Optimum Investment Planning and Operation of Local Hybrid Energy Systems from the End- User s Perspective

Transcription

1 Optimum Investment Planning and Operation of Local Hybrid Energy Systems from the End- User s Perspective Andreas Fleischhacker Energy Economics Group (EEG), TU Wien ENERDAY 2015, Session: Investment Planning , Dresden (DE)

2 Agenda Motivation Methodology Technical Model Investment Model Model s Assumptions Scenario Definition Disaggregated Aggregated Results Disaggregated Results Single-Family-House Business Aggregated Results Conclusion

3 Motivation and Central Question Expansion or open field planning of energy system in local areas requires a multiple energy carrier approach. Consider multiple final energy carriers to determine the optimum energy strategy. Multidimensional synergies: Co- and Trigeneration. Reduce overall CO2 footprint. Increasing utilization of small distributed energy resources for generation of heat and electricity. Which kind of technology or technology mix provides the most economical energy supply of the different consumption types of the considered project areas? Developed within the project: INFRA-PLAN Planning energy-border infrastructure and hybrid networks in urban neighborhood models

4 Methodology to determine economic efficiency from the end user s perspective. Formulated by an opimization problem consisting of: Investment Model objective function = net-presentvalue (NPV) restrictions: economies of scale: maintenance costs investment costs calculation of fuel costs own consumption revenues Technical Model restrictions: supply = demand minimum/maximum power conversion coefficients and efficiency dependence on the weather demand, solar thermal systems, photovoltaic systems decentralized feed-in

5 Technical Model Optimization Problem of one coupling point input energy storages energy conversion output energy storages grid connected demand not grid connected

6 Investment Model (1/2) The optimization problem s objective function (maximum net-present-value ): max bin,q, L i i,y,t i,y,t with CIF iyt,, ( 1 + p) (1 + l ) Y y y 8760 i biniii bini iyt,, (Q i,y,t, y CF Li,y,t i Ι i Ι y= 0 ( 1+ r) t = 1 = bin I + psf(r, p, l, Y)bin ( CIF COF )(Q, L i Ι NPV = = i i i iyt,, iyt,, i,y,t i,y,t i Ι t = 1 ( Q, L ) = Revenues (Q, L ) i,y,t i,y,t i,y, t i,y,t i,y,t COF (Q, L ) = FuelCosts (Q, L ) + Maintenance (Q, L ) i, y, t i,y,t i,y,t i,y, t i,y,t i,y,t i,y, t i,y,t i,y,t ) ) Legend bin... binary variable CF... cash flow CIF... cash inflow COF... cash outflow i... investment possibility I... investment costs p... price development factor r... discount rate l... psf... load development factor (e.g. renovation) price scenario factor y... current year till Y

7 Investment Model (2/2) Expand the investment possibilities by flexible investment time points and taking the lifetime into account. For example for one kind of investment (e.g. a boiler) with an lifetime of 20 years available available available available invest in boiler invest in boiler invest in boiler invest in boiler Investment Matrix: inv iy, = INV including INV into the technical model: Demand = inv L yt, iy, i,y, t i Ι

8 Model s Assumptions investment possibilities: electrical grid district heating system heat pump (sole-water) heat pump (air-water) gas boiler µchp photovoltaic biomass boiler solarthermal plant connection to not more than two grids: electricity and natural gas, electricity and heating etc. simulation of one year with a resolution of 15min modelled by MATLAB and YALMIP Toolbox solver: gurobi discount factor of 2% only maximum power can be installed of each investment electricity generation P =, max( P ( y,t )) max electricity demand electricit heat generation Pmax, heat = max( PDemand ( heat,t)) preinstalled heat storage

9 Scenario Definition Disaggregated View (End User) A A1) baseline scenario: no increase in fuel prices reflect the consumers investment behavior A2) decrease in electricity prices: reduction in electricity prices by 5% annually increase in fossil fuel and district heating prices by 5% annually A3) increase in natural gas price: no increase in the electricity and district heating prices increase in fossil fuel prices by 5% annually

