Integrated Renewable Power, Gas & Heat (DH) Systems & Markets Co-ordination and Cooptimization using PLEXOS Integrated Energy Model Mrs Peny Panagiotakopoulou, Senior Power Systems Consultant, Energy Exemplar Europe Dr Christos Papadopoulos, Regional Director Europe Abstract - The scope of this paper is to present the methodology and results of integrated modelling, simulation and co-optimization of Renewable Power, Gas & Heat Systems & Markets employing PLEXOS Integrated Energy Model and accounting for all characteristics unique to each subsystem. The trend towards a more interrelated energy industry calls for the need of multi-optimization models which will consider simultaneously all systems & markets components, in order to deliver better and more sophisticated Resource Planning capabilities combined with robust economic benefit and strategic development analysis. Keywords PLEXOS ; co- optimization; multi- optimization; power-gas-heat integrated model; integrated energy networks; Renewables Integration Introduction In the today s evolving energy world, there is an undeniable uncertainty and complexity in the energy markets. Market participants are forced to operate into extremely challenging environments characterized by increasingly uncertain and complex market conditions. Power and gas utilities strive to realize more value from their transmission and distribution businesses. At the same time the developments on the natural gas exploration lead to increased dependence of the power sector on the natural gas generation. This makes the gas-electric network coordination an emerging challenge for regulators and policy makers, and new strategies of co-optimization of gaselectric infrastructures are becoming of interest. Additionally there is an increased utilization of other distributed generation technologies such as co-generation and combined heat and power, which makes the coupling between electricity, natural gas and district heating energy systems even more common. This increasing utilization of gas-fired and other distributed generation, especially co- and trigeneration, is expected to affect both the technical and economical operation of energy systems. The conversion between different energy components (i.e. natural gas into electricity and heat) establishes a coupling of the corresponding power flows resulting in system interactions. Therefore, given this trend towards a more integrated energy industry, there is a prevailing need for a single multi-optimization model which will consider simultaneously the power, gas and district heat market components, in order to deliver better and more sophisticated Resource Planning capabilities combined with robust economic benefit and strategic development analysis. Inevitably, there is a need for advanced computational tools that will be able to capture and handle this complexity in the most efficient manner and to provide viable strategic long-term and operational short-term solutions. PLEXOS Integrated Energy Model software is such a sophisticated optimization tool that has been designed and developed to provide this kind of solutions in complex power, gas and heat systems and markets conditions. 1 P a g e
In this study we will develop an integrated electric- gas- heat network in PLEXOS. This PLEXOS model is capable of analysing the integrated system while accounting for characteristics unique to each subsystem, through a straight forward problem formulation. Aims The aim of this paper is to present an integrated electric- gas- heat network modelled in PLEXOS. This PLEXOS model is capable of analysing the integrated system while accounting for characteristics unique to each subsystem, through a straight forward problem formulation. For this case study a PLEXOS model using the Power and Gas Features was gradually developed, in order to finally account for all the different energy components. Theoretical Background A. CHP & District Heat Systems CHP technology is becoming even more popular nowadays, since it can provide a high energy supply performance, requires less fuel to be consumed and produces less CO 2 emissions per MWh. The CHP plants can nearly double the efficiency of steam power, whereas their total efficiency can be further improved by integration with gas- turbine plants and/ or the use of gas condensing units. District heating (DH) is a system for distributing heat generated from one or more sources via a network of insulated pipes carrying steam or hot water to heat buildings. The heat sources can be different types of power stations such as industrial processing power plants which generate heat as a by-product, energy from waste plant, heat-only boiler stations, geothermal or solar. By utilising low grade heat which otherwise might have been wasted and displacing localised boilers district heating systems can provide higher efficiencies and improve pollution control. The deployment of combined heat and power (CHP) plants burning fossil fuels or biomass can be facilitated by the use of District Heating Networks (DHNs). Such co-generation plants are designed to generate electricity whilst also capturing usable heat that is produced in this process. Their optimised design means that the overall efficiency of CHP plants can reach in excess of 80% at the point of use. The figure below shows a sketch of a CHP plant that supplies heat to a district heating system, chilled water by means of absorption chillers to a district cooling system as well as electricity to the grid. In CHP-DH systems there may be situations where there is demand for electricity but insufficient demand for heat. This can lead to possible alternatives of either closing down the CHP plants with a subsequent revenue loss from electricity sales or surplus heat rejected to the atmosphere. In order to avoid such situations, thermal storages are used together with the DHNs, with several examples existing of such large scale thermal storage systems that have been deployed across Europe (i.e. Denmark, UK). These systems follow an increasing trend and make heat storage a more and more widespread technology for performance optimization and increased revenue from electricity sales. 2 P a g e
