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, 31-261 Krakow, Poland

Outline of the presentation

• Background and aim of the study• Overview of the Polish power sector • Applied methodology• Results • Conclusions

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

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

The Polish power sector

Installed capacity [%]

Total installed capacity: approx. 35 GW

Hard coal; 64.6%

Brown coal; 28.0%

Natural gas; 2.7%

Hydro; 2.6%

Wind; 1.7%

Biomass; 0.4%

Electricity genration mix [%]

Hard coal; 57.1%

Brown coal; 35.5%

Natural gas; 3.0%

Hydro; 1.6%

Wind; 0.6%

Biomass; 2.3%

Annual electricity production: approx. 150 TWh

Market shares (installed capacity)

Market shares (electricity production)

Methodology

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

Lagrange function

p l p,l p ll

f ,ql p,l p,l

l f : f PF q:q LQ p

l p,l p,l p ,ll l

L Duration Production 1 Losses Price

FuelPriceDuration Production VariableCost

Efficiency

Duration ShpCapacity Production InstalledCapacity

p ,l

m l p,l m,p mm l p

l p,l p,l pl p p

CapacityFactor

ShpEmission Duration Production EmissionFactor EmissionLimit

Duration ShpRenewables Production MinRenShare Production RenewableIndicator

KKT conditions (1)

• Derivative of the Lagrange function:

,,

: :,

, ,

,1 1 1

p f qp l

f f PF q q LQp l p

p l m p mm

p

c pl p

l

L FuelPriceVariableCost

Production Efficiency

ShpCapacity ShpEmission EmissionFactor

ShpRenewables RenewableIndicator

MSharePrice Losses C

Elasticity

,c p p,lV 0 0 Production

KKT conditions (2)

• Demand for power:

0 0lElasticity

lp,l p l l

p l

PriceProduction (1 Losses ) ReferenceDemand Price

ReferencePrice

• Capacity constraint:0 0p ,l p ,l p,l p,lInstalledCapacity CapacityFactor Production ShpCapacity

• Emission constraint:0 0m l p,l m,p m

l p

EmissionLimit Duration Production EmissionFactor ShpEmission

ScenariosScenario/case Description

Scenario:

REF Reference scenario based on market outcomes for 2008

COMP Competitive behaviour of all electricity producers

FULL1STFull strategic behaviour of the biggest electricity producer (all other producers behave as price takers)

FULL2NDFull strategic behaviour of the 2nd biggest electricity producer (all other producers behave as price takers)

ALLSAMEAll 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):

_HC20UP Hard coal prices increased by 20%_HC20DN Hard coal prices decrease by 20%_BC20UP Brown coal prices increased by 20%_BC20DN Brown coal prices decrease by 20%

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]• SO2, NOX, CO2 emissions from the power generation

sector [kt] or [Mt] • Fuel supplies to the power sector (separately for hard

coal, brown coal, etc.) [PJ]

Results

Electricity production/market price

  REF COMP FULL1ST FULL2ND ALLSAME

Electricity production [TWh]

147.9

157.8

108.6 143.1 140.7

Market price [€/MWh] 38.8 33.1 82.9 42.0 43.6

Consumer/producer surplus [M€]

  REFDifference when compared to the REF scenario:

COMP FULL1ST FULL2ND ALLSAME

Consumer Surplus 214 827 803 -5 075 -412 -624

Producer Surplus 3 016 -695 3 985 382 576

Net social surplus 217 843 108 -1 090 -30 -48

Relative change when compared to the REF scenario [%]

REF COMP FULL1ST FULL2ND ALLSAME

Electricity producton - 6.7% - 26.6% - 3.3% - 4.9%

Market price - - 14.7% 113.9% 8.2% 12.5%

Consumer surplus - 0.4% - 2.4% - 0.2% - 0.3%

Producer surplus - - 23.0% 132.1% 12.7% 19.1%

Net social surplus - 0.05% - 0.50% - 0.01% - 0.02%

SO2/NOX emissions

CO2 emissions

Fuel supplies to the power generation sector [PJ]

REF COMP FULL1ST FULL2ND ALLSAME

Hard coal 755 790 604 686 677

Brown coal 517 558 301 526 526

Natural gas 19 28 15 30 19

Renewables 18 18 18 18 18

Total 1309 1394 938 1260 1240

Fuel supplies: difference when compared with the REF scenario [PJ]

REF COMP FULL1ST FULL2ND ALLSAME

Hard coal - 34.0 -151.3 -69.4 -78.7

Brown coal - 41.3 -215.7 9.7 9.7

Natural gas - 9.3 -3.7 10.5 -0.4

Renewables - -0.3 0.2 0.0 0.2

Total - 85.3 -370.5 -49.2 -69.2

Change in CO2 emissions when compared to the REF scenario

Conclusions

Conclusions• 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.

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 CO2 emissions in the short-run– long-term model recommended

• Indirect impact of market power on the emissions levels

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, 31-261 Krakow, Poland

Backup slides

HHI

HHI (power generation sector, installed capacity)

HHI > 5000 Belgium, 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

Key KKT conditions (3)

• Renewable electricity production constraint:

0 0p,l l p p,l l

p ,l p ,l

ll

Production Duration RenewableIndicator Production Duration MinRenShare

ShpRenewablesDuration

Legend:

Scenario elementsScenario elements

Balances

Balances

VariablesVariables

DataData

Price(l)Price(l)

FuelPrice(f)

FuelPrice(f)

Production(p,l)

Production(p,l)

ShpCapacity(p,

l)

ShpCapacity(p,

l)

ShpEmission(m

)

ShpEmission(m

)

ShpFuelCapacity(s)

ShpFuelCapacity(s)

FuelSupply(s)

FuelSupply(s)

Demand(l)Demand(l)

Losses(p)Losses(p)

Profit(p,l)Profit(p,l)

Elasticity(l)Elasticity(l)

VariableCost(p)

VariableCost(p)Efficiency(p)Efficiency(p)

Emission Factor(p,m

)

Emission Factor(p,m

)

ShpRenewabl

es

ShpRenewabl

es

MarketShare(p,c)

MarketShare(p,c)

ConjecturalVariation(p,c)Conjectural

Variation(p,c)

Minimum share of Renewables

Minimum share of Renewables

Emission Limit(m)

Emission Limit(m)

Emission Constraint(

m)

Emission Constraint(

m)

Capacity Constraint(p

)

Capacity Constraint(p

)

Installed Capacity(

p)

Installed Capacity(

p)

Capacity Factor(p)

Capacity Factor(p)

Renewables

Constraint

Renewables

Constraint

Fuel Demand(f)

Fuel Demand(f)

Fuel Supplier Profit(s)

Fuel Supplier Profit(s)

FuelCost(s)FuelCost(s)Fuel Capacity

Constraint(s)

Fuel Capacity

Constraint(s)

Fuel Capacity(s

)

Fuel Capacity(s

)

Profit/demand functions

• Profit function

p l p,l p ll

f ,ql p,l p,l

l f : f PF q:q LQ p

Profit Duration Production 1 Losses Price

FuelPriceDuration Production VariableCost

Efficiency

lElasticity

lp,l p l

p l

PriceProduction 1 Losses ReferenceDemand

ReferencePrice

• Demand function

(1 )i ii

s CVL

1ii

i

LCV

s

Conjectural Variation

Company nameOwnership structure as of the end of 2010

Production of electricityDistribution

TWh [%]

PGE Polish Energy Group

69.29% - State Treasury30.71% - Other stakeholders

60.5 39% 29%

Tauron Polish Energy

41.96% - State Treasury58.04% - Other stakeholders

21.1 14% 26%

EDF Group 100% - EDF Group 15.3 10% -

Enea52.92% - State Treasury18.67% - Vattenfall AB28.41% - Other stakeholders

11.8 8% 9%

ZE PAK50% - State Treasury47.38% - Grupa Elektrim2.62% - Other stakeholders

11.5 8% -

GDF Suez 100% - GDF Suez 6.3 4% -

Energa84.19% - State Treasury15.81% - Other stakeholders

3.5 2% 16%

Vattenfall 100% - Vattenfall 4.3 3% 11%RWE Stoen 100% - RWE - - 6%Other companies - 21.2 12% 3%Total - 155.5 100% 100%

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.)

2003 2004 2005 2006 2007 20080

5

10

15

20

25

30

35

40

Installed capacity Electricity production

Mar

ket s

hare

of t

he b

igge

st p

rodu

cer

[%]

2003 2004 2005 2006 2007 20080

10

20

30

40

50

60

70

Installed capacity Electricity production

Mar

ket s

hare

of t

hree

big

gest

pro

duce

rs [%

]

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%)

HHI

2003 2004 2005 2006 2007 20080

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Installed capacity Electricity production

HH

I

Residual Supply IndexPKE

BOT GiE

PGE

2003 2004 2005 2006 2007 20080.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40Re

sidu

al S

uppl

y In

dex

of th

e la

rges

t pow

er p

rodu

cer,

199

9-20

08

Residual Supply Index

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 290.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

2003

2008

Dist

ributi

on o

f Res

idua

l Sup

ply

Inde

x in

the

Polis

h po

wer

gen

era-

tion

sect

or, s

elec

ted

year

s

Lerner Index

2003 2004 2005 2006 2007 20080.30

0.35

0.40

0.45

0.50

0.55

0.60

Brown Coal Power Plants Hard Coal Power Plants

Financial results of the Polish Energy Group

MCP

• MCP is a generalization of a pure nonlinear complementarity problem (NCP).

• In an MCP, a vector x must be determined, so that:

0 0x F x

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:

1) 0

2) 0

3) 0

i i i

i i i i

i i i

l x F x

l x u F x

x u F x