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Market Power in a Coal-Based Power Generation Sector: Case Study for Poland Jacek Kamiński Polish Academy of Sciences Mineral and Energy Economy Research.

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Presentation on theme: "Market Power in a Coal-Based Power Generation Sector: Case Study for Poland Jacek Kamiński Polish Academy of Sciences Mineral and Energy Economy Research."— Presentation transcript:

1 Market Power in a Coal-Based Power Generation Sector: Case Study for Poland Jacek Kamiński Polish Academy of Sciences Mineral and Energy Economy Research Institute Energy and Environmental Policy Division Wybickiego 7, Krakow, Poland

2 Outline of the presentation Background and aim of the study Overview of the Polish power sector Applied methodology Results Conclusions

3 Background of the study Market reforms started in 1989: commercialisation, unbundling, TPA rule and partial privatisation Several consolidations of State-owned power plants (2000, 2004, 2007) Currently a 30% market share of the largest power producer – potential for market power Lack of quantitative analysis of market power in the Polish power generation sector: – Lack of any quantitative tool for market power studies

4 Aim of the study To develop a game theory-based market equilibrium model of the Polish power generation sector To carry out an analysis of market power in the Polish power generation sector based on modelling approach

5 The Polish power sector

6 Installed capacity [%] Total installed capacity: approx. 35 GW

7 Electricity genration mix [%] Annual electricity production: approx. 150 TWh

8 Market shares (installed capacity)

9 Market shares (electricity production)

10

11 Methodology

12 Methodology: game theory-based model of the Polish power generation market Partial equilibrium model of the wholesale power generation market The Cournot approach with Conjectural Variations The model captures different market structures based on strategies of market players (strategic behaviour, price-taker) 14 hard coal-based public power plants, 6 brown coal-based public power plants and 33 public CHPs: – as individual units or within energy groups 12 loads 4 seasons (Q1, Q2, Q3, Q4) and 3 loads (peak, mid, base) Copper plate model of transmission network Developed with GAMS as a Mixed Complementarity Problem (MCP), solution found by the PATH solver

13 Lagrange function

14 KKT conditions (1) Derivative of the Lagrange function:

15 KKT conditions (2) Demand for power: Capacity constraint: Emission constraint:

16 Scenarios Scenario/caseDescription Scenario: REFReference scenario based on market outcomes for 2008 COMPCompetitive behaviour of all electricity producers FULL1ST Full strategic behaviour of the biggest electricity producer (all other producers behave as price takers) FULL2ND Full strategic behaviour of the 2 nd biggest electricity producer (all other producers behave as price takers) ALLSAME All electricity producers are assumed to behave strategically with the intensity of the biggest producer under the REF scenario Case (formed by adding the following suffix): _HC20UPHard coal prices increased by 20% _HC20DNHard coal prices decrease by 20% _BC20UPBrown coal prices increased by 20% _BC20DNBrown coal prices decrease by 20%

17 Measures, compared under different scenarios Gross electricity production [TWh] Market price of electricity [/MWh] Consumer and producer surpluses [M] Net social surplus and the dead weight welfare loss [M] Electricity production fuel-mix [TWh] SO 2, NO X, CO 2 emissions from the power generation sector [kt] or [Mt] Fuel supplies to the power sector (separately for hard coal, brown coal, etc.) [PJ]

18 Results

19 Electricity production/market price REFCOMPFULL1STFULL2NDALLSAME Electricity production [TWh] Market price [/MWh]

20 Consumer/producer surplus [M] REF Difference when compared to the REF scenario: COMPFULL1STFULL2NDALLSAME Consumer Surplus Producer Surplus Net social surplus

21 Relative change when compared to the REF scenario [%] REFCOMPFULL1STFULL2NDALLSAME Electricity producton-6.7%- 26.6%- 3.3%- 4.9% Market price %113.9%8.2% 12.5% Consumer surplus-0.4%- 2.4%- 0.2%- 0.3% Producer surplus %132.1%12.7% 19.1% Net social surplus-0.05%- 0.50%- 0.01%- 0.02%

22 SO 2 /NO X emissions

23 CO 2 emissions

24 Fuel supplies to the power generation sector [PJ] REFCOMPFULL1STFULL2NDALLSAME Hard coal Brown coal Natural gas Renewables Total

25 Fuel supplies: difference when compared with the REF scenario [PJ] REFCOMPFULL1STFULL2NDALLSAME Hard coal Brown coal Natural gas Renewables Total

26 Change in CO 2 emissions when compared to the REF scenario

27 Conclusions

28 Existing potential for market power has a significant impact on wholesale electricity prices and production volumes – under the competitive scenario the average wholesale electricity price would be 5.7/MWh lower and total electricity production would be almost 10 TWh higher As a result of market power the transfer of surpluses is observed. Under the COMP scenario: – the consumer surplus would decrease by 803 M and the producer surplus would increase by 695 M – the estimated dead weight welfare loss of 108 M – the net social loss resulting from market power in the power generation sector.