Figure 1 Schematic figure of a CHP plant integrated with the district heating and cooling systems 3 The storage is charged when heat production is higher than the consumption, and discharged when heat production is below the consumption, allowing for CHP plants to operate with higher flexibility, especially when electricity prices are most favourable. A typical CHP plant s operation depends on the electricity network for certain periods, i.e. for purchasing power from the grid during high demand periods / off- peak prices, or for selling surplus power to the grid during peak hours with highest electricity prices, returning significant economic benefits. For that reason, and as the system loads (heat and electrical) fluctuate considerably with time of day/year, mathematical programming; and more specifically Economic Dispatch models, are used for optimization of the district heating and cooling networks with CHP. Figure 2 Thermal store integrated in a DH system 4 B. Gas Networks Given the recent technological advances allowing for access to unconventional sources in shale formations, coal beds, and sandstone formations, natural gas reserves have dramatically expanded. A subsequent increase in natural gas production is now a reality followed by supply surplus and lower prices, which are expected to continue resulting in relatively stable natural gas market conditions. As a result, natural gas is a new dominant player in the energy markets. Natural gas-fired turbines are characterized by higher efficiencies, lower capital costs, shorter installation times, faster start up capabilities and lower CO 2 emissions, which makes them an increasingly widespread option either as baseload, intermediate or peaking units. The electric power sector is using an increasing percentage of natural gas, and natural gas-fired electric power plants are expected to continue to increase in importance, projected to account for 60% of capacity additions between 2010 and 2035. 3 P a g e
Natural gas electricity generation relies on three basic technologies: - Steam turbine plants, that operate like traditional coal-fuelled power plants where fossil fuel (in this case natural gas) combustion heats water to create steam. The steam turns a turbine, which runs a generator to create electricity. These typically have thermal efficiencies of 30 35%. - Combustion turbine plants, which are generally used to meet peak electricity demand. These operate similarly to jet engines: natural gas is combusted and used to turn the turbine blades and spin an electrical generator. The typical size is 100 400 MW with a thermal efficiency around 35 40%. - Combined cycle plants, which are highly efficient because they combine combustion turbines and steam turbines; the hot exhaust from a gas-fired combustion turbine is used to create steam to power a steam turbine. High efficiency combined cycle plants emit less than half the CO2 per megawatt-hour as coal power plants, and operate with a 50 60% thermal efficiency range. A typical natural gas combined cycle power plant has a heat rate that is about one third lower than for a combustion turbine or gas-fired steam turbine plant. Figure 3 Combustion Turbine There is an increased recognition that environmental policy and public policy in combination to the vast shale gas developments will lead to increased reliance of the power sector on natural gas generation. Gas Electric coordination has emerged as a complex topic for regulators and the gas and electric sectors to confront along with the electrical system operators with concerns of present and future potential gas constraints that impact electric system operation and reliability. New strategies of co-optimization of electric and gas infrastructure are becoming of interest as much of the natural gas sector growth may likely be driven by resource change and dependency of electrical sector on pipeline network and gas network operational issue. The PLEXOS Model In this case study a PLEXOS Integrated power gas heat model has been developed (Figure 4). The Power feature of PLEXOS has been used in order to set up an electricity demand power node, ELECTRCITYDem, where all the power plants are located, as well as part of the electricity demand. The Generator Class of PLEXOS is generic enough so that all types of generation resources i.e. thermal, hydro, pump storage, wind, solar etc. are modelled under this class and the simulator infers the type of generator from the data and relationships that are defined on it. 4 P a g e
