european electricity day ahead market

163 Pages·2014·2.96 MB·English

by  Trabucchi Paolo

Università degli Studi di Bergamo
Department of Engineering
Ph.D. in Economics and Management of Technology
XXVII Cohort

EUROPEAN ELECTRICITY DAY AHEAD MARKET

A MULTIPLE TIME SERIES APPROACH

Doctoral Dissertation

Marta Trabucchi

Supervisors: Prof. Luigi Buzzacchi
Prof. Pia Saraceno

November 2014
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Abstract

The energy market reform of the last decades is a complex restructuring process that first has
opened up Member State electricity markets to competition and it gradually fosters them toward
integration into the Single European Market. Even if national markets are still characterized by
several differences in the production structures, regulation shapes a common market design at
European level and voluntary measures have been adopted to promote market integration. The
recent empirical literature highlights the presence of cointegration at least among the day ahead
electricity markets of Central Western Europe. In this framework, Power Exchanges have taken a
key role as shown by the growing volumes traded on their different segments and in recent years
electricity price forecasting has become an interesting research field. However, up to now, most of
the contributions on short term forecasting of day ahead electricity prices do not include the
possibility of dynamic interactions between several interconnected electricity markets. After a
primer on the economics of electricity markets and the analysis of the regulatory and market
framework, the present work proposes a multiple time series approach for electricity price
forecasting, joining the two strands of empirical literature on market integration and day ahead price
forecasting. Accounting for the presence of market integration enlarges the model information set,
so it may potentially improve the forecasting performance.
This thesis considers hourly day ahead electricity prices for eight European countries (Austria,
Belgium, France, Germany, Italy, Netherlands, Slovenia and Switzerland) for the period May 2010–
July 2013. Multiple time series models have been used to forecast electricity prices for all the
markets and an in-depth comparison between their accuracy and the one of simple time series
models has been provided. At present the implemented forecasting exercise does not allow stating
that estimating multiple time series models, and especially including potential cointegration
relationships between day ahead electricity prices, greatly improve their forecasting performances
compared to simple time series models. The adoption of multiple time series may lead to better
results only in some hours and in other hours, simple time series models outperform multiple time
series ones (especially ramp- up hours in the morning).

Keywords: European electricity markets, electricity prices, forecasting, electricity market
integration, multiple time series models

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Acknowledgments

This work has benefit from the support of several people. I gratefully acknowledge Luigi Buzzacchi,
Pia Saraceno and Laura Solimene for their guidance and for insightful comments on this thesis. I
am deeply in debt to Michele Dalena for patient day by day discussion and essential suggestions.
I wish to express gratitude to all the members of the Istituto di Economia e Strategie d’impresa of
the Catholic University of Milan: they pushed me on this road and constant support me during my
doctoral studies. I would like to thanks also Pippo Ranci, who first taught me Energy Economics.
Thanks also go to my PhD colleagues and all the Faculty Members of the Doctoral Programs in
Economics and Management of Technology of the University of Bergamo.
Finally, I heartily thank all my family and especially Carlo that every day reminds me that I “will
never walk alone”.

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Table of contents

Introduction ........................................................................................................................................ 13
1 A primer on the economics of electricity market ........................................................................ 18
1.1 Electricity technical and economic features ........................................................................ 19
1.2 Market design ...................................................................................................................... 23
1.2.1 Energy transactions ...................................................................................................... 24
1.2.2 System Operations ....................................................................................................... 28
2 The regulatory Framework .......................................................................................................... 34
2.1 The electricity liberalization era .......................................................................................... 34
2.2 The European electricity reform .......................................................................................... 36
2.2.1 Toward a market based industry .................................................................................. 37
2.2.2 Toward the Single European Market ........................................................................... 40
2.3 The Electricity Target Model .............................................................................................. 43
2.3.1 Day ahead market coupling .......................................................................................... 43
2.3.2 Target Model for intraday, forward and real time timeframes .................................... 45
3 The market framework ................................................................................................................ 48
3.1 National electricity generation capacity .............................................................................. 48
3.2 Explorative analysis of wholesale markets ......................................................................... 51
3.2.1 Market liquidity............................................................................................................ 52
3.2.2 Price convergence ........................................................................................................ 53
4 Literature review ......................................................................................................................... 56
4.1 Forecasting electricity prices ............................................................................................... 56
4.2 Electricity markets integration in Europe ............................................................................ 60
5 Dataset description ...................................................................................................................... 63
Appendix A ........................................................................................................................................ 68
6 Methods ....................................................................................................................................... 76

