Long-Term Security of Supply in Liberalized Electricity Systems

1. INSTITUTO DE INVESTIGACIÓN TECNOLÓGICA LONG-TERM SECURITY OF SUPPLY AND STRATEGIC POLICIES IN LIBERALIZED ELECTRICITY SYSTEMS: CAPACITY PAYMENTS, CAPACITY MARKETS AND RENEWABLES PROMOTION MECHANISMS Álvaro López-Peña, Efraim Centeno, Julián Barquín 10th IAEE European Conference Energy, Policies and Technologies for Sustainable Economies Vienna, 7 - 10 September 2009

2. Introduction Objective Model Description Case Study Conclusions References Outline

3. Introduction Objective Model Description Case Study Conclusions References Outline

4. European Energy Policy seeks an… …energy supply for European citizens Introduction (I/IV) Affordable Secure Environmentally friendly

5. Focusing on electricity supply in liberalised systems, the generation business importantly affects all these items Introduction (II/IV) • Enough installed capacity • Competition Affordable Secure Environmentally friendly • Enough installed capacity • Diversified primary energy sources • Clean and efficient generation technologies

6. The policies for influencing companies behaviours upon the mentioned aspects can be classified: Introduction (III/IV) Competition policy: a big issue, but not treated in this research • Enough installed capacity • Competition “Adequacy” problem: having enough megawatts Affordable Secure Environmentally friendly • Enough installed capacity • Diversified primary energy sources • Clean and efficient generation technologies “Indicative planning” problem: having good megawatts

7. Introduction (IV/IV) …therefore, in real power markets, regulators have to implement different policies pursuing different objectives… …and this may not be effective in reaching these objectives… …or may be inefficient at it… …or may even bring undesired effects…

8. Introduction Objective Model Description Case Study Conclusions References Outline

9. Objective (I/II) Analyse the effects of combining… • capacity mechanisms (for adequacy) • renewables promotion schemes …in a system like the Spanish one, and assess: • Efficacy for maintaining a good adequacy level and for increasing renewables’ share in the generation mix • Efficiency, from the total cost for consumers’ point of view • Possible undesired interactions between the policies

10. Objective (II/II) • Long term evolution of a liberalised electric system similar to the Spanish one will be studied, combining: • As capacity mechanisms: Capacity Payments (CP) and Capacity Markets (CM) • As renewable promotion scheme: different feed-in premiums • Its efficacy and efficiency will be assessed for: • Maintaining stable and acceptable reserve margins • Making renewable share grow on generation mix • And main interactions will be identified

11. Introduction Objective Model Description Case Study Conclusions References Outline

12. Model description (I/III) • Long-term evolution of the liberalised electrical system modeled through a System Dynamics-based simulation model (Sánchez, 2009) • Represents generation expansion process taking into account important aspects of liberalised systems: • Imperfect competition • Forward contracting and its influence over spot strategies • Companies’ (strategic and financial) differentiation • Similar system to the Spanish one: mainly thermal, capacity-constrained rather than energy constrained, no new hydro investments.

13. Model description (II/III) • Model overview: • Simulated sequentially, one-year resolution. Prices and productions calculated for each load level.

14. AUFCP= Average Uncovered Fixed Cost of a Peaker Model description (III/III): decision block • Decisions based on expected profitability (Net Present Value) • Different modeling depending adequacy mechanism • Capacity payments: constant capacity price of 39k€/MW • Capacity market: based upon ideas in (Hobbs et al., 2005) • Elastic demand curve • Generators offer firm capacity: old capacity at null price, new one at the price that makes it profitable • The CM results in a per-megawatt “capacity price” payment that all new generators that were committed that year perceive.

15. Introduction Objective Model Description Case Study Conclusions References Outline

16. Case study (I/VI) • Investment in thermal and renewables, not in hydro. 25 years horizon. • Eight companies (among which: one IPP and one Renewable Agent). • Some have some initial hydro capacity • 3% constant demand growth, Price cap=NSE price=180 €/MWh, carbon price 25 €/ton. • 8 technologies for possible investments: nuclear, fuel, coal, national coal, CCGT, gas turbines, wind and other renewables. Most relevant ones’ characteristics:

17. Case study (II/VI) • Peakers: gas turbines. AUFCP= 0.75 * 52130 €/MW = 39000 €/MW • Capacity payment = 39000 €/MW • Capacity market: AUFCP= 39000 €/MW, target reserve margin= 120%, DELTA= 15% • 6 studied cases:

18. Case study (III/VI)

19. Case study (IV/VI)

20. Case study (V/VI) • Both CP cases equal and equal to the CP_P0 one: premium values not enough for promoting renewable investments under CP scheme

21. Case study (VI/VI)

22. Introduction Objective Model Description Case Study Conclusions References Outline

23. Conclusions (I/IV) • In order to achieve enough installed capacity, it seems from our case study that capacitymarkets are more effective (higher and more stable reserve margins observed) and efficient (from the total costs for consumers perspective) than capacity payments. This is because a wider range of capacity prices can be attained, causing peaking units to enter the system if needed. In addition, this need is expressed by capacity prices growing when reserves margin fall from the target values, what creates a stabilising effect.

