Modelling investment decision making in the power sector under imperfect foresight

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Modelling investment decision making in the power sector under imperfect foresight

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Modelling investment decision making in the power sector under imperfect foresight

  1. 1. Simulating investment decision making in the power sector under imperfect foresight Kris Poncelet 69th Semi-annual ETSAP Meeting University College Cork Cork, Ireland
  2. 2. 22016-06-01 Reduce computational complexity Allows to increase level of detail Trade-off LT time horizon versus level of ST detail Either optimization or simulation paradigm Capture imperfect foresight and short- term focus of decision makers More realistic decision making? Simulation paradigm Myopic optimization: limited window of foresight
  3. 3. 32016-06-01 Complementing optimization models Optimization: what is the cost-optimal transition pathway? Simulation: which policies are needed to realize this transition? Assess effectiveness and efficiency of policies
  4. 4. 42016-06-01 Liberalized electricity markets Investment decision makers are private utilities Invest if projected revenues allow a reasonable internal rate of return IRR (incl. risk premium) Generation assets have a long lifetime Future revenue streams are uncertain
  5. 5. 52016-06-01 Methodological analysis 2 scenarios: Perfect foresight (PF) Myopic foresight (MF10) Focus on period 2020-2055 5-year periods Power system inspired by the Belgian system Increasing carbon price LUSYM Investment planning model: Partial equilibrium 8 representative days Clustered unit commitment
  6. 6. 62016-06-01 Perfect foresight 10-year foresight Impact on results
  7. 7. 72016-06-01 Liberalized electricity markets Investment decision makers are private utilities Invest if projected revenues allow a reasonable internal rate of return IRR (incl. risk premium) SR profits = revenues – operational costs {𝐸[𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦] Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§π‘¦ } β‰₯ 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘ 
  8. 8. 82016-06-01 Perfect foresight scenario Perfect foresight of: Electricity prices (and capacity remuneration) in each time step and all years (implicit) Generation during each time step and all years Generation costs (fossil fuel prices, maintenance, etc.) => Exactly know SR profits => {𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§π‘¦ } = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘ 
  9. 9. 92016-06-01 Perfect foresight scenario
  10. 10. 102016-06-01 Perfect foresight scenario
  11. 11. 112016-06-01
  12. 12. 122016-06-01 Perfect foresight scenario Factors which can impact the SR profits: Fossil fuel prices Carbon price Timing of decommissioning existing plants Type, timing and amount of newly built capacity Technological progress Evolution of the electricity demand Policy interactions Market design changes Interconnections Private utilities do not have perfect information The assumption of perfect foresight is not realistc
  13. 13. 132016-06-01 Perfect foresight scenario
  14. 14. 142016-06-01 Perfect foresight scenario - conclusions {𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§π‘¦ } = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  Perfect foresight for private utilities is unrealistic Can lead to unrealistic simulation of investment decisions
  15. 15. 152016-06-01 Myopic foresight model 𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§ π‘Ž+π‘€πΉβˆ’1 𝑦=π‘Ž = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  βˆ’ π‘ π‘Žπ‘™π‘£π‘Žπ‘”π‘’ π‘£π‘Žπ‘™π‘’π‘’ Calculation of salvage value typically based on two assumptions: 1. Total discounted value of an asset equals the total discounted cost 2. value is distributed homogenously over the asset’s life time 𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§ π‘Ž+π‘€πΉβˆ’1 𝑦=π‘Ž = π‘Žπ‘›π‘›π‘’π‘Žπ‘™π‘–π‘§π‘’π‘‘ 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘ Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§ π‘Ž+π‘€πΉβˆ’1 𝑦=π‘Ž TLIFE = 5 d = 10% Time horizon = window of foresight
  16. 16. 162016-06-01 Myopic foresight scenario
  17. 17. 172016-06-01 Myopic foresight scenario
  18. 18. 182016-06-01 Myopic foresight scenario - conclusions 𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§ π‘Ž+π‘€πΉβˆ’1 𝑦=π‘Ž = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  βˆ’ π‘ π‘Žπ‘™π‘£π‘Žπ‘”π‘’ π‘£π‘Žπ‘™π‘’π‘’ Salvage value is determined exogenously Homogeneous distribution of the value of an asset seems optimistic (implications for dynamic recursive models) => No extrapolation of observed trends Can lead to unrealistic simulation of investment decisions Additional issues: What is the window of foresight? Window of foresight identical for all uncertain parameters
  19. 19. 192016-06-01 {𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§π‘¦ } = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  Private utilities do not have perfect foresight on short-run profits Can lead to unrealistic investment decisions 𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦 Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§ π‘Ž+π‘€πΉβˆ’1 𝑦=π‘Ž = 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  βˆ’ π‘ π‘Žπ‘™π‘£π‘Žπ‘”π‘’ π‘£π‘Žπ‘™π‘’π‘’ No extrapolation of observed trends in short-run profits Can lead to unrealistic investment decisions Investment criterion: {𝐸[𝑆𝑅 π‘ƒπ‘Ÿπ‘œπ‘“π‘–π‘‘π‘  𝑦] Γ— 1 1+𝐼𝑅𝑅 π‘¦βˆ’π‘§π‘¦ } β‰₯ 𝑓𝑖π‘₯𝑒𝑑 π‘π‘œπ‘ π‘‘π‘  Summary and conclusions Perfect foresight Myopic foresight Kris Poncelet, kris.poncelet@kuleuven.be KU Leuven, energy and environment group http://www.mech.kuleuven.be/en/tme/resea rch/energy_environment/ Poncelet, K. et al., Myopic Optimization Models for Simulation of Investment Decisions in the Electric Power Sector. 13th International conference on the European Energy Market, 6- 9 June 2016, Porto.
  20. 20. 202016-06-01 Open questions Optimization Vs. Simulation: should TIMES be used for simulation? What are the alternatives?

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