ di Diesel use - [l] - R Θ le r Nitrogen leaching - [tonne/kg] ζ ng Gas used - [m 3 ] - R Υ r Dry matter - [kg/tonne] c red,fer Fertilizer reduction cost - [kr] - R Cɛ em,chp Emissions chp val - [kr/tonne] p excess,bgp Power from process heat - [MWh] - R Cɛ em,tr Emissions transport val - [kr/tonne] τ bgp t,h Heat allocation - [MWh] - R + Y chp,sub Emissions substituted CHP - [kg/mwh fuel] x r,z Resources used - [tonne] - R Y dh,sub Emissions substituted DH - [kg/mwh] z emi Total emission cost - R Y bng t Emissions substituted bng - [kg/mwh] z fer Total fertilizer cost - R Y ng t Emissions substituted ng - [kg/mwh] σ bng g,t Ugraded gas allocation - [MWh]- R

60 42 CHAPTER 5. MODEL DESCRIPTION Supply θ fer,red = ((νr,nit w Θ fer,red r ))Θ am,sca Nit N (5.71) r R l em red n2o = (θ fer,red AM nn + ((AM r org ν r,nit ) (AM r red νr,nit))) w 44 Nit N, n2o E 28 r R (5.72) em red ch4 = (x r,z CH r emi ) ch4 E (5.73) r R z Z θ re,leach = ( x r,z Θ le ) Θ le,sca (5.74) r R z Z r ca red = (x r,z Υ r CA re,soil r ) (5.75) r R z Z c red,fer = θ fer,red C red,val (5.76) ca red,val = ca red C ca,val (5.77) inc red,le = θ re,leach C le,val (5.78) The environmental effects from treating the slurry, described in subsection is modelled as described in this section. The primary effect of the treatment is the effect on the fertilizing value from nitrogen. The reduced need of synthetic fertiliser, θ fer,red, of nitrogen is calculated from (5.71). The nutrient content separated according to the line separation. It is the nutrients from the green line, described in chapter 2, that is important with respect to this. The total reduction of ammonium emission is calculated from the equation displayed in (5.72). The reduction of methane emission from the storage and use of the treated slurry, is calculated as expressed in (5.73). The reduced leaching of nitrogen is calculated from (5.74). The reduction of carbon in the soil is calculated from the resources used, the dry matter content and a reduction factor, as displayed in (5.75). The value of the reductions and changes is found from (5.76), (5.77) and (5.78), leading to the total fertiliser objective term, shown in (5.79). z fer = c red,fer + inc red,le ca red,val (5.79) Transport The transportation of the resources and the treated products is only a source to increased emissions, as it does not substitute anything. The technology used for transportation though, is of cause important in relation to the emissions, and the emissions are directly proportional to the type and amounts of fuel used, as displayed in (5.80). Besides the resource transportation, the upgraded gas can also substitute fuel in the transport sector, within the applied limits in (5.67). The net emissions in that case is the difference between the emissions from the used gas and the emissions from the amount of diesel or other fuels substituted. em trans ɛ = ζ di Y di ɛ + ζ ng Y ng ɛ ɛ E (5.80)

61 5.8. EMISSIONS AND FERTILIZER REDUCTION Heat and power production The emissions from the heat and power produced at the biogas plant has to be accounted for separately. Heat used for the process does only add to the emissions, whereas any co-generated power can substitute power produced elsewhere. Any excess produced heat and power will also substitute heat and power produced elsewhere. The heat and power produced at decentralised heat and combined heat and power plants with biogas, substitute a chosen technology instead. This means that the net emission is equal to the emission caused by the biogas use, subtracted the emission from the substituted technology. The calculation implemented for the production at the biogas plant is given in (5.81). A similar expression could be put up, describing the emissions from the end use of the biogas. em chp,to ɛ =em chp ɛ (p excess,bgp Y po,sub ɛ + ɛ E (5.81) τ bgp t,dh ( Y chp,sub ) + τ bgp bgboi,dh η Y ɛ dh,sub ) dh H, heat J t T chp heat,t bgboi T Upgraded gas use & Total value Similar to the decentralised plants, the net emissions are equal to the difference between the emission caused by the use of bio natural gas and the similar use of natural gas technologies, as given in (5.82) and (5.83). The upgraded gas can also substitute fuel in the transportation sector. As already mentioned, these emissions are valued a bit different from the energy sector, which is why they are treated separately. Finally the total net emissions from the changed fertilizing, the biogas plant and the end use of the gas is found, along with the total transportation emissions. The value of both set of emissions can be found using (5.84) and (5.85). In order to neglect the economic value of the reduced CO 2 emissions from the energy production, the value can be set to zero. Hereby is the reduction found, but not given an economic value. The total economic value of the changed emissions is found using (5.86). em bng t,ɛ = (σ bng g,t Y bng t σ bng g,t Y ng t ) t T upg, ɛ E (5.82) g G em upg,chp ɛ = em bng t,ɛ ɛ E (5.83) t T upg,chp em upg,tr ɛ = em bng trans,ɛ em val,net = ((em chp,to ɛ em red ɛ ɛ E em val,tr = ((em trans ɛ ɛ E ɛ E, trans T + em upg,chp ɛ )Cɛ em,chp ) (5.84) + em upg,tr ɛ )Cɛ val,tr ) (5.85) z emi = em val,net + em val,tr (5.86)

62 44 CHAPTER 5. MODEL DESCRIPTION 5.9 Distortion Losses Implementation f bgp = f end = su bgp = su end = g G t T hs u U t T hd u U t T chp u U bg bgp u,t Σbg LO fee t (5.87) bg end u,t Σ bg LO fee t (5.88) bg bgp u,t Σbg LO t sub + τ bgp bg boi,h LOsub h bg boi T (5.89) h H bg u,t end Σ bg LO t sub + τi,u,bg end boi,hlo h sub + bg boi T (5.90) i I u U h H t T chp u U σ bng g,t LOsub,bng t T bng z los = f bgp + f end + su bgp + su end (5.91) The main part of the calculations related to the dead weight and distortions losses are conducted in the spreadsheet part of the model, leaving only some coefficients to be applied in the optimization model. The losses from the fee difference between natural gas and biogas, as explained in subsection 3.3.4, is calculated in (5.87) and (5.88). The corresponding losses from increased subsidy payment is found in (5.89) and (5.90), which also contains the upgrading of gas. Finally is found the total term applied in the object function, from (5.91). Table 5.7: Distortion and dead weight losses nomenclature Sets T Technology Variables T chp CHP technologies bg bgp t,u Biogas allocation - [m 3 ] - R U Unit number bg u,t end Biogas allocation - [m 3 ] - R + T bng Bio ng technology f bgp Fee loss bgp - [kr] - R f end Fee loss end - [kr] - R Scalars σ bng g,t Bio ng energy content - [GJ] - R Σ bg Energy content bg - [GJ/m 3 ] τ bgp t,h Heat allocation - [MWh] - R + τi,u,t,h end Heat allocation - [MWh] - R + Parameters su bgp Subsidy loss bgp - [kr] - R LO fee t Fee loss - [kr/m 3 ] su end Subsidy loss end - [kr] - R LO t sub Subsidy loss - [kr/m 3 ] z los Total loss term - [kr] - R LO h sub Subsidy loss - [kr/gj] LO sub,bng Subsidy loss - [kr/gj]

63 CHAPTER 6 Results This chapter contains the results obtained with the constructed model. The results are divided into three groups; the first one is an attempt to construct the scenario of Maabjerg as it has been build, with the model, whereas the second is different optimisations of the Maabjerg case. Finally a sensitivity analysis of the main economic parameters is performed, in order to check the model. In the tables illustrating the results from the model, the italic type represents the used parameters in the model, whereas the upright type indicates a result. 6.1 The Maabjerg Case - As it is The Maabjerg case is intended constructed as described in chapter 2, in order to validate the results obtained with the model. This means that almost no optimisation has been performed here. The following constraints has been imposed to the model, in order to create the scenario Resource amounts has been limited to those actually used Biogas purchasers and technology use is constrained to fit the scenario Plant investment cost per tonne is constrained(no temperature level optimisation) No upgrade possibilities Supply The average farm size in terms of yearly manure delivery, described in chapter 3, is in the model used to find the number of serviced farms. The size differs between the manure types, whereas the farm investment is assumed the same, independently of the type of farm. The result, in terms of number of farms and related investment costs, are displayed in Table 6.1. The total investment of approximately 20 millions kr, 26 million kr with NTF, from about 130 farmers fits rather well both in numbers of farmers and in total investment, with the numbers learned from the actual case of 140 farms and a total investment from the farmers of about 20 to 25 million kr, see appendix section A.2 and section A.3. As no information about extra investments at the industry and slurry suppliers regarding storage tanks and so, has been obtained, this is assumed to have little impact on the result as a total investment of 2 millions from the industry supplier and the waste water treatment plant, would only 45

64 46 CHAPTER 6. RESULTS Table 6.1: Suppliers & related investments Number Price per unit w. NTF Total cost w. NTF Annualised cost [-] [kr] [kr] [kr] Number of farms a Industry & Slurry suppliers Total a [30] entail an annualised cost of approximately kr, it would not be very significant. The resources supplied are taken roughly from the information given at the visit at Maabjerg, and also presented in chapter 2. These numbers are applied in the model as a boundary of minimum and maximum use, in order to fit the scenario. The numbers in Table 6.2 therefore represents results obtained in the model, but forced by limitations to fit the scenario. As inflation is not considered in socio economic evaluations, the total resource cost of 3,375 million kr also represents the annualised value. Table 6.2: Biomass resources, prices and expected biogas production Amount Price w. NTF Total cost w. NTF Biogas [tonne/year] [kr/tonne] [kr] [1000 m 3 ] manure Litter a Industry Slurry Total a [6] Transport Table 6.3: Supplied farm distribution Farm type zo 1 zo 2 zo 3 total Cow manure pig manure Mink manure Total 129 The transportation of the supplied resources is an important part, in order to obtain a profitable project. The distance driven is found from the assumed distances to the collecting zones, and the number of farms serviced in the different zones, displayed in Table 6.3. Combined with the number of trips, the distance travelled, is used to find the time used to collect the resources. The resulting number of trips is displayed in Table 6.4, along with the time used, and the distance driven. A constant time use of 20 minutes per trip for offloading and filling of the trucks accounts for almost half of the time used for supplying the manure. When driving, it has been assumed that the average speed of the truck is 50 km per hour, a number taken from [19]. The calculated time use is important in order to estimate the salary cost for the drivers. From Table 6.4 it is clear that the driver salary is the largest part of the annualised transport costs.

65 6.1. THE MAABJERG CASE - AS IT IS 47 The truck investment and the size of this, is of course important as it appears twice, also in form of a reinvestment, see Table 6.4. The number of trucks is found from the amount of time used to collect the resources, using a time limitation on the separate trucks of 8 hours a day, 300 days a year. The result of five trucks corresponds to the actual number of trucks at Maabjerg Bioenergy [22]. An interesting result for the transportation, is the cost per tonne, which is also displayed in the Table 6.4, organised with the cost for manure and industry transport separately. The relative cost for transportation is almost equal for the truck transported manure, and the pipeline transported industry products, whit a cost of 10,96 and 11,63 kr per tonne respectively. Also the average transport distance of the manure is shown, with a number of approximately 20 km per round trip, which indicates that the distance between the plant and the average farm is a bit above 10 km. Summing up, the total annualised transport cost is approximately 7,0 million kr. Table 6.4: Transport economy Amount Price w. NTF Total cost w. NTF Annualised cost [kr/unit] [kr] [kr] Truck 5 trucks a Reinvest 5 trucks Distance driven km Number of trips trips Diesel use, w. annuity price l 6,77 b Time use hr 243 c Time use offloading & filling hr Time use driving hr Truck maintenance km 1,82 d Pipeline investment 20,00 km c Electricity use - pumpwork 850 MWh 753 c Total Transport cost manure tonne 10,96 kr/tonne Transport cost Industry and slurry tonne 11,63 kr/tonne Transportcost average tonne 11,14 kr/tonne Transport cost manure gas m 3 0,50 kr/m 3 Transport cost industry, slurry gas m 3 0,28 kr/m 3 Total cost average m 3 0,41 kr/m 3 Average transport distance 19,03 km a An assumed value based on [19] and information from Grontmij b Annualised value from[4] c [19] d [35] Biogas plant & Distribution The biogas plant is of course the main part of the investment associated with the biogas production. As shown in Table 6.5 the plant is assumed to have an investment cost of 500 kr per tonne, without NTF, and with a yearly treatment of tonnes of resources, that implies an investment plant cost of 430 million kr. This cost does not include the heat supply, in terms of the CHP biogas engine. The model results in an engine size of 3,34 MW capacity heat production, that implies a cost of 8,5 million kr. As described in chapter 2, the heat use is rather low, 10 kwh per tonne,

66 48 CHAPTER 6. RESULTS compared to other studies, but the plant is intended as a mesophile plant, and a high degree of heat reuse is a part of the explanation for the high investment cost, see section 3.2. The reinvestment percentage is an assumed value, based on the values used in the IFRO report [19]. It is assumed to be done in year eleven of the investigated operation time, similar to the trucks. Table 6.5: Biogas plant economy Amount Price w. NTF Total cost w. NTF Annualised cost [kr/unit] [kr] [kr] Investment - biogas plant tonne/year 675 a Reinvestment - plant 3% Electricity use MWh/year 753 b Maintenance Facility Salaries tonne/year 8,44 c Insurance tonne/year 2,03 d Administration e Ressource buy kr/year Investment CHP 3,34 MW Heat f CHP O&M 22840,54 MWh/year 67,5 g Total Heat sale MWh/year 331 h Electricity sale MWh/year 479 h Total Biogas plant total Treatment costs - tonne tonne 54,3 kr/tonne Treatment costs - gas m 3 1,85 kr/m 3 a An assumed value based on the [31] b Annualised value from[4], with an electricity consumption of 5,5 kwh per tonne c Assumed value, based on the known working stab at Maabjerg Bioenergy d Scaled from [19] e [19] f Based on information from visit, [34] and [24] g [18] h [4] Maabjerg has eight employees, according to [22], which according to there annual report entails a total cost of approximately 4 million kr, [7]. That number is rather close to the number obtained with the model, which without NTF gives 4.1 million kr. The administration cost is a combination of the costs associated with the board, audit, office and further administration, assumed to a total of kr. The only resource applied with a price is the litter. That price account for the pre processing and the transportation, as already mentioned in chapter 4. The total biogas production of 19,4 million m 3, corresponds to 106,3 GWh or 382,6 TJ, with an expected methane content of 55%, as displayed in Table 6.7. The biogas used at the biogas plant entails a power production of MWh per year and an extra heat production, besides the process heat, of MWh per year. Using the heat and power prices from [4], a total treatment cost of more than 50 kr per tonne resource is obtained, including