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6.1 An introduction to Vector Autoregressive Models for Multivariate Time Series ............... 76
6.2 Model specification ............................................................................................................. 79
6.2.1 Unit root and stationarity tests ..................................................................................... 79
6.2.2 The models implemented ............................................................................................. 84
Appendix B ........................................................................................................................................ 92
7 Day ahead electricity price forecasting ....................................................................................... 94
7.1 Short term forecasting ......................................................................................................... 94
7.1.1 Models setting .............................................................................................................. 94
7.1.2 Results .......................................................................................................................... 97
7.1.3 Conclusion ................................................................................................................. 105
7.2 Pre-filtered short term forecasting ..................................................................................... 107
7.2.1 Spike detection and substitution ................................................................................ 107
7.2.2 Filtered dataset description ........................................................................................ 108
7.2.3 Models settings .......................................................................................................... 111
7.2.4 Results ........................................................................................................................ 119
7.2.5 Conclusion ................................................................................................................. 125
7.3 Scenario based conditional forecasting ............................................................................. 126
Appendix C ...................................................................................................................................... 129
Conclusion and further developments .............................................................................................. 153
References ........................................................................................................................................ 155

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List of Figures

Figure 1.1: European Electricity Production (TWh) - 2013 ............................................................. 18
Figure 1.2: Hourly load values for Italy (MW) ................................................................................. 20
Figure 1.3: Hourly load (left) and Load duration curve (right)......................................................... 21
Figure 1.4: Optimal generation mix .................................................................................................. 22
Figure 1.5: Timeline of electricity transactions ................................................................................ 24
Figure 1.6: Market clearing price ...................................................................................................... 25
Figure 2.1: Main Steps in Electricity Reform ................................................................................... 35
Figure 2.2: EU Electricity Directives ................................................................................................ 42
Figure 3.1: Net generating capacity mix - 2013 ................................................................................ 49
Figure 3.2: RES plant evolution in net generating capacity mix (%) ............................................... 51
Figure 3.3: Wholesale market liquidity (%) ...................................................................................... 52
Figure 4.1: Empirical literature on price forecasting ........................................................................ 57
Figure 4.2: Empirical literature on European market integration (cointegration) ............................ 62
Figure 5.1: Average price by countries (May, 11th 2010 – July, 29th 2013) ..................................... 64
Figure 5.2: Average hourly load by countries in 2012 (MW) .......................................................... 66
Figure 7.1: Average hourly RMSE (€/MWh) ................................................................................... 97
Figure 7.2: Supply and demand structure ......................................................................................... 98
Figure 7.3: Hourly multiple times series model vs simple time series model ................................ 104
Figure 7.4: Hourly average Delta RMSE ........................................................................................ 105
Figure 7.5: EPEX France Spot price time series (Hour 10th) .......................................................... 109
Figure 7.6: Standard deviation by countries on the original (a) and on the filtered (b) dataset ..... 111
Figure 7.7: Average hourly RMSE (Pre-filtered dataset) ............................................................... 119
Figure 7.8: Hourly average Delta RMSE (Pre-filtered dataset) ...................................................... 125
Figure 7.9: Price changes across scenarios (VEC-X) ..................................................................... 128