A RENEWABLES power node has been also set up, where all the renewable resources (wind and solar) are located. In the same node an Electric Boiler with heat storage facilities as well, can be connected. Figure 4 System Topology The electric boiler is a type of boiler where heat 2, i.e. in the form of steam, is generated using electricity rather than through the combustion of a fuel. Electric boilers can convert electrical energy to heat with almost 100% efficiency, but because boilers draw power from the grid their true efficiency is a function of the overall grid production efficiency. Hence electric boilers may be economic as heat sources at times of very low electricity price, especially if heat can be stored for later use. An electric boiler can be created in PLEXOS by defining an anti-generator, which is a generator whose generation acts as a load on the system rather than proving power. A heat load is directly defined on the electric boiler, which represents the waste heat that must be extracted for exogenous loads. The total demand for electricity has been defined on a regional level in PLEXOS and then split between the two nodes, using a load participation factor, as shown in figure 5. Figure 5 Electricity Demand (MW) by node 5 P a g e
The gas market is modelled using the dedicated Gas Feature of PLEXOS, which include the gas nodes, gas fields for the fuel production, gas storages where the gas can be injected and extracted, and the gas pipelines for gas transportation. In PLEXOS the gas and electric markets integrate at the gas nodes. A generator can be attached to a gas node through the Gas Node and the Fuel Gas Node memberships. Defining both these memberships instructs the simulator that the Generator is physically supplied with Fuel from the Gas Node and therefore the Gas Demand will be driven by the generator s Heat Rate and Generation values. Furthermore, there is the option to define additional external demand for gas, as it has been done in this model, where Industrial and Domestic gas demands have been added using external datafiles of hourly demand profiles. Figure 6 Gas Demand driven by GAS1 generator Heat Rate function The District Heating network is approximated using the Gas Features of PLEXOS in combination with the Constraints Class. The demand for heat is modelled using the Gas Demand objects defined with external hourly profiles, as shown in figure 8. Figure 7 External Domestic & Industrial Gas Demand (TJ) 6 P a g e
Figure 8 Hourly Heat Demand (line stacked) The District Heating network is modelled as a closed loop using the Gas Pipeline objects, and allowing the HEATdist1, HEATdist2 and HEATdist3 pipelines being one-direction only by defining the pipeline [Max Flow Back]= 0. The CHP type generators, named as HEAT1, HEAT2 and HEAT3 are associated with the heat network indirectly through the use of custom constraints. The heat production from the HEAT1 Field is set to be equal to 0.1% of the HEAT1 generator heat production, and a similar constraint is created for the other two CHP generators/ Fields. The CHP plants HEAT2 and HEAT3 are assumed to be a back-pressure and an extraction turbine accordingly. They are modelled in such a way so that the waste heat from the back-pressure turbine is passed to the extraction turbine, using the [Heat Input/ Output] collection of PLEXOS. However, instead of defining directly a [Heat Load] on the extraction turbine, the demand is driven by the external heat demands defined through the District Heating network. The amount of heat transferred from the back-pressure to the extraction turbine is equal to the fuel input of the back-pressure less the electric output of the back-pressure (at notional maximum efficiency). The waste heat can then be passed through a boiler before using the extraction turbine. In PLEXOS the boiler is modelled as a component of the extraction turbine. The extraction turbine will produce electric output from the steam output of the boiler according to its heat rate i.e. the steam input acts just like a free fuel source. The Storage Class of the Gas model is used in order to set up a HEAT storage where the excess heat can be stored in periods of higher heat production compared to consumption, and heat can be released when the production is below the required amounts of demand. This allows the CHP plants to generate in a more flexible manner and the system to benefit from favourable electricity prices. Figure 9 Heat recovery steam turbine in PLEXOS 7 P a g e
Problem Formulation When defining such a network in PLEXOS the primary optimization is to find the least cost solution given all the system constraints. When the mathematical problem formulated involves multiple mutually exclusive opportunities, the optimal solution reported should take into account all of these components and a co-optimization problem is solved. Co-optimization applies where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Once set-up, co-optimization is a natural outcome of the PLEXOS solution. In the current case study there are three different markets considered; power, gas and heat; and the problem will be automatically configured as a co-optimization problem where the optimal solution has to be found considering all the mutually exclusive opportunities. The Objective function is formulated as: Minimize{Total system cost} = Electric production cost + Electric Demand Shortage Cost + Gas Production cost + Natural Gas Demand Shortage Cost + Heat Production Cost + Heat Demand Shortage Cost + Transmission Wheeling Cost + Pipeline Gas Flow Cost + Pipeline Heat Flow Cost Subject To{System constraints}: [Electric Production] + [Electric Shortage] = [Electric Demand] + [Electric Losses] [Electricity Transmission Constraints] [Electric Production] feasible [Gas Production] + [Gas Demand Shortage] = [Gas Demand] + [Gas Generator Demand] [Gas Pipeline Constraints] [Gas Production] feasible [Heat Production] + [Heat Demand Shortage] = [Heat Demand] + [Heat Generator Demand] [Heat Pipeline Constraints] [Heat Production] feasible [Other Constraints] Scenarios Modelled & Results analysis We have looked at two different scenarios; a base case, where an electric boiler was not included in the problem (case A) and one with an electric boiler in place (case B). The efficiency of the electric boiler was assumed to be 100%. 8 P a g e