67 6.1. THE MAABJERG CASE - AS IT IS 49 the NTF, corresponding to a cost of 1,85 kr per m 3 biogas produced, see Table 6.5. Despite the constrained heat demand from the process, the biogas use at the biogas plant has not been limited in the same way, as the biogas use at Vinderup and Maabjerg værket. This means that the excess biogas from the production, after delivering to Vinderup and Maabjerg, is used at the plant. The investment and operation costs of the biogas distribution network, displayed in Table 6.6, are rather small compared to the cost of the biogas plant, though still significant. The 22 km applied pipeline is determined from the pipeline demand of 20 km to Vinderup and 2 km to Maabjerg. The distribution cost per m 3 biogas is found only from the fraction of biogas, that is actually distributed through the net, equal to the amount used at Vinderup and Maabjerg værket, described in End use - Investment & Production. The total original investment from transport, the biogas plant and the distribution of the biogas is around 500 million kr with the NTF, and about 365 million kr without. That is roughly the same as presented in [31], of 375 million kr, when taking into account the Concerto programme subsidy and the omitted investment for the manure pipeline transportation of 25 million kr, leading to a total of 350 million kr. Table 6.6: Distribution economy Price w. NTF Total cost w. NTF Annualised cost [kr/unit] [kr] [kr] Investment pipeline 22 km a Operation pipeline (electricity) 331 MWh 753 b Total Total costs - biogas m 3 0,15 kr/m 3 a [19] b [4] The calculated expected biogas production is a a bit larger than the expected production of approximately 18 million m 3 per year. Looking at the net energy content, the 106,3 GWh corresponds pretty well with the expected energy content in the biogas of GWh, according to [31]. The distribution of gas to Vinderup and Maabjerg værket respectively are defined by a district heat demand. This means that the use of biogas and and the correlated production of power is fixed by the given district heat demand, assumed to be and MWh respectively. These demands are applied in the model, based on the known actual gas allocation of 7,5 million m 3 biogas for Vinderup and 3,5 million m 3 for Maabjerg værket, which they are contractually bound to purchase for 20 years. Table 6.7: Biogas and energy distribution Biogas Biogas energy District heat Process heat Power production [mio. m 3 ] [MWh] [MWh] [MWh] [MWh] Biogas plant 7, Vinderup 7, Maabjergværket 3, Total 19,

68 50 CHAPTER 6. RESULTS End use - Investment & Production As mentioned about the distribution, the biogas purchasers are applied with a pipeline distance to the plant. In the same way the purchasers are applied with an extra investment, to represent extra requirement, beside the biogas technology, for e.g. new buildings at the plant, in order to purchase the biogas, as described in chapter 5. Table 6.8: Biogas end use Price w. NTF Total cost w. NTF Annualised cost [kr/unit] [kr] [kr] Investment Vinderup O&M Vinderup MWh heat 59,4 a Investment Maabjerg værket O&M Maabjerg værket MWh heat 67,5 a Total Electricity sale MWh 479 b Heat sale MWh 331 b Total End use total Total cost - gas m 3-1,73 kr/m 3 a [18] b [4] The investment at the Vinderup combined heat and power plant therefore consist of two parts; an investment in the combined heat and power technology and a pre determined investment at the plant for new buildings and heat exchangers. The engine size obtained with the model here, is approximately 4,5 MW heat in combination with a heat pump. That corresponds rather well with the information given at the visit, see appendix section A.1. However, the technology and the heat demand has been constrained, and in combination with the technology data applied for the technology, there was no space for optimisation. The technology is limited by a max runtime per year, and with a constrained heat demand it leaves almost no space for optimisation in the model. The total investment of approximately 17,3 million kr without NTF and 23 million kr with, is in the same order as the actual investment at the plant of 21 million kr, see also appendix section A.1. The biogas engine and heat pump technology combination has been modelled as one technology. The heat pump is also the reason for the total heat and power production, being slightly larger than the energy content of the biogas supplied to Vinderup, in Table 6.7. The electricity use is subtracted from the electricity produced, but as the Coefficient Of Performance of the heat pump is set to five, according to [34], the sum of the heat and power produced, is larger than the written energy content of the biogas. The modelling of Maabjerg værket, and their use of biogas has been a bit different. The biogas is used to boost the power production, by super heating the steam. The actual investments and the efficiency of the use is not known. From [22] it is known that they are expected to purchase about 3,5 million m 3 of biogas, corresponding to approximately 4 million m 3 here, which, as mentioned earlier, has been translated into a heat demand. It has been assumed that the biogas is used in a biogas engine, though this is not actually the case. Hence both a power and a district heat production from the biogas is obtained, as shown in Table 6.7. As for the distribution, the total average cost per m 3 biogas is found in terms of the amount of

69 6.1. THE MAABJERG CASE - AS IT IS 51 Millions kr Supply Transport Biogas plant Distribution costs End use costs H&P sale bg plant H&P sale end use 30 Annualised cost Heat sale Power sale Figure 6.1: Annualised values of the simple socio economic analysis biogas used in the two plants. The operation and maintenance cost of the two plants are 1,6 and 0,8 million kr separately. Combined with the investment costs, a total annualised cost for the two plants of 4,7 millions is obtained. Taking into account the effects from the heat and power production, the total end use annualised cost is approximately -25 million kr, which corresponds to a negative price of -1,76 kr per m 3 biogas used in the two plants Evaluation without externalities The results from the supply, the transportation, the biogas production and the end use, only considering the direct effects, is summed up and displayed in Table 6.9 and further in Figure 6.1. From the table, it is obvious that the biogas plant is not a good investment in the simple socio economic energy perspective. From the figures, it is also clear that the three main costs are the biogas plant investment cost, and the avoided electricity and heat production costs, of course in combination with the biogas potential of the used resources. As the plant at Maabjerg was intended as a manure treatment plant, the fertilizing effects and the emission externalities has to be accounted for, in order to make a qualified evaluation of the case. Table 6.9: Simple economic evaluation NPV Annualised cost Supply 25,12 million kr kr Transport 94,34 million kr kr Biogas plant costs 664,65 million kr kr Distribution 23,38 million kr kr End use costs million kr kr Total 871,39 million kr kr H&P sale Biogas plant -186,81 million kr kr H&P sale End use -346,34 million kr kr Total 338,23 million kr kr Total costs - tonne tonne 38,94 kr/tonne Total costs - gas m 3 1,29 kr/m 3

70 52 CHAPTER 6. RESULTS Fertiliser effects - Absolute and Economic The main purpose of the biogas plant in Maabjerg was to treat and separate the manure, in order to maintain the local livestock production. Therefore evaluating the environmental effects of the project, is important in order to make a fair economic judge of the project. The modelled economic value of the treatment can be seen in Table From the table it appears that the need for synthetic fertiliser is decreased with 270,86 tonne per year, due to the increased efficiency of the treated manure. Assuming a Nitrogen fertiliser price of kr per tonne without NTF [25] and kr per tonne including the NTF, an annual saving of more than 2,5 million kr can be achieved. The gain demands that the farmers actually reduce the use of synthetic fertilizer, according to the increased effect of the use of slurry. This is supported by the interview wit ha farmer supplying to Maabjerg Bioenergy, see appendix section A.3. A reduction of the Nitrogen leaching is also obtained due to the changed viscosity of the treated manure. Assuming that the use of synthetic fertiliser is reduced, and that the separated fibre parts are not spread to the same field areas, the total reduction obtained is about 115 tonnes, which corresponds very well with the number of 109 tonnes given from Maabjerg Bioenergy in [22]. The valuation of the Nitrogen leaching is found from [19] and [25]. The value of 40 kr per tonne though, does not seem very well based, but appear more as a rule of thumb, [19]. The effect of the value is examined later in a sensitivity analysis. Also the reduced carbon content of the soil is evaluated. The treatment of the slurry reduces the carbon content of the soil in the total field area fertilised with the treated manure with approximately tonne per year. This value equals a CO 2 emission of tonne per year. The economic value of the total fertilising effects reaches almost 8 million kr. Converting the value gives a result of 15 kr per tonne treated manure and resources from the green line. The fertilising value in terms of biogas produced is also estimated, equalling almost 0,50 kr per m 3. Table 6.10: Fertilizer effects Amount Price w. NTF Total cost w. NTF Annualised cost [kr/unit] [kr] [kr] Reduction Nitrogen fertilizer 274,85 tonne a Reduced Nitrogen leaching 115,71 tonne b Loss of C in soil 1048,70 tonne 3,67 283,5 c Total Total costs resources tonne -15,08 kr/tonne Total costs biogas m 3-0,46 kr/m 3 a [25] b [25] & [19] c [4] Emissions The emission effects from the entire chain appears from Table As the biogas is used in biogas engines at all three plants, it is evident that emissions from the Maabjerg Bioenergy, Vinderup and Maabjerg værket are directly scalable to the biogas use. The combined heat and power production is assumed to substitute heat and power from natural gas engines, whereas the excess power from the process heat production at Maabjerg Bioenergy, is set to substitute the average emissions from

71 6.1. THE MAABJERG CASE - AS IT IS 53 the Danish electricity production from 2012 to 2031, provided by Energistyrelsen in [4]. The negative numbers in the table represents a saving. Table 6.11: Emissions effects CO 2 CH 4 N 2 O SO 2 NO x Particles CO CHP Maabjerg Bioenergy - [kg] a CHP Vinderup -[kg] a CHP Maabjerg Værket - [kg] a Power use - [kg] b Manure treatment effects- [kg] Total - produced- [kg] Substituted - [kg] a Total - [kg] Value stationary facilities - [kr/kg] b 0,21 5,14 6 1,29 73,00 49,00 87,80 0 Total stationary value - [kr] Transport -[kg] a Value tansport - [kr/kg] c 0, Total transport value - [kr] Total value - [kr] Total kr Total costs - tonne tonne -6,20 kr/tonne Total costs - gas m 3-0,20 kr/m 3 a [2] b [4] c [5] The emissions entailed by the transportation are also displayed in the Table Compared to the emissions effects from the energy production and the fertilising change, the emissions from the transportation are almost negligible, entailing a total cost of less than kr without the NTF, in a total of approximately -2.2 million kr per year without NTF, and approximately 4 million with. By use of the CO 2 equivalence values, presented in chapter 3, the total induced change in the CO 2 emissions can be found. By including the carbon loss from the soil induced CO 2 production in the fertiliser emission values, a total CO 2 abatement of more than tonne per year is achieved. Maabjerg Bioenergy has presented the expected numbers to about tonne per year [31], separated into tonne reduction from the energy production and from the fertilising effects. This difference will be discussed further in subsection Table 6.12: Total avoided CO 2 equivalence from entire chain CO 2 CH 4 N 2 O Heat & Power emissions - tonne ,6 0,6 Fertilizer emissions - tonne ,5-51,5 Transport emissions - tonne 251,5 0,0 0,0 CO 2 equivalence [5] 1,0 25,0 298,0 CO 2 equivalence - tonne Total tonne

72 54 CHAPTER 6. RESULTS Distortion and dead weight losses The entailed distortion and dead weight losses are shown in Table The main subsidy is applied on the electricity production from combined heat and power, as described in section and in appendix section C.1. As all the produced biogas is used for combined heat and power production, a significant loss in terms of increased subsidy payments is enforced. A loss of about 6,5 million kr per year is enforced, taking into account the existing subsidy scheme, and the scheduled development of it within the next 20 years. The corresponding loss from the decreased tax and fee payment amounts to approximately 3,2 million kr per year, entailing a total yearly distortion and dead weight loss of 9,7 million kr. Table 6.13: Distortion and dead weight losses Annualised cost kr/year Change in fee payment Subsidy payments Total Maabjerg case total The total socio economic evaluation of the original set up is displayed in Table 6.14 and further visualised in Figure 6.2. From the table it can be seen that the result is a total Net present value cost of more than 300 million kr. From the table, it is evident that the main contributor is the biogas plant. Combining the annualised cost with the total amount of abated green house gas CO 2 equivalent, also including the substituted CO 2 from the energy sector, gives the CO 2 abatement cost or the CO 2 shadow price, displayed in (6.1). A cost of 685 kr per tonne CO 2 abated is obtained. The cost is higher than what was presented from both [19] and [25], but is also very dependent on technology use, and assumed emission coefficients, and will be discussed later kr Abatement cost CO 2 = = 685 kr per tonne (6.1) tonne The main cost in the chain origins from the biogas plant, as it can be seen both in Table 6.14 and Figure 6.2, whereas the main value is added from the end use of the gas, combined with the fertilising effects. The distortion and dead weight loss also adds significantly to the total socio economic cost, actually more than the transportation. The results, both in terms of physical numbers but also in economic values, will be discussed further in section 7.1.

73 6.2. MAABJERG - OPTIMISATION 55 kr Millions Figure 6.2: Annualised values of the total original analysis Table 6.14: Total economic overview NPV Annualised Cost Resource value Biogas value mio kr kr kr/tonne kr/m 3 Supply 25,12 1,85 2,85 0,09 Transport 94,34 6,94 10,71 0,35 Biogasplant 477,84 35,16 54,25 1,79 Distribution 23,38 1,72 2,65 0,09 End use -282,45-20,43-32,07-1,06 Fertilizer effects -108,22-7,96-12,29-0,41 Emissions -54,61-4,02-6,20-0,20 Distortion losses 132,13 9,72 15,00 0,50 Total 307,54 22,98 35,46 1, Maabjerg - optimisation Based on the Maabjerg case a few optimisation scenarios has been developed. The purpose of the scenarios has both been to optimise the original case, but also to investigate the possibilities in the model. The initial scenario, Optimisation 1(OPT 1), represents a case similar to the Maabjerg case without any constraints about resource use or biogas allocation, but with the same resource possibilities. The effect of a possible lowering of the investment cost in combination with an increased heat demand per tonne resource, more similar to those used in the [25] and [19] and described in chapter 4, is applied in this scenario. A case where the resource constraint, from the original investigated Maabjerg case, is substituted with a nitrogen leaching decrease demand, is created in Optimisation 2 (OPT 2). This case seeks to reflect the original faced problem at Maabjerg. In this scenario, the original relative plant investment cost is retained. Finally the effect of a possible lowering of the relative plant investment cost in combination with an increased heat demand per tonne resource, and a nitrogen leach abatement demand is investigated in scenario Optimisation 3(OPT 3). The varied constraints and parameters are displayed in Table Moreover is applied a constraint demanding a maximum use of 20% mink manure in the resource mix, as the mink manure in to high concentrations can entail technical problems, see Appendix E. The resource foundation in all three scenarios is equal to that displayed in appendix in Appendix F, which is also illustrated in Figure 6.3. The extra investment at Vinderup is also maintained, if biogas is supplied to the plant.