24. Conclusions (II/IV) • Concerning the renewables support: as these technologies have relatively high fixed costs, more investment is observed under capacity markets for the same premium value.

25. Conclusions (III/IV) • From our results, it seems that capacity markets aremore desirable for obtaining enough and good megawatts, because more investment is obtained in peaking units, causing reserve margins and prices to be stabilised in acceptable levels. A slight renewables promotion suffices to transfer some of this investment to wind turbines.

26. Conclusions (IV/IV) • Given that feed-in premiums are a fully price-based mechanism, if set too high, they may ruin the stabilising effect that capacity markets (quantity-based) create upon reserve margins. This is the main contribution of our study, and it suggests the need for careful design of policies aiming at addressing the adequacy problem while implementing indicative planning.

27. Introduction Objective Model Description Case Study Conclusions References Outline

28. References Hobbs, B. F., J. Inon, M.-C. Hu and S. Stoft (2005). Capacity Markets: Review and a Dynamic Assessment of Demand-Curve Approaches. IEEE Power Engineering Society General Meeting. San Francisco, California USA. Sánchez, J. J. (2009). Strategic Analysis of the Long-Term Planning of Electric Generation Capacity in Liberalised Electricity Markets. Instituto de Investigación Tecnológica. Madrid, Universidad Pontificia de Comillas. PhD Industrial Engineering: 310.

29. THANK YOU VERY MUCH FOR YOUR ATTENTION!! Alvaro López-Peña PhD Student Instituto de Investigación Tecnológica Universidad Pontificia Comillas, Madrid alvaro.lopezpena@iit.upcomillas.es www.iit.upcomillas.es

30. Capacity mechanisms’ choice • Leave it to the market: • Learning period for contracts, politically harmful • Capacity auctions: • Effective but interventionist • Strategic reserves (peaking units purchase) • Effective but interventionist • Capacity payments: • Theoretically sound (compensation for price cap), effective • Capacity markets: • Effective and efficient (market based), hard to implement if hydro investors • Reliability options: • Effective and efficient (market based), overcome most of the drawbacks, but do not promote active demand response and may be subject to market power abuses

31. Model description (IIb1) • Model overview: • Simulated sequentially, one-year resolution. Prices and productions calculated for each load level. • Forward market • Strategic parameter endogenouscalculation • Spot market • Permit obtaining delay • Construction delay • Endogenous discount rate calculation • Profitability calculation • Investment decision-making • Price forecasting • Production forecasting

32. Model description (IIb2) • Decision block: • Endogenous discount rate calculation: credit risk theory • Profitability calculation: NPV per agent and technology in €/MW. Hypothesis: one single plant does not significantly change electricity prices, whatever the plant’s size is. Valid for a big system as the one under study. • Each year, every company invests only in its most profitable technology. Portfolio analysis and risk management not modeled. Common under both CP and CM schemes Different in the CP case from the CM one

33. Model description (IIIb1) • Decision block’s third step under CAPACITY PAYMENTS: • The CP is a constant per-megawatt payment that all generators perceive. • It’s used to compute the NPV • Then, the amount of requested permits (MWs) is decided as a function of investment costs and profitability, having a maximum value. Very complex relation, empirical exponential curves habitual in the literature • When permits are obtained, they are divided into a discrete number of plants.

34. Model description (IIIb2) • Decision block’s third step under CAPACITY MARKETS (I of IV) Based upon ideas in (Hobbs et al., 2005) • Regulator sets a target reserve margin over expected demand, and generators must offer firm capacity (old or new one) for contracting. Demand curve set by the regulator (not vertical one). • The CM results in a per-megawatt “capacity price” payment that all new generators that were committed that year perceive.

35. Model description (IIIb3) • Decision block’s third step under CAPACITY MARKETS (II of IV) Based upon ideas in (Hobbs et al., 2005) • Generators’ offers: • No market power in the Capacity Market: long term, easy entry • Already existing firm capacity at null price • New capacity: growing stepwise function but just the most profitable technology (the first one with a positive NPV for a range of capacity prices) • For calculating it: range of capacity prices -> range of NPVs –> range of MWs -> discretization

36. Model description (IIIb4) • Decision block’s third step under CAPACITY MARKETS (III of IV) Based upon ideas in (Hobbs et al., 2005) • Generators offer: • Supposition made: offering the first technology with a positive NPV implies considering it’s the most profitable one for all capacity prices • Slopes affected by: • Fixed Costs/Variable Costs relation • Firmness factors • Hypothesis: same firmness for all thermal technologies

37. Model description (IIIb5) • Decision block’s third step under CAPACITY MARKETS (IV of IV) Based upon ideas in (Hobbs et al., 2005) • Market clearing: