Market Power in Power Markets: The Case of French Wholesale - - PowerPoint PPT Presentation

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Context Literature review Modeling market power Data Estimation results Conclusion Market Power in Power Markets: The Case of French Wholesale Electricity Market Sophie MERITET Thao PHAM Centre of Geopolitics of Energy and Raw Materials

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Context Literature review Modeling market power Data Estimation results Conclusion

Market Power in Power Markets: The Case of French Wholesale Electricity Market

Sophie MERITET Thao PHAM

Centre of Geopolitics of Energy and Raw Materials (CGEMP), Universit´ e Paris Dauphine

26th of October, 2015

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Context Literature review Modeling market power Data Estimation results Conclusion

Outline

1

Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

1

Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

Context

Electricity reforms in Europe since the late 1990s: unbundling, market opening, deregulation. Creation of market and price liberalization.

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Context Literature review Modeling market power Data Estimation results Conclusion

Wholesale electricity price year-ahead base load in EUR/MWh 2000-2007

Source: European Commission, Energy Sector Inquiry (2007) 5 / 24

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Evolution of French electricity prices and tariffs 2000-2013

Source: Authors, based on data from EPEX 6 / 24

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Context Literature review Modeling market power Data Estimation results Conclusion

Weekly prices and volumes on electricity spot market in France (2005-2013)

Source: Authors, based on data from EPEX, CRE, Eurostat 7 / 24

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Context Literature review Modeling market power Data Estimation results Conclusion

France’s case:

Highly concentrated market structure despite of the electricity reform Substantial increases in wholesale power prices since the liberalization Very little empirical studies on market power issues in France

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Context Literature review Modeling market power Data Estimation results Conclusion

1

Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

Literature review on diagnosing market power in power markets

Structure approaches:

Market share and HHI: Schmalensee and Golub [1984]; Economic Londons [2007] Pivotal Supplier & Residual Supply Indicator: Sheffrin [2002] Residual demand Analysis: Wolak [2000, 2003]

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Context Literature review Modeling market power Data Estimation results Conclusion

Literature review on diagnosing market power in power markets

Structure approaches:

Market share and HHI: Schmalensee and Golub [1984]; Economic Londons [2007] Pivotal Supplier & Residual Supply Indicator: Sheffrin [2002] Residual demand Analysis: Wolak [2000, 2003]

Market simulation approaches:

Supply function equilibrium model: Green and Newbery [1992]; Willems et .al [2005] Cournot/Nash models: Borenstein, Bushnell & Wolak [1999 – 2002] Optimization models: Lang and Schwarz [2006]; Weigt and Von Hirschhausen [2008] New Empirical Industrial Organisation: Hjalmarsson [2000]; Bergland et Mirza [2012]

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Context Literature review Modeling market power Data Estimation results Conclusion

1

Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

GMM model in panel dataset

Following Bresnahan [1982], Lau [1982] and Hjalmarsson [2000] Demand equation: Qht = α0+

k

  • i=1

γiQh,t−i +

k

  • i=0

αP,iPh,t−i +

k

  • i=0

αZ,iZh,t−i +

k

  • i=0

αPZ,iPZh,t−i +εht (1) with εht = µh + υht (2)

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Context Literature review Modeling market power Data Estimation results Conclusion

GMM model in panel dataset

Following Bresnahan [1982], Lau [1982] and Hjalmarsson [2000] Demand equation: Qht = α0+

k

  • i=1

γiQh,t−i +

k

  • i=0

αP,iPh,t−i +

k

  • i=0

αZ,iZh,t−i +

k

  • i=0

αPZ,iPZh,t−i +εht (1) with εht = µh + υht (2) Supply relation: Pht = β0 +

k

  • i=1

φiPh,t−i +

k

  • i=0

βQ,iQh,t−i +

k

  • i=0

βW ,iWh,t−i +

k

  • i=0

λiQ∗

h,t−i + ηht

(3) with ηht = νh + τht (4) and Q∗

ht =

Qht (θP + θPZ Zht) ; θj = k

i=0 αj, i

1 − k

i=1 γi

j = P, Y , Z, PZ (5)

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Context Literature review Modeling market power Data Estimation results Conclusion

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Context

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Literature review

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Modeling market power

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Data

5

Estimation results

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Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

Summary statistics for sample variables

Unit Mean Median

  • Std. Dev.

Skewness Kurtosis Turnover GW 6.394527 6.266 1.350896 0.562295 3.463442 Temperature Celcius 12.52322 12.85 6.405129

  • 0.253489

2.252443 Daylength hours 12.06426 12.173 2.711247

  • 0.007733

1.580447 Electricity Price EUR/MWh 46.13638 46.252 18.98393 4.235954 109.3078 Gas Price EUR /MWh 19.65497 22.01 5.703643

  • 0.487586

1.784082 Carbon Price EUR /t CO2 11.81185 12.95 3.411035

  • 0.67926

2.701959 Load MW 55.63161 53.7 12.72469 0.571252 2.714237 Capacity Margin MW 7065.713 6610.8 2501.517 1.05195 4.748406 EX Germany MW

  • 694.595
  • 857

1702.846 0.193937 1.891437 EX Spain MW 151.1926 694.7847 0.210961 1.832861 EX Belgium MW 377.991 300 1113.764 0.310391 2.243382 EX Italy MW 1872.296 2187 737.7182

  • 0.785028

2.84714 The whole sample spans from 26 October 2009 to 31 December 2012, yielding T = 1163 for each hour and 27912

  • bservations for the whole panel dataset.

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Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

Estimation results for the Demand Equation

Variables coef Robust Std.Err z-stat Prob. [95% Conf. Interval] Price

  • 0.0210***

(0.00789)

  • 2.662

0.0077

  • 0.0365
  • 0.00554

Temperature

  • 0.130***

(0.0416)

  • 3.129

0.0017

  • 0.212
  • 0.0487

P ∗ Temp 0.00196*** (0.000674) 2.910 0.0036 0.000640 0.00328 Turnover(-1) 0.522*** (0.0195) 26.84 0.0000 0.484 0.561 Daylength 0.00813 (0.0201) 0.404 0.6860

  • 0.0313

0.0476 Holidays

  • 0.249***

(0.0766)

  • 3.251

0.0011

  • 0.399
  • 0.0989

Summer

  • 0.147

(0.117)

  • 1.256

0.2090

  • 0.375

0.0822 Spring

  • 0.258***

(0.0859)

  • 3.003

0.0026

  • 0.426
  • 0.0896

Fall

  • 0.0929

(0.0738)

  • 1.258

0.2080

  • 0.238

0.0518 18 / 24

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Context Literature review Modeling market power Data Estimation results Conclusion

Estimation results for the Supply Equation

Variable coef Robust Std.Err z-stat Prob. [95% Conf. Interval] Q∗ 1.33e-05*

  • 7.46E-06

1.787 0.0871

  • 2.10E-06

2.88E-05 Load 0.346***

  • 0.051

6.797 0.0000 0.241 0.452 Load2 0.00130***

  • 0.000309

4.192 0.0003 0.000657 0.00194 Price (-1) 0.276***

  • 0.0287

9.623 0.0000 0.216 0.335 Price (-2) 0.0783***

  • 0.0155

5.066 0.0000 0.0463 0.11 Price (-3) 0.0603***

  • 0.00987

6.106 0.0000 0.0399 0.0807 Price (-4) 0.0533***

  • 0.0082

6.498 0.0000 0.0363 0.0703 Price (-5) 0.00692

  • 0.0106

0.652 0.5210

  • 0.015

0.0289 Price (-6) 0.0350***

  • 0.00999

3.5 0.0019 0.0143 0.0556 Price (-7) 0.115***

  • 0.0202

5.683 0.0000 0.0732 0.157 Gas price 0.142

  • 0.116

1.226 0.2330

  • 0.0974

0.381 Margin

  • 0.629***
  • 0.0569
  • 11.05

0.0000

  • 0.747
  • 0.511

Carbon

  • 0.0241
  • 0.0191
  • 1.259

0.2210

  • 0.0636

0.0155 EX Germany 0.180***

  • 0.0625

2.879 0.0085 0.0507 0.309 EX Italy

  • 1.887***
  • 0.302
  • 6.239

0.0000

  • 2.513
  • 1.261

EX Spain

  • 0.960***
  • 0.233
  • 4.123

0.0004

  • 1.442
  • 0.478

EX Belgium 0.278***

  • 0.0814

3.409 0.0024 0.109 0.446 Holidays

  • 6.935***
  • 1.546
  • 4.486

0.0002

  • 10.13
  • 3.737

Summer 7.007***

  • 0.358

19.57 0.0000 6.267 7.748 Spring 5.341***

  • 0.28

19.09 0.0000 4.763 5.92 Fall 7.715***

  • 0.379

20.37 0.0000 6.931 8.498 19 / 24

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Context Literature review Modeling market power Data Estimation results Conclusion

Robustness test: Multivariate time series models Lerner index across hours

LI-h5 LI-h8 LI-h9 LI-h11 LI-h22 Short term 0.01963641 0.02803341 0.04188475 0.01715386 4.7438E-07 Long term 0.0446282 0.03467772 0.02644286 0.01000519 8.7443E-07 20 / 24

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Context Literature review Modeling market power Data Estimation results Conclusion

Discussion

Extremely regulated market in France. No economic incentives for incumbent firm to exercise its market power High prices can be justified by other exogenous factors.

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Context

2

Literature review

3

Modeling market power

4

Data

5

Estimation results

6

Conclusion

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Context Literature review Modeling market power Data Estimation results Conclusion

Conclusion

Market power parameters are found statistically significant for several peak hours but remain at very low levels. On average, no market power was exercised during the examined period (2009-2012) in France. Various economic justifications for this result Implications for further research

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Context Literature review Modeling market power Data Estimation results Conclusion

Thank you

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