74 56 CHAPTER 6. RESULTS Table 6.15: Constraints in optimisation scenarios Description Alternative inv. cost N leach Combined Optimisation 1 Optimisation 2 Optimisation 3 Plant investment - [kr/tonne] 350 / 425 / / 425 / 500 Plant heat demand - [kwh/tonne] 30 / 20 / / 20 / 10 N leach demand - [tonne/year] Supply & Transportation The resource supply in the three scenarios appears from Table 6.16 and is further visualised in Figure 6.3. From the table and the figure it is clear that the amount of resources used in the first scenario only amounts to a small fraction of the resources used in the original case. Introducing the nitrogen leach reduction constraint of at least 100 tonne, increases the resource use to something near the original case level, which is also illustrated in the figure. The only resource which is not supplied in any of the cases, is the waste water slurry. The required transport and plant investment must be too high, compared to the value of the produced gas, as no fertilising benefits is modelled, from the use of the slurry. The use of the slurry actually possess a value, as investments at the local treatment plant were required if the slurry was not used at the biogas plant [24]. The investments related to the general resource supply is also illustrated in Table 6.16, varying almost linearly with the resource use. Tonne Thousands Cow manure Pig manure Deep litter Mink manure Whey Slurry OPT 1 OPT 2 OPT 3 Org Available Figure 6.3: Resource use in optimisation scenarios including resources available Even though the resource use in the optimisation scenarios differs a lot, the relative cost associated with the transportation are almost similar, as it appears from Table The total average transportation cost is slightly lower for the large scale plants, than for the medium scale. The transport cost of the industry supplied resource seems a bit high, compared to the manure cost, especially as they are delivered through a pipeline. One reason could be that if a possible truck should transport the industry resource, it would be dedicated to that, giving only tonne per truck per year. The similar manure trucks transports approximately tonne each year. Combined with the increased driver salaries, the pipeline solution is still the economic optimal solution, according to the model.

75 6.2. MAABJERG - OPTIMISATION 57 Table 6.16: Resource use & farm investments optimisation OPT 1 OPT 2 OPT 3 Number of farms Price [kr] Total price w. NTF [kr] Annualised cost [kr] manure tonne/year Litter tonne/year Industry tonne/year Slurry tonne/year Total tonne/year Table 6.17: Optimisation transport costs Price w. NTF OPT 1 OPT 2 OPT 3 [kr/unit] [kr] [kr] [kr] Truck kr/truck Reinvest Diesel cost 6,77 l Salary 243 hr Truck maintenance 1,82 km Pipeline investment km Electricity use - pumpwork 753 MWh Anualised cost kr/year Transport cost manure kr/tonne 9,24 10,36 10,36 Transport cost Industry and slurry kr/tonne 17,14 17,14 17,14 Transportcost average kr/tonne 11,61 11,14 11,14 Transport cost manure gas kr/m 3 0,41 0,48 0,48 Transport cost industry, slurry gas kr/m 3 0,17 0,17 0,17 Total cost average kr/m 3 0,26 0,37 0, Biogas plant optimisation The plant investment cost varies a lot in size between the three scenarios, as shown in Table Optimisation scenario two operates with the investment cost of 500 kr per tonne, whereas the investment cost in optimisation scenario one and three is a part of the optimisation. The possibilities used were as displayed in Table The investment cost of 79 and 206 million kr both corresponds to an investment cost of 350 kr per tonne, without NTF. This investment cost entails a heat demand of 30 kwh per tonne resource treated. The heat demand in scenario one and two is covered by a boiler, whereas both a biogas engine and a boiler is used in scenario three. As illustrated in Table 6.18, excess power production only occurs in scenario three, indicating that it is the only scenario where a biogas engine solution is used. The heat supply investment cost is smallest in scenario two, and largest in three, whereas the heat sale is almost equal in all three scenarios. The high amount of biogas used at the plant in scenario three represents the high degree of heat use in the process, combined with a complete utilisation of the possibility to sale heat. The biogas engine both supply heat to the process and district heat, in combination with a boiler. In scenario two a large amount of the produced biogas is supplied to Vinderup, despite the high extra investment, and the distribution costs associated with the plant. The administration costs are assumed constant, independent of the plant size, which means that the cost account for a larger share of the plant costs in optimisation scenario one, compared to the two others.

76 58 CHAPTER 6. RESULTS Table 6.18: Biogas plant optimisation economic values Price w. NTF OPT 1 OPT 2 OPT 3 [kr/unit] [kr] [kr] [kr] Investment - plant Reinvestment - plant Electricity use 753 kr/mwh Maintenance Facility Salaries 8,44 kr/tonne/year Insurance 2,025 tonne/year Administration Ressource buy kr/year Investment heat supply Heat suply OM Annualised cost kr/year Treatment costs - tonne kr/tonne 58,43 68,29 60,90 Treatment costs - gas kr/m 3 1,30 2,24 2,00 Heat sale 331 kr/mwh Electricity sale 479 kr/mwh Total Biogas plant total Total biogas plant cost - tonne kr/tonne 11,18 51,16 19,92 Total biogas plant cost - gas kr/m 3 0,25 1,68 0,65 The increased treatment cost in terms of kr per m 3 biogas in scenario two compared to scenario one, represents the high investment cost, combined with a heat sale in the same order as scenario one. The lower treatment cost in optimisation scenario three of course represents the decreased investment cost. The low total plant costs, both in terms of per tonne and per m 3 biogas, in scenario one, expresses the high biogas potential in the used resources, combined with a relative large heat production at in plant facilities and a low relative investment cost, see Table The heat sale from scenario two is approximately the same as scenario one, but combined with a much larger investment cost, the total cost per tonne and per m 3 biogas increases. The total plant cost per tonne and m 3 biogas is much lower in scenario three, compare to scenario two, both because of the lower investment cost but also because of the electricity sale. One important note is that none of the three plants entails a socio economic gain, evaluating their economy independently Distribution & End use The distribution and end use of the biogas also differs a bit between the three scenarios. The allocation and the total biogas production appears from Table Initially it is clear that none of the three scenarios choose to upgrade the gas. Even though an investment for both pipeline and the required extra investment cost at Vinderup combined heat and power plant, the plant is still preferred rather than upgrading in scenario two. That biogas is supplied to Maabjerg værket in the first scenario, is because the heat sale limit from the biogas plant is reached. In scenario two and three is applied the possibility of allocating upgraded gas to transportation, but it is not preferable

77 6.2. MAABJERG - OPTIMISATION 59 to upgrade rather than supply Vinderup and Maabjerg værket. A reason for this could be the high subsidies to upgraded biogas, which would entail a distortion loss, when evaluating the economy from a societal perspective. Table 6.19: Allocation of biogas in optimisation scenarios OPT 1 OPT 2 OPT 3 Biogas plant [mio. m 3 ] 5,31 4,97 11,10 Vinderup [mio. m 3 ] - 6,13 - Maabjerg værket [mio. m 3 ] 2,20 2,20 2,20 Upgrade [mio. m 3 ] Total [mio. m 3 ] 7,51 13,30 13,30 The total costs associated with the distribution, end use and upgrade of the biogas appears from Table It appears that supplying biogas to Vinderup is associated with a high distribution cost, because of the distance. It is also clear that both a biogas engine and a boiler is used at Vinderup, in scenario two, in order to fulfil the heat demand. Though the annualised cost from the end use are significantly higher for scenario two, the total negative cost per m 3 biogas is also higher for that scenario, compared to scenario one and three, due to the large heat and power sale. Table 6.20: Optimisation distribution cost Price OPT 1 OPT 2 OPT 3 [kr/unit] [kr] [kr] [kr] Investment pipeline km Operation pipeline 753 MWh Investment Vinderup O&M Vindeup Investment Maabjerg værket O&M Maabjerg værket Investment Upgrade O&M Upgrade Annualised cost Electricity sale 479 MWh Heat sale 331 MWh Upgrade biogas sale 3,62 kr/m Total End use total annualised cost Total costs - gas kr/m 3-0,49-0,89-0, Fertilizing & Emission effects The fertilising effects from the three optimisation scenarios are summarised in Table The effects are almost linearly dependent of the amount of manure treated at the plant. Scenario one represent the free optimisation, whereas scenario two and three has a constraint of avoiding minimum 100 tonne of nitrogen leaching. As the resources used in scenario two and three are identical, so are the fertiliser effects. The relative value of the fertilising effect per tonne treated at the green line are almost similar, see also Table The corresponding value per m 3 biogas produced though, is higher for scenario

78 60 CHAPTER 6. RESULTS two and three compared to scenario one. The reason for this is the lower biogas potential from the additional amount of resources used in scenario two and three, compared to scenario one. Table 6.21: Fertilising effects Price w. NTF OPT 1 OPT 2 OPT 3 [kr/unit] [tonne] [kr] [tonne] [kr] [tonne] [kr] Reduced use of Nitrogen Reduced Nitrogen leaching Loss of C in soil Annualised cost Annualised cost - green line kr/tonne -16,61-16,83-16,83 Annualised cost - gas green line kr/m 3-0,37-0,55-0,55 The emissions related to the three optimisations scenarios are displayed in Table It appears from the table that the valued CO 2 emissions changes are positive, indicating an increased amount of emissions. This is not surprising as the biogas is used in the energy sector, from where the CO 2 abatement is not valued as the CO 2 emission is assumed constant, as explained in subsection This entails that the increased emissions origins from the increased resource transportation. The emissions in general are almost scalable with the amount of resource treated, which also accounts for the annualised cost. Looking at the total CO 2 green house gas emission(ghg) abatement, also including the substituted energy production, about tonne abatement is achieved in scenario one, compared to approximately tonne in scenario two and more than tonne in scenario three. The difference between two and three, is the allocation of the biogas. Even though a larger share of the biogas is used for process heat in scenario three than in scenario two, the total amount of abated emissions is larger as a larger share of electricity production is substituted. This abatement is not valued, which is why scenario two also has the largest gain to the society. Table 6.22: Emission values, [5] & [4] OPT 1 OPT 2 OPT 3 [kg] [kr] [kg] [kr] [kg] [kr] Valued CO CH N 2O SO NO x particles CO Total Annualised cost Total CO 2 abatement Distortion losses The distortion losses associated with the three optimisation scenarios all appear from Table It is clear that the electricity production from biogas engines induces a larger loss from subsidy payments, than the corresponding heat production from biogas boilers. Though the biogas production is similar in scenario two and three, the induced loss is significantly greater in scenario three, because of the different biogas utilisation.

79 6.2. MAABJERG - OPTIMISATION 61 Table 6.23: Distortion losses OPT 1 OPT 2 OPT 3 Change in fee payment - kr/year Subsidy payments - kr/year Total annualised cost - kr/year Total results Millions kr OPT 1 OPT 2 OPT 3 Org Figure 6.4: Annualised cost in each step The total annualised costs associated with the three scenarios are summed up in Table 6.24 and in Figure 6.4. From the table it is clear that the only scenario creating a socio economic gain, here illustrated with a minus, is scenario one. Though it is not very large, it is a gain. Both scenario two and three, with the constrained nitrogen leach decrease, ends up with significant yearly costs to the society. As scenario one ends up with a socio economic gain, the CO 2 abatement cost is also negative, ending up at approximately 100 kr per tonne. Scenario two, with the large relative investment cost and the constrained nitrogen leach, ends up with a CO 2 abatement cost of approximately 400 kr, which is rather close to what was obtained in the report from IFRO [19]. Looking at the total annualised cost of the scenario, it ends up at almost 10 million kr, which is less than half of what was obtained in the original scenario. The reason for that is the lower total plant investment cost, due to the fact that the slurry is not used.. The annualised cost associated with scenario three is about 5.5 million kr, which is also significantly smaller than that of the original case. Here a major reason is the lower plant investment cost per tonne, assumed. In scenario three also the cost per tonne is almost half the cost per tonne in scenario two, which treats the same amount of resource per year. The achieved CO 2 abatement cost in scenario three is less than half of what is obtained in scenario two, and below 200 kr per tonne, as it also appears from Table 6.24.

80 62 CHAPTER 6. RESULTS Table 6.24: Optimisation scenarios total OPT 1 OPT 2 OPT 3 Original Supply - kr Transport - kr Biogasplant - kr Distribution - kr End use - kr Fertilizer effects- kr Emissions - kr Distortion Loss - kr Total annualised cost kr Resource cost kr/tonne -5,42 22,35 12,75 35,46 Biogas cost kr/m 3-0,12 0,73 0,42 1,17 CO 2 abatement cost kr/tonne -97,48 373,68 176, Sensitivity analysis Heat & Power prices The heat and power prices might seem a bit conservative, compared to what is used in [25] and [19], which values the power 460 and 720 kr per MWh respectively, compared to the annualised power price of 355 used here. Therefore the effect of increasing the power price has been investigated. A scenario has been put up, similar to the original case, but without the resource use constraints, and the use of the biogas is neither constrained. This means that no applied limitations or constraints drives the model, except for the possibility of creating a socio economic gain. In Figure 6.5 is illustrated how increasing the value of the power distributed to the net from 355 kr per MWh to 710 kr per MWh, affects the socio economic gain, the CO 2 abatement cost, the biogas production, the resource use and the heat and power production. The first optimisation resulting in a socio economic gain, is with a power price of 440 kr per MWh. The socio economic gain increases almost linearly with the increased power price, see sub figure (a), whereas the amount of resources used is almost constant, see sub figure (b). However the amount of biogas produced also increases to a level of about 9 million m 3, see sub figure (c). Looking at sub figure (e) it is clear that initially both pig and cow manure is used. However the increased value of the power, increases the use of cow manure, even though the distance is longer. The distance does not appear from the figure, but the level of approximately tonne of cow manure represents the total amount in zone 1 and two, see appendix Appendix F. From sub figure (f) in Figure 6.5, it is clear that the excess heat production from the biogas plant stabilises at the maximum district heat supply level applied, of MWh. It also appears from the figure that an increased share of the heat is produced with a biogas engine, inducing the illustrated increased power production. That also implies that a large share of the heat is initially produced with a biogas boiler. Similar to the study of the power prices, a study of increased district heat prices has also been conducted. The district heat network is often smaller local closed grids, implying that the prices differs between the grids, as they exist as smaller closed markets with no interchanging. Therefore, the assumed district heat value is very importing when evaluating a system. From appendix section A.3 it is known that the district heat price from Vinderup combined heat and power plant is about 420 kr per MWh. That, of course, does not represent the factor price, but as no earnings are