The unit commitment and economic dispatch of the plants is based on the marginal cost concept (nodal load settlement method). The renewables (WIND1, WIND2, SOLAR) are the least cost generators and that explains the fact that WIND2 is mostly used compared to the other generators in order to meet the whole electricity demand in the least cost effective way. The total generation as well as the dispatch of each plant changes, depending on whether the electric boiler is used or not (Figures 10 & 11). Figure 10 Generation Stack curve (Case A) Figure 11 Generation Stack curve (Case B) When the electric boiler is included in the system topology, the reported electricity demand on the RENEWABLES node changes compared to the base case, since the electric boiler is defined as an antigenerator (extra load). This subsequently affects the output of the wind and solar generators that have to account for the changing demand, whereas the total MW output from the conventional plants remains the same. 9 P a g e
Figure 12 Electricity Node RENEWABLES reported Load The Gas Field GASProd1, located at the gas node GASProd, has to produce in order to meet the gas demand from both the external sources (industrial & domestic) and the heat rate driven demand from the gas generator. Figure 11 Gas Field Production The total gas demands can be reported on each gas node separately as shown in figure 12. These demands remain unchanged whether the electric boiler is in place or not. Figure 12 Total Gas Demand The Heat demands on the other hand, should be covered by the Heat production as well as the Heat Storage facility (figure 13). The Heat Pipelines are also used in order to transfer the heat from the production/storage points to where it is required. 10 P a g e
Electric Boilers are usually economic as heat sources at times of very low electric price, especially if the heat can be stored for later use. In Case B the Electric Boiler [Heat Production] increases when electricity price decreases (Figure 14). Figure 13 Heat Production (Heat Fields Production & Heat Storage Net Withdrawal) Figure 14 Electric Boiler Heat Production in relation to Electricity Price Figure 15 Electricity Nodes Price (Case A & B) This integrated power-gas-heat model is a multi-optimization problem which considers all the different energy components and their constraints simultaneusly. PLEXOS will report one unique price for the Region modelled taking into account all the constraints, but it will also report the shadow price of each component individually. We can therefore obtain the Gas Nodes [Shadow Prices] as well as 11 P a g e
the Heat Nodes [Shadow Prices], in addition to the Electricity Nodes [Shadow Prices] (Figures 15 & 16). Conclusion Figure 16 Gas & Heat Nodes Shadow Price Creating integrated electric-gas-heat networks in PLEXOS is straight forward and can be easily done using the dedicated features for Power and Gas modelling. The link between the different energy type networks, can be defined using the proper type of memberships between key objects in PLEXOS interface. In this case study a short term multi-optimization problem was solved, taking into account the individual techno-economic characteristics of each energy subnetwork, and analysing the system as a whole integrated (NEXUS) network in terms of production costs. The interactions between the different energy components were captured, as for example the relation between heat production and storages charge /discharge periods, CHP plants operation during periods of more favourable electricity prices, gas demand driven by the power network, etc. The same network topology can be easily modelled for long term period studies, using the LT Plan capacity expansion phase of PLEXOS, in order to assess the type, time and location of new investments. The PLEXOS Forecasting tool can be used in order to create power, gas and heat demand profiles for i.e. 20-30 years ahead, whereas the power, gas and heat energy components can be modelled as expansion candidates using the appropriate expansion properties of PLEXOS (Max Units Built, Built Cost, Economic Life, WACC, etc.). Therefore PLEXOS can be a powerful and robust optimization tool for demand planning, strategic investment and operation decisions in the era of integrated energy systems. References [1] Article: Combined Heat and Power, PLEXOS Wiki [2] Article: Heat Storage, PLEXOS Wiki [3] The potential for thermal storage to reduce the overall carbon emissions from district heating systems, The Tyndall Centre, Tyndall Manchester [4] Economic and Environmental Benefits of CHP-based District Heating Systems in Sweden, Linköping Institute of Technology 12 P a g e