10 Scenario Definition Aggregated View (Spatial Distribution of Different End Users Types) B considering only the heat supply with grid connected heat generators project region Reininghaus in Graz (AT) splitting the area in 44 zones modelling the investment path up to 2050 B1) baseline scenario: low renovation moderate price increase (electricity, natural gas, district heating) of 2% B2) high renovation scenario: high renovation moderate price increase (electricity, natural gas, district heating) of 2%

11 Results Overview A) Disaggregated Results: single-family house (passive house standard) - three scenarios (A1-A3) - Levelized Costs of Electricity/Heat (scenario: decrease in electricity prices), calculation by: LC 8760 C I + psf (, r p, l, Y ) COF( t). crf (, r l, Y ) Demand ( t) norm, i i i Ι t = 1 electricity / heat = 8760 electricity / heat business customers: three scenarios (A1-A3) B) Aggregated Results: project region Reininghaus in Graz (AT) two scenarios (B1-B2) t = 1 Legend C norm,i... normalized transformation matrix bin... binary variable COF... cash outflow crf... capital recovery factor I... investment costs l... load development factor (e.g renovation) p... price development factor l... load development factor (e.g renovation) r... discount rate psf... price scenario factor y... current year till Y

12 A) Single-Family-House (Passiv) A1) baseline scenario Rank A2) decrease in electricity prices Rank A3) increase in natural gas price Rank electrical grid district heating heat pump (sole) heat pump (air) gas boiler µchp photovoltaik biomass boiler solarthermal plant 12

13 A) Single-Family-House (Passiv) - Levelized Costs of Electricity/Heat add A2) decrease in electricity prices solarthermal plant biomass boiler photovoltaik µchp gas boiler heat pump (air) heat pump (sole) district heating electrical grid Rank Levelized Costs of Electricity Levelized Costs in EUR/MWh Rank Levelized Costs of Heat Levelized Costs in EUR/MWh Rank investment costs fuel costs maintenance costs 13

14 A) Business A1) baseline scenario Rank A2) decrease in electricity prices Rank A3) increase in natural gas price Rank electrical grid district heating heat pump (sole) heat pump (air) gas boiler µchp photovoltaik biomass boiler solarthermal plant 14

15 B) Aggregated Results B1) baseline scenario, with parameters: - rate of renovation: 3% - renovation effect: 10% heat energy saving of construction class D, E, F and G district heating district heating district heating district heating natural gas natural gas natural gas natural gas not defined not defined not defined not defined heat pump heat pump heat pump heat pump

16 B) Aggregated Results B2) high renovation scenario, with parameters: - rate of renovation: 3% - renovation effect: 60% heat energy saving of construction class D, E, F and G district heating district heating district heating district heating natural gas natural gas natural gas natural gas not defined not defined not defined not defined heat pump heat pump heat pump heat pump

17 Conclusion (1/2) Small-Scale Customers Most economic for heat generation: 1. natural gas boiler 2. district heating 3. electric heat pump (AT) / biomass (DE) Trade-Off between renovation costs and resulting high energy prices (especially district heating) due to high fix costs. low potential of PV surplus power in heat pumps for heating cooling small hybrid composite advantages

18 Conclusion (2/2) Medium/Large-Sized Customers Most economic for heat generation : 1. natural gas: boiler / µchp (DE) 2. electric heat pump (AT) / biomass (DE) 3. district heating (AT/DE) high electricity prices support distributed self-generation (benefit of economies of scale) µchp heat storages PV high hybrid composite advantages

19 Andreas Fleischhacker Vienna University of Technology Energy Economic Group, EEG Gußhausstraße / E Vienna, Austria [T] [F] [E] fleischhacker@eeg.tuwien.ac.at [W]

20 hybrid? Source: IBA (2014) r/projekt/energiebunker.html

21 Data consumer energy prices (DE) Households Business Source Electricity 298 EUR/MWh 230 EUR/MWh Natural Gas 68 EUR/MWh 57 EUR/MWh District Heating 89 EUR/MWh 71 EUR/MWh eurostat (2014), BDEW (2014), own calculations eurostat (2014), own calculations Bundeskartellamt (2012), own calculations Biomass (Pellets) 55 EUR/MWh 50 EUR/MWh CARMEN (2014)