81 6.3. SENSITIVITY ANALYSIS 63 kr Millions Total Annualised gain kr/mwh kr CO2 abatement , , , ,00 0 0, kr/mwh CO2 abatement cost (gain) CO2 abatement Tonne Thousands (a) Annualised socio economic gain (b) CO 2 abatement and abatement cost m 3 Millions Biogas produced kr/mwh tonne Thousands Amount of resources used kr/mwh (c) Total biogas production (d) Total amount of resources used tonne Thousands Ressource use kr/mwh Manure Cow Manure Pig Deep litter Manure Mink Whey (e) Resource utilisation MWh Thousands Heat & Power sale kr/mwh Power sale bgp Power sale end Heat sale bgp Heat sale end (f) Amount of power and heat sold Figure 6.5: Sensitivity analysis of increasing the power price

82 kr Millions 64 CHAPTER 6. RESULTS Total Annualised gain kr/mwh kr CO2 abatement , , , , , kr/mwh CO2 abatement cost (gain) CO2 abatement Tonne Thousands (a) Annualised socio economic gain (b) CO 2 abatement and abatement cost m 3 Millions Biogas produced kr/mwh Thousands tonne Amount of resources used kr/mwh (c) Total biogas production (d) Total amount of resources used tonne Thousands Ressource use kr/mwh Manure Cow Manure Pig Deep litter Manure Mink Whey (e) Resource utilisation MWh Thousands Heat & Power sale kr/mwh Power sale bgp Power sale end Heat sale bgp Heat sale end (f) Amount of power and heat sold Figure 6.6: Sensitivity analysis of increasing the district heat price

83 6.3. SENSITIVITY ANALYSIS 65 allowed on district heat production, the price could be used as an indication of the production costs associated with the district heating. Therefore is performed a sensitivity analysis of increasing the price of the substituted district heat from 245 to 445 kr per MWH. None of the two studies [19] and [25] operates with excess heat or district heat production, which is why the price from Vinderup is the only comparable number, to the numbers obtained from Energistyrelsen [4] and displayed in appendix section B.1. The results can be seen in Figure 6.6, divided similar to the power price analysis. From the figures it is evident that no big change in the heat prices was essential, in order to obtain a socio economic gain. A district heat price of 255 kr per MWh was sufficient to entail a socio economic gain, according to all six figures. It is also clear that the 7 million m 3 biogas produced, see Figure 6.6 (c), are used to supply Maabjerg Bioenergy and Maabjerg værket, with a heat price up till 400 kr per MWh see Figure 6.6 (f), as the end use heat sale of MWh corresponds to the applied demand from Maabjerg værket. With a heat price larger than 400 kr per MWh, biogas is also supplied to Vinderup, which is clearly visible in Figure 6.6 (b) to (f), as the biogas production increases significantly. From b it is also clear that the initial increased gain per tonne CO 2 abated only depends on the increased heat price, as the amount of CO 2 abated is constant. Increasing the production, due to the delivery to Vinderup, increases the amount of CO 2 abated, leading to a lower CO 2 abatement economic gain per tonne. However Figure 6.6 (b) to (f) also clearly illustrates the district heat limits applied to thee three plants, and the extra investment accompanied, when supplying to Vinderup. Lastly it is evident that the heat production is preferred to combined heat and power, as seen in Figure 6.6 (b). This might be due to higher investment cost for the combined heat and power producing technologies compared to the heat technologies, combined with a slightly lower efficiency of the CHP technologies. The very constant heat productions in both analysis, reflects the way the district heat demands from the end users are applied, as the demands are given as specific numbers and not applied as upper limits Slurry Value As mentioned in relation to the three optimisation scenarios, the slurry was not preferred in any of the scenarios. That relates both to the investments related to supply the resource, but also as the resource has no direct externality effect, as the residual products from the biogas production, cannot be applied as fertiliser in the same way as the residuals from the green line. However the slurry might very well possess a value, as other investments were required at the waster water treatment plant, if the slurry was no used at the biogas plant. An evaluation has therefore been performed, revealing the required value of the slurry, in order to obtain a socio economic gain from a biogas plant. In addition to the increasing slurry value, no changes has been applied to the scenario compared to the analysis of the heat and power prices. The effect of applying a value to the waste water slurry can be seen in Figure 6.7. Here are illustrated how the resource use distributes(a), the size of the socio economic gain (b), the induced biogas production (c) and the entailed CO 2 reduction and corresponding CO 2 abatement cost(d). From the figures it is evident that a value of 80 kr per tonne is sufficient to obtain a socio economic profitable plant. Again the heat demand limit appears from Figure 6.7 (c) with the initial production of 7 million m 3 biogas per year. Increasing the value of the slurry further to 95 kr per tonne, increases the production to a level of about 8,5 million m 3 per year. The total socio economic

84 66 CHAPTER 6. RESULTS tonne Thousands Ressource use kr/tonne Manure Cow Manure Pig Manure Mink Slurry Whey kr Millions Total Annualised gain 3,5 3 2,5 2 1,5 1 0, kr/tonne (a) Resources used (b) Annualised socio economic gain m 3 Millions Biogas produced kr/tonne CO2 abatement kr/tonne kr CO2 abatement cost (gain) CO2 abatement 25,00 20,00 15,00 10,00 5,00 0,00 Tonne Thousands (c) Total biogas production (d) CO 2 abatement and abatement cost Figure 6.7: Sensitivity analysis of increasing the district heat price gain obtained with the value of 80 kr per tonne is not significant. Similar is the obtained shadow price of the CO 2 not very high, below 20 kr per tonne. However, the reason for this is that the initial plant inducing a gain is larger than in the analysis of the heat and power prices, with a total capacity of almost tonne per year. This also induces a high amount of CO 2 abatement which leads to a low shadow price.

85 CHAPTER 7 Discussion The evaluation of the model and the results can be divided into several topics. Does the formulated model reflect the biogas production, and the set up around a plant? Are the assumed values regarding biogas potential, nutrient content and technical efficiencies of both transportation, biogas plant and end use valid? These are both important in order to judge the correctness of the model, regarding the physical results from the model, e.g. heat and power production and nitrogen leach decrease. This is discussed in section 7.1. Further it is interesting whether the assumed economic values applied to the different resources, technologies, fuels and heat and power production are of a realistic order, and especially regarding the externalities. The effect of some of the economic values have already been investigated in the sensitivity analysis, and some of the physical values and main economic values has been checked with the values presented by Maabjerg Bioenergy, in order to validate the model. Some of these results will also be discussed in section 7.1, in combination with the results from the similar studies. The importances of the different economic values will be discussed in section 7.1.2, based on the results obtained in the three optimisation scenarios from section 6.2 and the sensitivity analysis in section 6.3. Possibilities and challenges in the model will be discussed in section 7.3, where the difficulties regarding implementation of another case will be examined. 7.1 The Maabjerg Case Physical Values An essential reason for analysing the original Maabjerg case with almost no optimisation, was to check the physical values obtained with the model. As the Maabjerg Bioenergy plant has not been running for more than a year, the plant does not possess that many data about the operation, and use. The values obtained with the model have therefore been compared to the technical values presented before the commissioning of the plant. The stated resource use implemented in the model, induces a biogas production similar to what is expected at Maabjerg Bioenergy. The numbers about transportation are difficult to check, though the required number of trucks seems to fit the actual requirement at Maabjerg. Furthermore the numbers used for diesel efficiency and truck maintenance are taken from data provided by the Transport department at DTU, [35]. As mentioned are the operational data for the Maabjerg Bioenergy plant not well known. The technical numbers regarding electricity and power use has been assumed based on own calculations, and technical values provided in similar studies, [19] and [25], and technical reports regarding biogas production, [33] and [32]. It has not been possible to check the accuracy of these values, 67

86 68 CHAPTER 7. DISCUSSION which is why e.g. the effect of changing the relative heat demand from the process has been investigated in the scenario analysis. The fertilising effects has also been implemented, and compared to the values presented by Maabjerg Bioenergy. The nitrogen leaching decrease found with the model, based on the calculations presented by [25], seems to fit very well with the numbers provided by Maabjerg Bioenergy. The methane and nitrous oxide emission effect does also seem to fit very well with the provided number, when they are transferred into CO 2 equivalent values. The effects on the phosphor leaching has not been modelled with the same degree of detail, as numbers and covering theory within the topic has not been found, which is why these numbers are not mentioned. A phosphor content in the resources is applied in the model, and the content of the separated part is also found. In total the wet manure products supply 626 tonne of phosphor to the plant each year, according to the model. Of these, 197 tonne remains in the wet fraction, which is returned to the farmers after the treatment and separation at the plant, also according to the model. From the Måbjerg Gården figure presented in [31], it appears that 560 tonne of slurry should be supplied to the plant, and that 165 tonne of these are returned to the field. Both numbers obtained with the model therefore seems to be valid. However turning this into a phosphor leach is not obvious, and further investigations of it has not been performed. The potential in the fibre part of the separated treated manure from the green line has neither been investigated further. The nutrient content is calculated, but the use of it has not been modelled, which means that a possible energy content and a possible fertilising effect from the use of it, is not implemented in the model. The use of the treated slurry, both in terms of the wet and the fibre part, is neither investigated. This means that the model contains some dead ends in terms of the fibre part from the green line, and both parts of the treated resources from the slurry line. These could both posses a value or a cost to the society, which is not investigated further. The emissions from the implemented technologies are also important. The values used in the model are in general provided by Danish Center for Environment and Energy [2]. However, the emission values are based on average values from the different technologies. This could imply that the emission numbers used for the biogas engines might reflect emission numbers from older engines and not the emissions from brand new engines. The same argument accounts for the trucks used to transport the resources. With regard to the substituted technologies, the emissions here are also important with respect to how the biogas is optimally used. In the model it is initially assumed that combined heat and power, produced from the biogas engines, substitute natural gas fuelled engines. From Table 6.12 it appears that a CO 2 reduction of approximately tonne is achieved in the energy sector. Maabjerg Bioenergy has presented a number of tonne CO 2 per year reduction from the energy production. This number might also cover the use of the separated fibre parts, which could be the reason for the difference. Another obvious reason could be the substituted technology chosen. Substituting coal would increase the abated amount of CO 2 significantly. The small amount of power produced in excess to the process heat is assumed to substitute power in terms of emissions corresponding to the average emissions from power production in Denmark within the next 20 years, provided by Energistyrelsen in [4] Economic Values First of all no socio economic gain is obtained, but an annualised cost to society of more than 20 million is obtained, corresponding to a negative net present value of more 300 million kr, see Table The loss is more than twice the loss obtained in The IFRO report and the DCE study.

87 7.1. THE MAABJERG CASE 69 However the evaluated plant is also more than twice the size of the two model studies. Applying a value to the abated phosphor reduction might decrease the cost to society. The reduced nitrogen leach induced an annualised gain of 6 million kr a year, and a similar gain from reduced phosphor leaching, would decrease the cost to society. This is important, as a decrease of the phosphor leach was one of the main motives for building the plant. However, there does not seem to exist a consensus about valuing phosphor leaching, when evaluating biogas plants. Comparing the obtained CO 2 abatement costs, a value of approximately 680 kr per tonne is obtained in the main case of this study. That is less than the 50% of the value from the DCE study of kr, but significantly larger than the IFRO study, with a value of 414 kr per tonne CO 2. Two large differences between the results presented in this study and the study from IFRO, is the biogas potential of the treated resources and the relative plant investment cost. In the IFRO study, an average biogas production of more than 40 m 3 biogas per treated tonne is achieved whereas only 30 is obtained in this study. Moreover is the relative investment cost much bigger in this study compared to the IFRO study. However, the cost of transportation obtained in this study is significantly lower than what is obtained in the IFRO study and what is used in the DCE study. One reason here is that in this study, it is assumed that the trucks will never travel empty. In the IFRO study at least, it is assumed that one third of the trips will be with an empty truck, which of course increases the transportation costs. The DCE study only relies on manure from pigs in the scenario used for comparison, which is not realistic due to the low biogas potential in manure from pigs. However, the other studies presented in that report are neither comparable as they rely on a large share of energy crops, which is why the 100% pig scenario is used for comparison. A loss of biogas due to lower heat demand in the summer is assumed in the IFRO study, which is neither incorporated in this study, as they do not expect so, at the Maabjerg Bioenergy plant. Finally is all the biogas used in this study transferred into heat and power, meaning that the value of it is expressed by the heat and power production substituted, whereas the main part of the gas in both the DCE and the IFRO study is valued by the energy content in terms of natural gas prices. As the investments for the end use is applied in this model, it must also be reasonable to value the end products, in order to obtain representative results. Comparing the total net present value cost of more than 300 million kr, obtained with the model, with the net present socio economic gain of 1 billion presented by Maabjerg Bioenergy in [22], indicates some fundamentally differences in the studies behind the two numbers. According to Allan Lunde, presented in appendix section A.2, the main value of this gain represents the avoided investment for hectare land, necessary to maintain the livestock production. With a field price in the area of approximately kr per hectare [20], an investment cost of 1,4 billion kr is avoided. Taking this number into account, the evaluation performed in this study would also result in a huge socio economic gain Optimisation Variations & Sensitivity Based on the results from the main study and some of the values used in the [25](DCE) and the [19](IFRO) studies, three optimisation scenarios was applied in the model. The first one should reveal the potential in lowering the investment cost on behalf of an increased process heat demand. From the results it was clear that the optimal solution was obtained with a plant investment cost of 350 kr per tonne per year and a heat demand of 30 kwh per tonne. The result was a relatively small plant( tonne per year), with a high use of whey and the manure products available