29 Conclusions The power generation sector based on coal has a significantly reduced responsiveness to changes in fuel prices Imposing a carbon tax would not lead to a significant change in CO 2 emissions in the short-run – long-term model recommended Indirect impact of market power on the emissions levels

30 Market Power in a Coal-Based Power Generation Sector: Case Study for Poland Jacek Kamiński Polish Academy of Sciences Mineral and Energy Economy Research Institute Energy and Environmental Policy Division Wybickiego 7, Krakow, Poland

31 Backup slides

32 HHI HHI (power generation sector, installed capacity) HHI > 5000Belgium, France, Greece, Latvia, Luxemburg, Slovakia 1800 < HHI< 5000 The Czech Rep., Germany, Lithuania, Portugal, Slovenia, Romania, Hungary, Denmark, Norway 750 < HHI < 1800 Finland, Poland, UK, Spain, Itally, The Netherlands, Austria

33 Key KKT conditions (3) Renewable electricity production constraint:

34 Legend: Scenario elements Balances Variables Data Price(l) FuelPrice(f) Production(p,l) Shp Capacity(p,l) Shp Emission(m) ShpFuel Capacity(s) ShpFuel Capacity(s) Fuel Supply(s) Fuel Supply(s) Demand(l) Losses(p) Profit(p,l) Elasticity(l) VariableCost(p) Efficiency(p) Emission Factor(p,m) Shp Renewables Market Share(p,c) Conjectural Variation(p,c) Conjectural Variation(p,c) Minimum share of Renewables Emission Limit(m) Emission Constraint(m) Capacity Constraint(p) Installed Capacity(p) Capacity Factor(p) Renewables Constraint Renewables Constraint Fuel Demand(f) Fuel Supplier Profit(s) FuelCost(s) Fuel Capacity Constraint(s) Fuel Capacity(s)

35 Profit/demand functions Profit function Demand function

36 Conjectural Variation

37 Company name Ownership structure as of the end of 2010 Production of electricity Distribution TWh [%] [%] PGE Polish Energy Group 69.29% - State Treasury 30.71% - Other stakeholders %29% Tauron Polish Energy 41.96% - State Treasury 58.04% - Other stakeholders %14%26% EDF Group100% - EDF Group15.310%- Enea 52.92% - State Treasury 18.67% - Vattenfall AB 28.41% - Other stakeholders 11.88%9% ZE PAK 50% - State Treasury 47.38% - Grupa Elektrim 2.62% - Other stakeholders 11.58%8%- GDF Suez100% - GDF Suez6.34%4%- Energa 84.19% - State Treasury 15.81% - Other stakeholders 3.52%2%16% Vattenfall100% - Vattenfall4.33%3%11% RWE Stoen100% - RWE--6% Other companies %3% Total %

38 Concentration Ratio (CR1) PKE (14%) El. Bełchatów (18%) BOT GiE (22% moc zainst.) (31-33% produkcja e.e.) Polska Grupa Energetyczna (30% moc zainst.) (36-39% produkcja e.e.)

39 Concentration Ratio (CR3) PKE (14%) Bełchatów (13%) Kozienice (8%) Bełchatów (18%) PKE (13%) PAK (9%) BOT GiE (22%/32%) PKE (14%/13%) EdF (9%/10%) PGE (30%/36-39%) Tauron (15%/15%) EdF (9%/10%)

40 HHI

41 Residual Supply Index PKE BOT GiE PGE

42 Residual Supply Index

43 Lerner Index

44

45 Financial results of the Polish Energy Group

46 MCP MCP is a generalization of a pure nonlinear complementarity problem (NCP). In an MCP, a vector x must be determined, so that:

47 MCP The MCP formulation also allows for non-zero lower bounds as well as upper bounds to the variables for which a solution must be determined:


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