88 70 CHAPTER 7. DISCUSSION close to the farm. The relative investment cost was still higher than the investment cost used in the two studies from IFRO and DCE. However the result here was a socio economic gain of 0,9 million in annualised cost, equal to a net present value of 12,3 million kr, and a gain to society of 97,5 kr per tonne CO 2 abated. It is within very small margins that the positive result is obtained, however it is still a positive result. Replacing the resource constraint from the original case with a nitrogen leach decrease demand is the main idea of optimisation scenario two. The original relative investment cost of 500 kr per tonne per year and a heat demand of 10 kwh per tonne is maintained. The huge difference in the resource use between the original case and this is that the waste water slurry is not used in the optimisation. In total, the scenario ends up with a total annualised socio economic cost to society of close to 10 million kr per year, corresponding to a net present value cost to society of 132 million kr. The induced CO 2 abatement cost is 374 kr per tonne CO 2 abated. Despite the high relative investment cost, the obtained abatement cost is significantly lower than what was obtained in the original scenario, and also slightly lower than the cost presented in the IFRO study. The solution illustrates that the biogas potential in the waste water slurry does not prove the investments related to transport and treat the slurry, as no externality gain of using the slurry is applied. Finally, the two scenarios are combined into a single scenario, illustrating both the demand for nitrogen leaching decrease, but also the possibility in lowering the investment cost on behalf of the heat demand. Hereby is obtained an annualised socio economic cost to the society of 5,6 million kr equalling a total net present value cost of 76 million kr. That implies a CO 2 shadow price of only 176 kr per tonne which significantly lower than what was obtained in the IFRO study. The decreased investment cost and the increased heat demand illustrates the possibility of increasing the temperature in the reactor, further decreasing the hydraulic retention time of the resources. This temperature increase might not work with all resources. As described in Appendix E, the manure from mink contains some substances that might be inhibiting to the biogas production process, if the temperature is to high. Therefore the different temperature levels should also reflect some different limitations with regard to the resource mix. In the model it is also assumed that the relative investment cost is unaffected by the size of the plant, meaning that the investment cost per tonne is the same for a small scale plant as for a large scale plant. That might be a very rough assumption. However, a study of the investment costs related to biogas plants in Denmark within the last 30 years, did not reveal that the assumption should not be valid, see Appendix G. The numbers contain both constructed plants and model studies of plants. The sensitivity analysis of the applied power and heat prices respectively, reveals that even small adjustments of the applied prices can entail a socio economic gain. Increasing the applied value of the produced district heat from 245 kr per MWh to 255 kr per MWh, makes it possible to set up a solution inducing a socio economic gain to the society, even with the high investment cost. Similar socio economic gains are obtained when the value of the produced electricity is increased from 355 to 440 kr per MWh. It is clear from the sensitivity analysis of the heat and power that the cheaper production technologies favours the heat production to the combined heat and power production. From the analysis it also appears that the distance to the plant is crucial when the manure resource use is optimised as both the manure from pigs and cows settles on specific levels. Applying a value to the use of slurry, can also induce a socio economic gain. The use of slurry is limited by the amount available, which of course reduces the total effect. A value of the slurry of 80 kr per tonne induces an annualised gain to the society of kr which is not much. Similarly

89 7.2. GENERAL DISCUSSION ON METHOD AND ASSUMPTIONS APPLIED 71 a value of 100 kr induces a yearly socio economic gain of 3 million kr, which is neither significant. In the real case Maabjerg Bioenergy, the biogas engines is preferred to the boilers. This is because of the high degree of subsidy on the power production, which favours the power production technologies, in order to obtain a private economic profitable solution. In this study, the power production induces a cost, in terms of a distortion loss, due to the high degree of subsidy, explained in subsection This is another reason for the model to prefer the heat production from boilers, to biogas engines. Upgraded gas receive approximately the same subsidy per GJ end product compared to electricity, which might be a reason why the upgrade of gas is not preferred by the model, in any of the scenarios. 7.2 General discussion on method and assumptions applied The manure pipeline transport is omitted from this study. Looking at the size of the transport cost compared to the total annualised cost, the transport cost is not the decisive cost, though still significant. However, moving the manure and the treated wet residual fertiliser by pipeline would still require trucks to transport the manure from the farms to the pipeline connections. The idea is to create two filling stations in the countryside where both treated wet residual can be collected and fresh manure can be delivered. This means that the number of trips will remain at the same level, hence the same amount of time will be used on filling and emptying the trucks. The driving time would of course decrease, but as mentioned in section 6.1.2, the driving time only accounts for half of the time used when transporting the manure. In addition to the investment cost of the pipeline system, the transportation will also require electricity, which is why it is questionable how much can be saved by the solution. With regard to the abated CO 2, it is the general approach, according to [16] and [17], that CO 2 emissions abated in the energy sector is not valued in socio economic evaluations. This might seem reasonable when considering the energy sector in short term. However, a decrease in the CO 2 emissions from energy producing technology now, might lead to a reduction in the amount of quotas in the long term, inducing an actual reduction in the CO 2 emissions. Therefore it could be argued that a CO 2 reduction in the energy sector should be applied a value to the society too. Another very important aspect which is not incorporated in the model is the time variation. This variation both regards the manure supply, in order to find necessary resource storage capacities for the plant, and also to create a more thorough modelling of the transportation. Moreover it could be important, in order to reveal if a given biogas production actually can supply the suggested purchasers, or they all have a demand for biogas at the same time, requiring storage capacities. In order to obtain valid results with a model working with hourly or daily time steps, reliable data for both manure or resource delivery, and heat or biogas demands from purchasers are required. The upgrade technology has not been a part of any of the obtained solutions. However the technology might be preferable if the time variation within the biogas purchasing was implemented. As the upgrade is not a part of the initial solution at Maabjerg Bioenergy, the modelling of the upgrade technologies has not been very extensive. A more thorough investigation of the technologies used for upgrading, the possibilities within heat recovery in combination with a biogas plant, and the externalities related to the use of upgraded biogas might reveal that the technology possess bigger

90 72 CHAPTER 7. DISCUSSION potentials and possibilities. 7.3 General Applicability of the model In order to asses the applicability on other projects with the model, a possible modelling of the planned biogas plant in Tønder has been explored. Information about the plant has been obtained through meetings with the consultancy of the project. The plant in Tønder is intended to be in the same order of size as Maabjerg Bioenergy, and also with a huge amount of resource delivery from the local livestock production. Compared to Maabjerg Bioenergy, a large fraction of the manure used at the Tønder plant, will origin from organic livestock production. These resources have to be treated separately, which is why they should also be applied as separate types of resources in the model. It could be applied rather easy in the model, by adding a separate organic line, similar to the slurry and green lines. The fertiliser effect from the organic slurry also differs significantly to those from conventional slurry. The reason for that is that the fodder produced for the livestock, cannot be produced by use of organic fertiliser. This implies that no reduction in fertiliser use is achieved. However, the increased efficiency of the nitrogen in the treated slurry induces a possible increased production from the fields, where the treated manure is spread. These other fertilizer effects would require some additions to the model, but they are not very different to what is already applied in the model. The resource transport is planned to be performed by dual fuelled trucks, which run on both diesel and upgraded gas. This possibility is already applied in the model. A demand for use of biodiesel is also a part of the solution in the Tønder case. Biodiesel is not a part of the model now, but could be added easily, with a cost and an emission factor. An addition to the transportation is that litter products are expected to be transported on container trailers, in combination with the wet manure trucks. Such a solution, would also demand some changes in the model, in terms of time use, when litter and manure products are transported together, which would neither be a big challenge to implement. The plant will probably be built in an area without any district heat network. This entails that only a small heat production on site would be required. Hence a production of excess power will be very small which is one of the reasons why the focus will be on upgrading the gas rather than producing heat and power. The upgrade is a part of this model, though a more thorough upgrade modelling, in terms of heat requirement and pressurising of the upgraded gas, would be preferable if the model should be more accurate. Finally, the already mentioned time aspect would be interesting to incorporate in the model, both with regard to seasonal variations, but also long term variations. Regarding the Tønder case it would be interesting, as the resource mix is expected to change significantly throughout the time frame of the project. All things considered, the Tønder case has revealed that the model formulated is organised in a fairly generic way, making it possible to asses and evaluate other plants than the original investigated biogas plant, with only a few modifications.

91 CHAPTER 8 Conclusion Reliable values for biogas production from manure resources has been obtained and used to determine the biogas potential in a given mix of resources with the formulated mode. As presented in section 6.1, a biogas production of more than 19 million m 3 biogas is obtained with the use tonne resources, with a manure share of 70%, less than 4% litter, 8% industrial waste and 18% waste water slurry, leading to an average biogas production of 29 m 3 per tonne resource. An average transport cost of 11,1 kr per tonne resource is also obtained with the model, composed of 11,0 and 11,6 kr per tonne truck and pipeline transported resource respectively. The total biogas plant treatment costs amounts to more than 50 kr per tonne, with an original plant investment exceeding 400 million kr, and a total annualised plant cost of 35,2 million kr. The produced biogas plant has an energy content of more than 100 GWh, leading to a total district heat production of 56,4 GWh and a power production of 42,2 GWh. Including distribution and end use investments and operation, a simple socio economic result is obtained, with a net present value cost of almost 340 million kr, corresponding to an annualised cost of 25,2 million and a cost of 39 kr per tonne resource. The treatment of the slurry induces some positive effects within the fertiliser characteristics. This reduces the need for synthetic nitrogen fertiliser with more than 270 tonne per year, leading to a yearly save of 2,8 million kr. Furthermore, the leaching of nitrogen is reduced with more than 115 tonne per year, entailing a socio economic gain of more than 6,2 million kr. A reduction of the carbon content of the soil, leading to a fossil CO 2 emission, produces a loss of approximately 1,1 million kr per year. In total that leads to socio economic gain of 15 kr per tonne resource used on the green line. The changed emissions induced by the biogas production and the use of the gas leads to a socio economic gain of 4 million kr, or a bit more than 6 kr per tonne resource treated. The production also entails a socio economic distortion loss to the society, from increased subsidy payments and decreased tax payment. These subsidy losses amounts to 9,7 million kr. In total that leads to a socio economic annualised cost of almost 23 million kr, equal to a cost of 35,5 kr per tonne resource treated. With an induced CO 2 abatement of tonne, a CO 2 shadow price of 685 kr per tonne is achieved. The obtained results are very consistent to those obtained in both the large study presented in [25] and the study presented in [19], though the economic values used in this study in general are conservative, see subsection The results obtained in the three optimisation scenarios reveals that it is possible to set up realistic 73

92 74 CHAPTER 8. CONCLUSION scenarios that can produce a socio economic gain, when adjusting the relative investment cost. They also confirms that the investment cost related to the biogas plant is the governing cost, related to a biogas projects. The sensitivity analysis has revealed that even smaller changes in heat and power prices can imply changes in the total of a project, from a socio economic cost to a significant gain. By increasing the value of the produced electricity to approximately 700 kr per MWh, which is a value used in other studies, the optimisation has turned from no solution to an annualised gain to society of 6 million kr. Similarly does an increase in the value applied on the produced district heat from 245 kr per MWh to 400 kr per MWh entail a shift from no solution, to an annualised socio economic gain of 7 million kr. The analysis has also clarified that resource values are extremely important, both in order to obtain valid results but also in order to illustrate the actual scenario, in terms of willingness to include for example slurry. Generally seen, it can be concluded that a model has been formulated, which can provide trustworthy and comparable answers regarding the physical values related to the biogas production chain. The model can also provide an extensive insight to the economy related to the biogas production. This includes both the simple energy economics but also the related externalities in terms of fertiliser effects and emissions. The genericness of the model was challenged by a theoretic implementation of a possible biogas plant in Tønder. The evaluation revealed that by small modifications and few extensions the model is capable of handling very different plants.

93 CHAPTER 9 Further Work Evaluating the project has revealed that a few topics would be interesting to investigate more thoroughly and implement in a more detailed model. As Mentioned in chapter 7, the time resolution could be very interesting to look at. This model evaluates a whole year, but a more detailed evaluation, on daily or hourly basis could be very interesting, both with regard to resource delivery but also for the biogas utilisation. That would also imply a need for modelling of storage facilities, which could be very interesting. When increasing the time resolution it might also be interesting to look further into the details of the plant modelling. Hereby a more detailed model of the hydraulic retention time and the actual resource mix, and the significance for the produced gas, in terms of biogas composition and methane, CO 2 and sulphur content and so, could be very interesting. With regard to the resource use, it could be interesting to incorporate the effect from growing energy crops. Further also a more detailed evaluation of the phosphorus effects in the treated slurry could be interesting. Changing the optimisation variable from the total socio economic gain, to a weighed combination of e.g. the CO 2 shadow price, the nitrogen and phosphor leach decrease and socio economic gain, could maybe also be relevant. Widening the scope a bit, it could also be interesting to model the corresponding private economic effects. This should not include a private economic optimisation, but to present the optimised socio economic solution in a private economic evaluation as well. 75

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95 Bibliography [1] Danske nøgletal - danish key energy numbers. Web page, URL dk/info/tal-kort/statistik-nogletal/nogletal/danske-nogletal. [2] Emission factors. Internet data set, January URL videnudveksling/luft/emissioner/emission_factors/. [3] Subsidies for biogas production. Web page, June URL ens.dk/undergrund-forsyning/vedvarende-energi/bioenergi/ tilskud-biogas. [4] Spread sheet from energistyrelsen with tables containing socio economic conditions - energistyrelsens regneark med tabeller med samfundsøkonomiske beregningsforudsætninger. Excel data sheet, October URL ens.dk/info/tal-kort/fremskrivninger-analyser-modeller/ samfundsokonomiske-beregnings-forudsaetninger. [5] Technological evaluation of alternative fuels for the transport sector - teknologivurdering af alternative drivmidler til transportsektoren. Excel data sheet, may URL alternative-drivmidler-transportsektoren-21. [6] New resources and prices for maabjerg bioenergy - nye priser og leverancer til maabjerg bioenergy. Article, February URL nye-priser-og-leverancer-til-maabjerg-bioenergy/. [7] Maabjerg bioenergy yearbook Årsrapport Report, may URL http: //maabjerg-bioenergy.dk/om-maabjerg-bioenergy/aarsrapport/. [8] Alternative fuels for waste trucks - alternative drivmidler til renovationsbiler, April [9] Optimisation value chains for biogas production in denamrk. Web page, URL http: //biochain.dk/. Biochain project homepage. [10] Illegal tax exemption for biogas - biogas ulovligt fritaget for afgifter. Web article, June URL Gas/ article ece. 77

96 78 BIBLIOGRAPHY [11] Suspicion of illegal support for biogas - mistanke om ulovlig støtte til biogas. Web article, June URL mistanke-om-ulovlig-stoette-til-biogas [12] Updatet note about discount rate and life time - opdateret tillægsblad om kalkulationsrente, levetid og reference til vejledning i samfundsøkonomiske analyser på energiområdet. Web note, June [13] Discount rate and tax distortion loss kalkulationsrenten og skatteforvridning. Web page, July URL pl?vaerkid=3541&reprid=0&filid=29&iarkiv=1. [14] Trine Krogh Boomsma and Peter Meibom. Mathemaical programming model for energy system analysis: An introduction. Note for Modelling and analysis of sustainable Energy systems - DTU, [15] Declaration about CO2 fee from sertant energy products. Act of co2 fee for specific energy products, lbk nr bekendtgørelse af lov om kuldioxidafgift af visse energiprodukter - lbk nr 321,. Web page, URL r0710.aspx?id=133858#b2. [16] Manual for socio economic evaluations in the energy field - Vejledning i samfundsøkonomiske analyser på energiområdet. Energistyrelsen, juli 2007 edition, April [17] Preconditions for socio economic evaluations in the energy field - Forudsætninger for samfundsøkonomiske analyser på energiområdet, april Energistyrelsen, April [18] Technology Data for Energy Plants - Generation of Electricity and District Heating, Energy Storage and Enegy Carrier Generation and Conversion. Energistyrelsen - Energinet.dk, May [19] Brian H. Jacobsen, Frederik M. Laugesen, Alex Dubgarrd, and Mikkel Bojesen. Biogas production in denmark evaluations of operation and socio economics - biogasproduktion i danmark- vurderinger af drifts og samfundsøkonomi. Report 220, June [20] Christian Juel Jørgensen. Field prices varies from 118 to 270 thousand kr pe ha- jordbpriser svinger fra til kr/ha. Net article, January URL /1/10/Jordprisersvingerfra118000til270000krha.htm. [21] Uffe Jørgensen. Options for increased nitrogen efficiency in the field and a reduction of nitrogen loss - muligheder for forbedret kvælstofudnyttelse i marken og for reduktion af kvælstoftab- faglig udredning i forbindelse med forberedelsen af vandmiljøplan iii. Report 103, May [22] Fakri Koleilat, Jesper markussen, Morten Munk Strægaard, Ninna Pirchert, and Hans Krabbe. Maabjerg bioenergy paper - maabjerg bioenegy profilavis. Plant brochure, June [23] Manual of instruments for reduction of Nitrogen and Phosphor leaching by EU Life project AG-WAPLAN - Manual for virkemidler til reduktion af kvælstof- og fosforudvaskningen udarbejdet i EU- LIFE projektet AG-WAPLAN. Life Agwaplan. [24] Allan Lunde. Visit at maabjerg bioenergy. See appendix A.2, April 2013.

97 BIBLIOGRAPHY 79 [25] F. Møller and L. Martinsen. Socio-economic evaluation of selected biogas technologies. Technical report, [26] H.B Møller, I Lund, and S.G Sommer. Solid liquid separation of livestock slurry: efficiency and cost. Bioresource Technology, 74(3): , ISSN doi: http: //dx.doi.org/ /s (00)00016-x. URL com/science/article/pii/s x. [27] H.B. Møller, S.G. Sommer, and B.K. Ahring. Separation efficiency and particle size distribution in relation to manure type and storage conditions. Bioresource Technology, 85 (2): , ISSN doi: URL S [28] Poul Erik Morthorst, Marie Münste, and Lena Kitzing. Feasibility studies and assessment of energy systems - slides session 4 main. Course slides for DTU course Feasibility studies and Assessment of energy systems, September [29] Bruno Sander Nielsen. Biogas production - are wee ready? - biogasproduktion - er vi klar? January URL seminarer/130115/materiale/1155,%20bruno%20sander.pdf. [30] Jens Christian Pedersen. Visit at farm. See appendix A.3, April [31] Plancher. Maabjerg bioenergy presentation posters - maabjerg bioenegy præsentations plancher. posters, August URL media/1344/mbe_plancher.pdf. [32] K. Tybirk (red.). Handbook for establishing biogas plants - Kogebog for etablering af biogas anlæg Agro Business park/innovationsnetværket for biomasse, 2012 edition, September [33] Teodorita Al Seadi, Dominik Rutz, Hainz Prassl, Michael Köttner, Tobias Finsterwalder, Silke Volk, and Rainer Janssen. Biogas Handbook. University of southern Denmark, ISBN [34] Jørgen Sørensen. Visit at vinderup chp. See appendix A.1, April [35] Transport economical prices v.13. Economic transport unit prices - transportøkonomiske enhedspriser v. 13. Excel spreadsheet, July URL Transportoekonomiske-Enhedspriser.

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101 APPENDIX A Visit in Holstebro 83

102 84 APPENDIX A. VISIT IN HOLSTEBRO A.1 Vinderup visit 1 Besøg fredag d. 22 marts i Holstebro Besøg fredag d. 22 marts i Holstebro 1. Besøg Vinderup Kraftvarmeværk Kontaktperson Driftsleder Jørgen Sørensen ( / ) Teknik Vinderup Kraftvarmeværk er et gammelt kulfyret kraftværk, der i 1995 blev lavet om til et naturgasfyret kraftvarmeværk. Værket består nu af to naturgasfyrede varmekedler, som kun benyttes til spidslast og som maks må benyttes 500 timer om året. Herudover har værket to naturgasmotorer til kraftvarme produktion, hver med en kapacictet på 2.7 MW el produktion. Til sidst har værket én biogasmotor med en kapacitet på 3 MW elektricitet (4,2-4,3 MW varme uden varmepumpe og 4,5-4,6 MW med) - fordelt med en elvirkningsgrad på omkring 43 % og varme oppe imod % (Total virkningsgrad er 100% uden varmepumpe og 104 % med). I motoren tilsættes biogassen op imod 2% ren naturgas, for at sikre en ordentlig antænding omkring tændrøret. Biogassen leveret fra Måbjerg Bioenergi har et metanindhold på mellem 49 og 59%. Akslen fra motoren driver generatoren, der producerer elektricitet. Udstødningsgassen med en temperatur på omkring C, varmeveksles til fjernvarme igennem 3 lodrette varmevekslere. Den resterende varme veksles til vand ved omkring 25 C og boostes via en varmepumpe til 50 C, inden den sendes tilbage til motoren. Varmepumpen har en størrelse på ca. 650 kw og en COP/virkningsgrad på op imod 5.2. Hermed skulle den samlede virkningsgrad nå op på 104%. Det er stort set ikke muligt at regulere motoren, det er enten fuld kraft eller sluk. Motoren og biogasledningen til Vinderup, er dimensioneret til 1000 m 3 i timen. Herudover er værket udstyret med et varmelager i form af en akkumuleringstank på m 3, svarende til to et halvt døgns forbrug, om sommeren. I vinterhalvåret kommer omkring halvdelen af varmeproduktionen fra Biogas, mens den årligt forventes at benyttes til mellem 70 og 80% af varmeproduktion. Biogassen fra Måbjerg bioenergi er ikke opgraderet, hvilket medvirker til det vekslende metanindhold i gassen. Biogasmotoren er indstillet til at kunne håndtere de vekslende grader af metanindhold, så dette giver begrænsede problemer. Selvom gassen renses for svovl på Måbjerg Bioenergi er der også en skiftende grad af svovlindhold, så Vinderup Kraftvarmeværk har investeret i en måler, der måler mængden af svovl. Hvis svovlindholdet overstiger et givet niveau slås motoren fra, eftersom for meget svovl i røgen kan erodere røggasrenseren, hvilket ikke indgår i den serviceaftale, som Vinderup har på røgrenseren. Da Måbjerg Bioenergi er et nyt værk, har det særligt i den begyndende fase været nødvendigt at stoppe forbruget af biogas. Økonomi Økonomisk set er Vinderup forpligtiget til at aftage biogas til deres varmeproduktion, med den kapacitet de har til rådighed. Gasaftalen med vinderup strækker sig over 20 år, med ¼ af prisen fastsat og ¾ af prisen

103 2 Besøg fredag d. 22 marts i Holstebro bundet til nettoprisindekset og dermed den generelle økonomiske udvikling. Vinderup ville ikke acceptere at lade prisen binde til naturgasprisen, da alternativet til biogas for dem ikke var naturgas, men solvarme. Biogasledningen er Måbjerg Bioenergis investering, mens Vinderup har haft en investering på 20 millioner, fordelt på bygning, biogasmotor, varmevekslere, pumper og frekvensomformere og varmepumpe. Biogasmotor - 8 millioner kr. Varmevekslere 5 millioner kr. El entreprise (pumper og frekvensomformer) 3 millioner kr. Varmepumpe 2,5 millioner Bygninger - 2,5 millioner kr. For at acceptere risikoen ved investeringen, vurderede Vinderup at deres varmeforbrugere skulle opnå en % besparelse på varmeregningen. Kunderne betaler nu 405 kr./mwh. Vinderup ville nok selv have været villige til at betale for ledningen, hvis det havde været nødvendigt. Det er langt billigere for dem at modtage biogas på den lokale ledning, end hvis de skulle have modtaget opgraderet biogas via naturgasnettet, som det vurderes meget dyrt at transportere gas på i sammenligning med den lokale ledning. Bygningerne afskrives over 15 år. For det resterende regnes med renovering/ reinvestering efter 8 år. (?) Generelt set har Vinderup kraftvarmeværk været utroligt glade for aftalen med Måbjerg bioenergi. Kunderne sparer omkring 15 % i varmepriser, og med den aktuelle udvikling i naturgaspriserne, er de rigtig tilfredse. I 2018 forsvinder grundbeløbet til elproduktion på de decentrale kraftvarmeværker. Vinderup overvejer i den forbindelse at lukke produktionen på de to naturgasmotorer, der leverer spids- og reservelast. De to motorer har behov for en reinvestering på ½ mio. kr. hver i nær fremtid. Alternativt vil Vinderup (da biomasse ikke er tilladt) producere varme via enten: - en ny blandgasmotor (naturgas og biogas) - en ny elektrisk varmepumpe - et nyt solvarmeanlæg

104 86 APPENDIX A. VISIT IN HOLSTEBRO A.2 Maabjerg visit 3 Besøg fredag d. 22 marts i Holstebro 2. Besøg Måbjerg bioenergi Kontaktperson Allan Lunde (Vestforsyning) Baggrund & Idé Måbjerg Bioenergi har været et projekt der har været undervejs fra Projektet har baggrund i de store mængder af nærringstoffer i de lokale vandløb, søer og fjorde, som truede den lokale husdyrsproduktion med krav om tilkøb af mere jord pr. husdyrenhed. For at kunne opretopholde den daværende produktion af dyr i en tid med stigende jordpriser, var det nødvendigt at gøre noget. Her blev en del af løsningen, at afgasse og separere gyllen i en våd og en tør fraktion. Da phosphoren blandt andet binder sig til tørstoffet i gyllen, har det været muligt at fjerne phosphoren, og forhindre at den kommer tilbage på marken. Anlægget er dermed ikke tænkt som et energianlæg, men mere som et renseanlæg for den lokale gylle. Denne tankegang betyder også at afgasningen af gyllen er en ydelse som landmændene betaler for (de økonomiske forhold kommenteres længere nede). Anlægget blev først færdigbygget i foråret 2012, og er først rigtig taget i drift i juni I starten var der stor modstand blandt naboer, men efter i små hold at have vist dem det daværende næsten lugtfri rensningsanlæg med biogasproduktion, der lå tæt på centrum af Holstebro, skiftede de holdning. Da anlægget ikke har været i jævn drift så længe, findes der ikke samlede driftsdata, som strækker sig over et helt år. På anlægget forventer de at modtage gylle, energiafgrøder (kartoffel pulp), biologisk affald (valle), og slam fra det lokale rensningsanlæg. De modtager gylle fra op til 20 km væk, for at få flest mulige minkfarme med. Ressource Mængde Gylle tons Energiafgrøder (kartoffelpulp) tons Biologisk affald (15-16/7 % TS) tons/ tons (valle) Slamlinje(3.5% TS) tons Total tons Dette skulle resultere i en produktion af biogas og fiberdel, som med den planlagte brug resulterer i følgende energiproduktion MWh Varme Elektricitet Biogas Fiber I alt Opdeling og ejerskab Ejerforholdet for anlægget var oprindeligt tænkt sådan (2003), at landmændene sammen med Måbjerg BioEnergi og evt andre leverandører skulle sidde på 51% af firmaet. Herudover skulle Vestforsyning varme have 32 % og Nomi (affaldsselskab) og Elsam 9 % hver.

105 4 Besøg fredag d. 22 marts i Holstebro I 2009 blev denne struktur ændret således at Måbjerg Bioenergi blev opdelt i 2 store dele; MBE drift og MBE A.M.B.A. MBE Drift A/s ejer og driver produktionsanlægget, ledningsanlægget til gylle og biogas, slamledninger og varme og elledninger. Ejerforholdene i Drift er opdelt så Vestforsyning Varme A/S ejer 71.4 % (5/7) og Struer fjernvarme A/S ejer 28.6 % (2/7). Ændringen i ejerforholdet skyldes at biogasanlægget kunne opnå et bedre lån fra Kommune Kredit, hvis det var ejet af de kommunale forsyningsselskaber, uden at beløbet gik fra kommunernes låneramme. MBE leverandørforeningen A.M.B.A. står for at levere husdyrgødningen og anden biomasse til produktionsanlægget. De har indtil videre ansvaret for logistikken for både leverancer fra og til de enkelte landmænd og ejer de 5 lastbiler. A.M.B.A. er ejet af de mellem 140 og 150 landmænd som leverer til Måbjerg Bioenergi. Anlægget Ressourcerne fra landet ankommer til Måbjerg BioEnergi med lastvogn. Hver lastvogn indeholder 32 tons gylle, og der ankommer ca. 50 læs dagligt. Inden lastbilerne kører ud til landmændene, fyldes lastvognen først med afgasset gylle således, at lastbilerne ikke kører nogen tomme ture. Alle indkomne læs analyseres for tørstofindhold. Når gyllen leveres fra lastvognen, sendes den til en stor blandetank med et volumen på 2000 m 3, inden den sendes til forlageret(størrelse?). Her lagres gyllen i store siloer, inden den behandles. Når den sendes til reaktorne, sendes den først via rørledninger til hygiejnisering. Hygiejniseringen af ressourcerne på den grønne linje foregår i portioner (batch) af 60 ton, med en opholdstid på en time og en temperatur på 70 C. Herefter sendes den videre ind i selve reaktortankene, der hver har en kapacitet på 8000m 3 (?) (ikke batch). I forbindelse med hygiejniseringen og reaktortankene, varmeveksles gyllen således at varmeforbruget minimeres. Anlægget benyttes som et mesofilt anlæg, det vil sige med en reaktortemperatur på 37 C. Dette betyder også at anlægget har en hydraulisk opholdstid, dvs. tiden gyllen er i reaktortankene, på 23 dage, i reaktorne. Da minkgylle har et højt indhold af ammonium, der kan virke inhiberende på biogasproduktionen, har det været nødvendigt at køre anlægget mesofilt, da biogasprocessen er mindre følsom ved lave temperaturer. Der har været forventet problemer med fiskeben fra minkgyllen i håndteringen, men det er ikke set endnu. I stedet er der fundet store mængder sand fra kvæggyllen, som fylder forlagrene. Fra reaktorne sendes gyllen til et efterlager hvori den resterende gas får lov at sive ud, inden gyllen separeres i en tør og en våd fraktion. Biogassen sendes til to gaslagre på m 3 hver, svarende til 10 timers

106 5 Besøg fredag d. 22 marts i Holstebro produktion. Den våde fraktion opbevares i siloer, inden den returneres til landmændene. Fiberfraktionen sendes med lastvognstrailere til naboanlægget Måbjergværket, hvor det var meningen at fiberen skulle afbrændes. Det sker dog ikke i øjeblikket, hvorfor Måbjergværket afsætter fiberdelen andetsteds. Med den oprindelige konstalation, skulle fosfat kunnet hentes fra de afbrændte fibre og genanvendes i landbruget. Slamlinjen fungerer lidt anderledes. Således hygiejniseres gyllen ikke inden den sendes til reaktortanken. Hygiejniseringen sker først efter afgasningen, ved separeringen ved tilsætning af læsket kalk. Opdelingen af anlægget i en grøn linje og en slamlinje skyldes ARLAGården, der har et regelsæt, som stiller store krav til sporbarheden af den gødning, som benyttes til foderproduktionen til malkekvæg, der leverer mælk til ARLA. Hygiejniseringen af den grønne linje sker også for at fjerne eventuelle bakterier og lignende fra gyllen. Vallen, der leveres fra ARLAs flødeoste mejeri i Holstebro, opkoncentreres, inden den blandes i den grønne linje. Således modtgaer Måbjerg BioEnergi årligt omkring tons valle med et tørstofindhold på ca. 7-8 %. Dette opkoncentreres til tons med et tørstofindhold på op imod 15-16%. Samlet kan anlægget (reaktorne?) indeholde tons, og der er et ressource anlæg svarende til ca. 3 dages produktion. Anlægget kører konstant, men det vurderes at ca. 10% af inputtet kan flyttes, så produktionen bliver mindre om sommeren og større om vinteren. Anlægget producerer samlet ca m 3 biogas i timen eller 18 mio. m3 per år, hvoraf Vinderup, der har første prioritet, modtager ca. 1/3. Måbjergværket (der bruger biogassen på deres overheder) har anden prioritet, og Maabjerg BioEnergi selv 3. prioritet. Anlægget er udstyret med en flare, således at evt. overskydende gas produktion kan brændes af. Brænderen har en kapacitet på 3.000m 3 biogas i timen. Endelig renses m3 luft i timen (?). Der er undertryk i indsamlingshallerne og der suges luft fra fibercontainerne, for at undgå lugtgener, hvilket har været af høj prioritet fra starten af projektet. Laboranten (som man normalt ikke har på et biogasanlæg) analyserer løbende biogaspotentialet i nye biomasser. Et forsøg tager ca. 30 dage, men firmaet (BioProcess) arbejder på forsøg med bestemmelse af biogaspotentiale efter 2 dages ophold. Biogasmotor Som ved Vinderup kraftvarmeværk er også Måbjerg Bioenergi udstyret med biogasmotorer, dog to hos Måbjerg BioEnergi. Disse er lidt mindre med en størrelse på 1.4 MW elproduktion, og en el virkningsgrad på 43%. Varmeproduktionen har en virkningsgrad på ca. 47 %, det vil sige en samlet virkningsgrad på 90%. En del af varmen benyttes i anlægget, og den resterende afsættes som fjernvarme, ligesom den producerede elektricitet afsættes til nettet. Når motorerne kører, kører de i minimum 10 timer. Fjernvarmen afsættes til samme net som Måbjergværkets varmeproduktion, hvilket måske kan give nogle konflikter om hvem der har ret til at afsætte først om sommeren, da Måbjergværket primært forbrænder affald. Transport af gyllen Gyllen transporteres først og fremmest via særligt fremstillede gylletankvogne, der både transporterer den nye

107 6 Besøg fredag d. 22 marts i Holstebro gylle til biogasanlægget og den afgassede gylle tilbage til landmændene. Leverandørforeningen ejer 5 lastbiler, som hvert kører gennemsnitligt 10 gange dagligt. Systemet med på- og aflæsning af gylle, er designet, så det kan foregå let og elegant med så få lugt- og svinegener muligt. Der er foreløbigt lavet en taxameterordning hos landmændene, hvor den enkelte betaler for antal minutter tankbilen er på gården. Dette er bl.a. for at skabe et incitament for landmanden til at lave de nødvendige investeringer, der skal til for gøre transporten af gylle mellem landmænd og biogasanlægget så hurtigt og rent, som muligt. Bilerne er hvide, ligner mælketankbiler og rengøres hele tiden for at give et godt visuelt indtryk af transporten. Pumpning af gylle Som et led i projektet var det intentionen at pumpe en del af gyllen fra landdistrikterne, og ind til værket. I den forbindelse er der anlagt en rørledning fra Måbjerg BioEnergi og til en lokation et stykke uden for Holstebro, med mulighed for udvidelse. Denne del af projektet har modtaget en del støtte fra EU, og Måbjerg BioEnergi er forpligtet til at udvide med endnu en omladestation. Der er endnu ikke foretaget forsøg med at pumpe rå gylle ind til anlægget. Kvæggylle kan ikke pumpes alene, og en opblanding må derfor ske først. Der arbejdes blandt andet med pumpekapaciteten langs rørledningen. Den oprindelige plan indeholdt pumper for hver 2-3 km, hvilket nu er forøget til pumper for hver kilometer. Hvis pumpestationen kommer op og køre med fuld kapacitet, vil den kunne mindske transporten ind til anlægget med max 30 %. Økonomien antages ikke at blive forbedret, da mest tid går med omlagring af gyllen, hvilket stadig skal ske. Fiberfraktion til Måbjergværket Den oprindelige plan og kontrakt med Måbjergværket, går ud på at Måbjergværket skal aftage fiberfraktionen fra den grønne linje og brænde den af til varme produktion. Asken fra afbrændingen vil efterfølgende indeholde næsten ren fosfor, som vil kunne afsættes til gødningsproduktion. Anlægget ved Måbjergværket er dog ikke blevet opdateret, og de fornødne tilladelser indhentet, således at fiberen kan afbrændes. Værket (Dong) er dog kontraktligt forpligtigt til at aftage fiberen i mindst 9 måneder om året. De aftager den derfor også nu, men der betales efterfølgende penge til landmænd i Jylland, for at de aftager den til gødningsformål. I de resterende 3 måneder separeres fiberen ikke fra den afgassede gylle. Hvilket viser sig at være udmærket for landmændene, der ellers ikke ville have nok fosfor i den afgassede gylle. Dette skyldes blandt andet at fosfor indholdet i gyllen er faldet pga. ændringer i foderet (tilsætning af fystase) Økonomi Måbjerg BioEnergi har 8 ansatte på værket; en laborant, en driftsleder, 2 elektrikkere, 2 smede, en sekretær og en leverance logistik ansvarlig. Alle de aftaler som er indgået i forbindelse med leverancer fra landmænd og aftagning af biogas, er indgået over en 20 årig periode. Dette er gjort for at minimere risici og sikre en stabil økonomi for anlægget. Anlægget var oprindeligt udbudt som total entreprise, men blev efterfølgende udbudt i 13 delentrepriser.

108 7 Besøg fredag d. 22 marts i Holstebro Pengene til projektet er lånt hos Kommune Kredit, da projektet kan karakteriseres som et varmeforsyningsprojekt. Dette er med til at sikre en fast lav rente, hvilket har været afgørende for at give landmændene mulighed for at deltage i projektet. Måbjerg BioEnergi kan ikke anses for et direkte profitabelt projekt, og med den nuværende udformning, synes biogasanlægget at tage den største risiko af de tre parter.

109 A.3. FARM VISIT 91 A.3 Farm visit 8 Besøg fredag d. 22 marts i Holstebro 3. Besøg Leverandørforeningen Kontaktperson Svineavler og bestyrelsesmedlem Maabjerg BioEnergy AMBA, Jens Christian Pedersen Generelt Gårdene der leverer gylle til Måbjerg BioEnergi har en mindre gylletank, der indeholder den frisk producerede gylle. Når denne tank er fuld, sendes der besked til Måbjerg BioEnergi, via programmet BioLink. Derved samles gyllen ind så frisk som muligt. En lastbil tømmer den fyldte tank, efter at have leveret den medbragte afgassede gylle til landmanden. Denne opbevares i størrere klassiske gylletanke. Ved den besøgte gård, var den nye mindre tank indbygget i en eksisterende gylletank. For at blive en del af gårdene, som leverer til Måbjerg BioEnergi, betales et optagelsesgebyr på 525 kr pr dyreenhed på gården. Én dyreenhed svarer til 36 slagtesvin. Et slagtesvin fedes op fra 30 kg til 105 kg på ca. 12 uger. Landmændene betaler i dag gennemsnitligt 20 kr./t gylle for håndtering af gyllen og modtager ca. 5 kr./t for gasværdien af gyllen. Deltagelse forudsætter at bedriften maksimalt ligger hhv. 20 eller 15 km væk fra biogasanlægget. Herefter varierer betaling ikke med afstand til anlæg. Det vurderes fra Jens Christians side, at betalingen fint opvejes af den øgede gødningsværdi og mulighed for at udbringe mere gødning per hektar. Derudover er det en fordel i forbindelse med naboskab, at lugten er substantielt reduceret i forhold til udbringning af rågylle. Aftagelsesaftalen med Måbjerg BioEnergi indeholder udover et afgasnings- og separationsgebyr, et tørstofgebyr, hvis ikke gyllen har et vist tørstofindhold. Omvendt modtages penge, hvis tørstof indholdet overstiger denne standardgylle værdi. Dette har dog givet økonomiske problemer for nogle af de deltagende landmænd (der har skullet betale omkring kr/t og som nu skylder flere hundrede tusinde kroner), da de har haft svært ved at leve op til tørstofskravene. Dette har ført til at aftalen imellem landmændene og Måbjerg BioEnergi, dvs. mellem A.M.B.A. og Drift formentlig ændres. Dette vil sandsynligvis betyde at tørstofindholdet i standardgyllen bliver sat ned, samtidig med at gebyrerne for at levere gylle med for lavt tørstofindhold bliver sænket. Det forventes at afgiften på 9 kr/enhed/m3 per % tørstof sænkes til 4kr. Derudover vil Maabjerg BioEnergi Drift sandsynligvis overtage lastbiler og transport. Uofficielt vil dette formentlig betyde en nedsat indtjening for Måbjerg BioEnergi Drift årligt på omkring 6 millioner kr. Ændringen vil for slagtesvin eksempelvis formodentlig betyde at standard tørstfindholds kravet vil falde fra 6.8% til 5.3% og at gennemsnitsudgiften for svineavleren kan forventes at blive 0 kr. per ton. Det tidligere nævnte taxametergebyr for ophold på bedrift anses også for at forsvinde med den nye ordning. Phosphor Det har vist sig at fosfor indholdet i den afgassede gylle, er så lavt at der kan opstå et fosfor underskud, på markerne. Selv når gårdene i 3 måneder om året modtager gylle som ikke er separeret, er der tendenser til at fosforindholdet i jorden er for lavt. Dette er dog noget som længere tid med jævn drift, vil kunne afklare. Økonomi For landmændene koster det altså penge at blive en del af leverandørene til Måbjerg BioEnergi (Samlet set 11 mio. kr.). Herudover kræves også nogle investeringer på de enkelte gårde, for at kunne levere til

110 9 Besøg fredag d. 22 marts i Holstebro Måbjerg BioEnergi, investeringer i omejnen af omkring kr. (Samlet set vurderes investeringer på bedrifterne at løbe op på 25 mio. kr.). Herudover skal der betales for at få separeret gyllen hos Måbjerg- en udgift der dog ser ud til at falde lidt med den nye aftale, ifølge Jens Christian Pedersen. Alligevel skulle det være investeringer, som er givet godt ud, og som godt kan betale sig, i form af den afgassede gylle de får tilbage. Udover det ændrede næringsindhold, lugter gyllen også langt mindre, samtidig med at den hurtigere trænger ned i jorden, i kraft af den tyndere konsistens. Jens Christian Pedersen har desuden investeret i låg til sine gylletanke med afgasset gylle. Det giver en yderligere reduktion i lugtgenerne og transportomkostningerne ved udbringning af gylle på markerne tidligere indeholdt denne gylle også regnvand. Forskellige dele af værdikæden har fokus på forskellige karakteristika ved gyllen. For landmændene er det mængden af kvælstof i den afgassede gylle vigtig. Derudover er det vigtigt at få nedbragt mængden af fosfor, som er bundet til fiberfraktionen. Kvægfarmerne har brug for kali til dyrkning af foder til kvæget (mere end de får tilbage nu). I den forbindelse har kali kompensering til kvægfarmerne været diskuteret. For biogasanlægget er det vigtigt at tørstofindholdet er højt (dog max 10 % i anlægget). Derudover kan for stort indhold af ammonium give problemer på biogasanlægget. Endelig er indhold af sand til skade for anlæg og pumper, samt optager kapacitet i lagringstankene. Medlemskab i Maabjerg Bioenergi giver pligt til at levere en vis mængde gylle i 20 år. Pligten følger bedriften og gælder også hvis den skifter ejer, men ikke hvis denne går konkurs. Medlemskab sikrer også en ret til rensning af gyllen, hvilket på sigt kan blive en økonomisk gevinst som måske vil kunne handles, når anlægget fx lukker for yderligere medlemmer og der måske stilles yderligere miljøkrav vedrørende udbringning af gylle på landbrugsjord. Der er plads til 10% mere gylle på anlægget for indeværende.

111 APPENDIX B Prices 93

112 94 APPENDIX B. PRICES B.1 Prices on electriciy, fuel and district heat, [4] El Fjernvarme 2011 prisniveau Naturgas Diesel Nord Pool uvægtet Nord Pool forbrugsvægtet An virksomhed An forbruger CO2 kvote pris [kr/gj] [kr/gj] [kr/mwh] [kr/mwh] [kr/mwh] [kr/gj] Kr/Ton ,6 140, ,0 46, ,7 139, ,4 62, ,8 138, ,8 77, ,4 136, ,1 91, ,7 136, ,3 105, ,0 137, ,9 119, ,3 137, ,3 132, ,2 138, ,5 147, ,1 139, ,2 162, ,9 140, ,0 167, ,7 141, ,9 173, ,5 142, ,6 178, ,3 143, ,5 183, ,1 144, ,1 189, ,8 145, ,6 194, ,4 146, ,5 200, ,1 147, ,1 205, ,8 147, ,5 210, ,5 148, ,6 216, ,9 149, ,9 221,7

113 APPENDIX C Subsidy 95

114 96 APPENDIX C. SUBSIDY C.1 Subsidy rules, [3] Tilskud til anvendelse af biogas i 2012 ifølge lovforslag vedtaget den 8. juni Figuren viser støtten til biogas i forenklet form. For de juridisk gældende regler henvises til den til en hver tid gældende lovgivning, som kan findes på Udover nedenfor viste tilskud er varmeproduktion fritaget for energi - og CO2-afgift. Satserne aftrappes som beskrevet nærmere beskrevet i loven. Indfyring af biogas i almindelig kedel, varmen er til varme eller proces Biogas tilskud: 0 kr KEDEL tilskud: kr/gj varme til proces varme til varme tilskud: kr/gj Biogas til transport uden naturgasnet Biogas tilskud: 0 kr Tankstation tilskud: kr /GJ transport Indfyring af biogas i kraftvarmeanlæg. Varme anvendes til rumvarme eller proces og el sælges til nettet. Biogas fast afregning på 79,3 tilskud: 0 kr tilskud: 0 kr + tillæg på øre/kwh eller 43, øre/kwh varme GASTURBINE GENERATOR El til nettet Opgradering af biogas til naturgasnettet tilskud: 0 kr tilskud: tilskud: 0 kr kr/gj uanset hvad Biogas OPGRADERING Naturgasnet

115 APPENDIX D Technology data & Emissions 97

116 98 APPENDIX D. TECHNOLOGY DATA & EMISSIONS D.1 Truck technologies Table D.1: General Work assumptions, trucks [19] & [24] Work hours per day 8 [hr] Yearly workdays per truck 300 [days] Constant time use 0,3334 [hr/load] Variable time use 0,02 [hr/km] Average velocity 50 [km/hr] Table D.2: Truck data Diesel truck, [19],[35] Dual fuel truck, [8] Investment cost 1,700,000 2,000,000 [kr] Fuel use [l/km] Maintenance cost [kr/truck/year] Size [m 3 ] Related investment - 3,000, [kr] Salary [kr/hr] Efficiency compared to diesel [-] bg use [m 3 /km]

117 D.2. HEAT SUPPLY TECHNOLOGIES 99 D.2 Heat supply technologies Table D.3: Heat supply technologies Biogasengine Boiler Biogas engine Biogas turbine [18], [34] [18] with heat pump, [18], [34] [18] Minimum engine size - electricity [MW] Maximum engine size - electricity [MW] Electricity efficiency [-] Total efficiency [-] Heat efficiency [-] Biogas use per hour - min [m 3 /hr] Biogas use per hour - max [m 3 /hr] Minimum runtime [hr] Investment price [kr/mw] O&M cost (variable) [kr/mwh] O&M cost (constant) [kr/mw] Maximum run time per year [hr]

118 100 APPENDIX D. TECHNOLOGY DATA & EMISSIONS D.3 Upgrade technologies Table D.4: Water scrubber upgrade technology [18] small Large Biogas use per hr min [m 3 /hr] Biogas use per hr max [m 3 /hr] minimum runtime [hr] maksimum runtime [hr] Plant unvestment [kr/m 3 /hr] O&M cost 0,3 0,3 [kr/m 3 ] Power use for upgrade 0,03 0,03 [kwh/m 3 ] Upgrade efficiency 0,98 0,98 [-]

119 D.4. EMISSIONS 101 D.4 Emissions Table D.5: Transport emission values Transport Diesel Biogas [g/l] [g/m 3 NG] CO CH N 2 O SO NO x Particles CO Sources [2] [5] Regarding the emissions from the biogas gas turbine, it has been assumed that the same relation exist emission wise, between a biogas engine and a biogas gas turbine, as for a natural gas engine and a natural gas turbine. Table D.6: Biogas emission values Heat & power production Biogas engine Biogas boiler Biogas Gasturbine Biogas [g/gj] [g/gj] [g/gj] CO CH N 2 O SO No x Particles CO Sources [2] [2] Own calculations Table D.7: Natural gas emission values Heat & power production Natural gas engine Natural gas - gasturbine Natural gas boiler Natural gas [g/gj] [g/gj] [g/gj] CO CH N 2 O SO No x Particles CO Sources [2] [2] [2]

120 102 APPENDIX D. TECHNOLOGY DATA & EMISSIONS Table D.8: Other emission values Heat & power production Cole steam turbine Waste steam turbine Electricity - average Other [g/gj] [g/gj] [kg/mwh] CO CH N 2 O SO No x Particles CO Sources [2] [2] [4]

121 APPENDIX E Preproject 103

122 Preleminary Study From Waste To Gas - Conceptual model for value chain in biogas production Lau Linnet Andersen - s Supervisors: Nina Juul & Marie Mu nster DTU Management Engineering Risø Campus February

123 Contents 1 Introduction 1 2 Resources Manure Deep litter Industrial waste Slurry Household waste Molasses Energy crops Biogas potential Biogas production Production at Måbjerg Energy Amounts Production in general Products Biogas Heat & power production Fuel cells Biogas upgrading Digestate Fertilizer Heat Economy & Overview Economy Manure Industrial waste Slurry Production Products Biogas Power & heat Upgrade Fertilizer Fiber Conceptual model i

124 6 Model Description Worksheets Resources Production & cost Results Assumptions Data Reference Validation and further Conclusion 15 Bibliography 16 A Diagram 17 B Diagram - possibilities 19 ii

125 Chapter 1 Introduction The energy plan Grøn Vækst from June 2009, presented by the Danish government, has a target of using up against 40 to 50% manure for energy production in In this strategy, biogas is set to play a very important role(danish-government [2009]). This has increased the focus on biogas and biogas production in the resent years. When using the manure from the farmers to produce biogas by anaerobic digestion, the waste products are converted into renewable energy, with a natural fertilizer as a co product. In addition, the produced fertilizer contains a lower amount of organic matter which increases the efficiency of the fertilizer, and decreases the emission of green house gasses. Besides the effect of lowering the emission of green house gases and the use of fossil fuels, the depletion of nutrients is also lowered, which is also important as especially the phosphor has turned out to be a scarce resource. The scope of the project presented in this report, is to map the different processes and related treatment of manure resources and waste products to biogas production. Based on an existing plant, the Måbjerg Bioenergy, inputs in terms of waste and resources like manure and whey will be identified. Further, end use products and possible applications will be identified. Hence an overview of the production and possibilities within biogas production will be obtained, and presented. Also a simple conceptual model of the plant, with some of the possible applications is build and presented. Also key figures within the related economy will be identified, and used to obtain an overview of the value chain in biogas production. Together with the simple idea about biogas and biogas potential, this has lead to a simple excel model, which is also presented. The report contains initially a short introduction to different types of relevant resources and waste products, with their pros and cons. This is followed by a chapter describing the technology used to produce biogas, before a chapter concerning the products from the biogas production. After this is a short chapter describing the economy related to the biogas production and an overview of the production and the conceptual model, followed by a chapter describing the excel model. Finally is a short conclusion summing up the most relevant conclusions from the report. 1

126 Chapter 2 Resources As mentioned briefly in the introduction the Danish government has a target of increasing the amount of manure used for energy production to up against 50%. Today it is expected that only about 5% is used for energy production. In numbers that means that in 2012 about 2 million tons of manure were used for biogas production, resulting in a gas production of about 4 PJ(Energistyrelsen [2013]). The primary resource used for biogas production in Denmark, is manure. The manure is produced at the farms by both pigs, cattle, mink, poultry and so. This resource is both cheap and relatively easy to handle, as it can be transported either in trucks or in a pipeline. The amount of gas produced from this type of resource is though limited, as the biogas potential is not very high, see Figure 2.1. The limited potential in the manure induces the biogas producing plants to boost their gas production with something else. This is often energy crops or industrial waste. This chapter contains a short introduction to different often used resources and their pros and cons. Figure 2.1: [2013]). Distribution of biomass treated versus origin of biogas produced(agrotech 2.1 Manure Manure is the base resource in the Danish biogas production in terms of weight. the manure comes from both cattle, pigs, mink and poultry. The manure considered here is the wet part without deep litter and to much fiber, as the technology or technique is not ready for biogas production from this, yet. The production of gas from the manure is an important factor to have the local ecosystems working. It helps avoiding discharges of phosphors and other nutrients to the local streams and lakes, when the manure is returned to the fields as fertilizer. An important 2

127 factor when using manure for biogas production, is the percentage of dry matter. The dry matter is the fraction that contains the carbon which transforms to methane, that is the important part of the biogas. A low fraction of dry matter means that you have to use more manure to retain the same production of biogas. In Table 2.1 is shown the amount of dry matter that is usually present in different types of manure. At Måbjerg Energy they uses different types of manure from the surrounding farmers. This is basically from pigs, sows, cattle, and in the season also from mink(lunde [2013]) Deep litter Another type of manure is the deep litter. It is a mix of straw and manure, almost like ensilage. It has a high energy content and a very high biogas potential(wenzel and Syddansk-Universitet [2013]). The problem is that the fiber content in the straw is not very well suited for the biogas production as it tend to plug or jam the systems. However this is a resource which is tested and examined a lot, as the potential is high. 2.2 Industrial waste Industrial waste is also widely used in the production of biogas. It can be used in many forms, but the obvious and often used things could be both guts or gastrointestinal waste from slaughter houses, whey or similar from dairies, or excess fat from different food industries. These types all have the characteristic that they are easy to add to the manure in the production and they all add significantly to the biogas production. Another resource among the industrial waste is fallen livestock. It can basically also add quite a lot to the biogas production. The main problem with the use of fallen livestock, is the risk of contamination which can be spread from the plant. This means that a biogas plant using this resource in their production, must have separate facilities to receive and treat it in. Also strict cleaning practices to avoid contamination and a possible spread to other farms is required. At the Måbjerg Energy plant whey from a local dairy is received daily, and pulp from a local potato industry is used in the season, to boost the biogas production Slurry A widely used resource within the waste category is slurry or sludge. As with the manure the biogas potential in pure slurry is not that great. Contrary to the manure it is not as obvious to boost the production from slurry. This is due to the use of the co products from the gas production. Arlagården, which is a set of standards for the farmers delivering to ARLA, has a high standard for traceability from their products. This means that the co products from the gas production from slurry can not be used as fertilizer in the same way as co products from biogas produced with manure and well defined industrial waste. Slurry is therefore treated in their own facilities and lines at the biogas plants. The produced gas though still has the same quality. At Måbjerg Energy the slurry is used at its own line, with a direct pipeline, sending the slurry to the plant Household waste A possible boost for the biogas produced with slurry could be household waste. The waste could both be sorted organic waste or Renescience treated waste, which is a wet mix of waste dissolved by use of enzymes and heat. As with the slurry, the traceability in such a mix is not very good, why it would be an obvious boost to the slurry. At Måbjerg Energy household waste is not yet used in the production though it is a part of a possible expansion plan for the plant. 3

128 2.2.3 Molasses Another part of the possible expansion of the facilities at Måbjerg, consist of a huge bioethanol plant, producing ethanol from straw. A waste product from this plant would be molasses, and the possibilities in involving this in the biogas production is still investigated. If the tests are successful, that would increase the biogas production considerably. 2.3 Energy crops Another well mentioned resource for biogas production are the energy crops. Energy crops are often maize, beets or similar, in a type of ensilage. The main discussion about this resource is that it is often produced with another purpose. This means that a lot of energy has been put into the growing of the crop, which means that energy produced from this type of resource is no way near energy efficient, or improving the emission of green house gases(wenzel and Syddansk-Universitet [2013]). Energy crops are not used in the production at Måbjerg Energy at the moment. 2.4 Biogas potential The biogas potential in the above mentioned resources vary a lot. In Table 2.1 is presented the biogas potential in the different resources. The numbers origins from a report made by the consulting company Niras for the Danish Energinet.dk (Niras and Energinet.dk [2012]). In the report the numbers are collected from different articles and preferred number. As mentioned, the dry matter content in the manure has a high influence on the biogas potential of the gas, which is why the gas potential here is expressed as m 3 per kg dry matter. In order to ensure a relatively high fraction of dry matter, different things can be done. This primarily relates to the arrangement of the pigsty or cowshed in relation to water trough, separation of urine and so, but also type of fodder has an influence of the dry matter quality. Practically it is not possible to produce the full biogas potential of a resource. The numbers in this table are corrected for this, and therefore relates to the actual possible production. Table 2.1: [2012]). Dry matter (realistic interval) and biogas potential(niras and Energinet.dk Dry matter Biogas potential Type % m 3 biogas per kg dry matter Pigs 4.5 ( ) 0.39 Sows 2.5 ( ) 0.33 Cattle 7.6 ( ) 0.26 Mink 6.5 ( ) 0.47 Whey 10.0 ( ) 0.53 Fat 100.0(-) 1.35 Slurry 4.0 ( ) 0.49 Maize 31.0 ( ) 0.48 Beet 18.0 ( )

129 Chapter 3 Biogas production This chapter provides a small introduction to the processes related to the biogas production. The very simple idea in production of biogas can easily be described with a few steps; Preliminary storage and mixing, Sanitation, Digester, Temporary storage with additional production and Separation of fibers from fertilizer, see Figure 3.1. Figure 3.1: Simple system sketch- number of tanks in each step are not exact. Multiple tanks means at least more than one. 3.1 Production at Måbjerg Energy At Måbjerg Energy manure is send to the plant through a pipeline from the countryside, but also carried to the plant by truck. At the plant it is stored in big silos. In these the manure is mixed with the whey, which is also broad there through a pipeline. In the season, the manure is also mixed with the potato pulp. The slurry is kept in similar but separate silos. From these silos the feedstock is brought to sanitation in batches. In the sanitation tanks the feedstock is heated to 70 C and kept there for an hour, in order to ensure that bacteria is removed. From here the batches is continuously send to the digester tank. In the digester tank the anaerobic digestion takes place, in the absence of oxygen. The digestion is a microbiological decomposition of the organic matter in the feedstock(seadi et al. [2008]). In the digester tank the feedstock is kept in an oxygen free environment at a temperature level between 30 and 60 C which means that the process is a thermophilic process(koleilat et al. [2012]). The temperature level is important in relation to the activity of the culture of bacteria in the digester. Initially it is very important that the temperature level is kept very constant, which means that the temperature must not vary within the given interval but be kept steady at a much more specific given temperature. This temperature though, has not yet been given from the plant. In general it is known that the hotter the process, the faster the gas is produced. Therefore the temperature also relates to the time, the feedstock has to stay in the digester, known as the Hydraulic retention time(seadi 5