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List of Tables

Table 1.1: Electricity demand by region and scenario (TWh) .......................................................... 19
Table 3.1: Net generating capacity (MW) ......................................................................................... 49
Table 3.2: Wholesale price convergence 2010-2013 ........................................................................ 54
Table 5.1: Descriptive statistics EPEX France price ......................................................................... 65
Table A.1: Descriptive statistics EXAA price ................................................................................... 68
Table A.2: Descriptive statistics Belpex price .................................................................................. 69
Table A.3: Descriptive statistics EPEX Germany price .................................................................... 69
Table A.4: Descriptive statistics IPEX price ..................................................................................... 70
Table A.5: Descriptive statistics APX price ...................................................................................... 70
Table A.6: Descriptive statistics BSP price....................................................................................... 71
Table A.7: Descriptive statistics EPEX Switzerland price ................................................................ 71
Table A.8: Descriptive statistics Austrian Load ................................................................................ 72
Table A.9: Descriptive statistics Belgian Load ................................................................................. 72
Table A.10: Descriptive statistics French Load ................................................................................ 73
Table A.11: Descriptive statistics German Load ............................................................................... 73
Table A.12: Descriptive statistics Italian Load ................................................................................. 74
Table A.13: Descriptive statistic Dutch Load ................................................................................... 74
Table A.14: Descriptive statistics Slovenian Load ........................................................................... 75
Table A.15: Descriptive Statistics Swiss Load ................................................................................. 75
Table 6.1: Augmented Dickey-Fuller test ......................................................................................... 81
Table 6.2: Phillips-Perron test ........................................................................................................... 81
Table 6.3: Kwiatkowsky-Phillips-Schmidt-Shin test ........................................................................ 82
Table 6.4: Lag selection VAR models .............................................................................................. 85
Table 6.5: Cointegration rank ............................................................................................................ 89
Table B.1: Lag selection VAR-X models ......................................................................................... 92
Table 7.1: The average SMAPE errors in percentages for all the hours of the day (%) ................... 99
Table 7.2: SMAPE errors from AR and VAR models (%) ............................................................. 100
Table 7.3: SMAPE errors from AR-X and VAR-X models (%)..................................................... 101
Table 7.4: SMAPE errors from ARI and VEC models (%) ............................................................ 102
Table 7.5: SMAPE errors from ARI-X and VEC-X models (%) ................................................... 103
Table 7.6: Summary statistics for EPEX France price (Pre-filtered dataset) .................................. 110

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Table 7.7: Augmented Dickey- Fuller test (Pre-filtered dataset) .................................................... 112
Table 7.8: Phillips-Perron Test (Pre-filtered dataset) ...................................................................... 113
Table 7.9: Kwiatkowsky-Phillips-Schmidt-Shin test (Pre-filtered dataset) .................................... 113
Table 7.10: Lag selection for VAR models estimated (Pre-filtered dataset) .................................. 115
Table 7.11: Cointegration rank (Pre-filtered dataset) ...................................................................... 117
Table 7.12: The average SMAPE errors for all the hours of the day (%) (Pre-filtered dataset) ..... 120
Table 7.13: SMAPE errors from AR and VAR models (Pre-filtered dataset) ................................ 121
Table 7.14: SMAPE errors from AR-X and VAR-X models (Pre-filtered dataset) ....................... 122
Table 7.15: SMAPE errors from ARI and VEC models (Pre-filtered dataset) ............................... 123
Table 7.16: SMAPE errors from ARI-X and VEC-X models (Pre-filtered dataset) ....................... 124
Table 7.17: Average monthly price values (VEC-X model) – August, 2013 (€/MWh) ................. 127
Table 7.18: Price changes across scenarios (VEC-X) ..................................................................... 128
Table C.1: MAPE errors from AR and VAR models (%) .............................................................. 130
Table C.2: MAPE errors from AR-X and VAR-X models (%) ...................................................... 131
Table C.3: MAPE errors from ARI and VEC models (%) ............................................................. 132
Table C.4: MAPE errors from ARI-X and VEC-X models (%) ..................................................... 133
Table C.5: RMSE errors from AR and VAR models...................................................................... 134
Table C.6: RMSE errors from AR-X and VAR-X models ............................................................. 135
Table C.7: RMSE errors from ARI and VEC models .................................................................... 136
Table C.8: RMSE errors from ARI-X and VEC-X models ............................................................ 137
Table C.9: Descriptive statistics EXAA price (Pre-filtered dataset) ............................................... 138
Table C.10: Descriptive statistics BELPEX price (Pre-filtered dataset) ......................................... 138
Table C.11: Descriptive statistics EPEX Germany price (Pre-filtered dataset) .............................. 139
Table C.12: Descriptive statistics IPEX price (Pre-filtered dataset) ............................................... 139
Table C.13: Descriptive statistics APX price (Pre-filtered dataset) ................................................ 140
Table C.14: Descriptive statistics BSP price (Pre-filtered dataset) ................................................. 140
Table C.15: Descriptive statistics EPEX Switzerland price (Pre-filtered dataset) .......................... 141
Table C.16: Lag selection VAR-X models (Pre-filtered dataset) ................................................... 142
Table C.17: MAPE errors from AR and VAR models (%) (Pre-filtered dataset) .......................... 144
Table C.18: MAPE errors from AR-X and VAR-X models (%) (Pre-filtered dataset) .................. 145
Table C.19: MAPE errors from ARI and VEC models (%) (Pre-filtered dataset) ......................... 146
Table C.20: MAPE errors from ARI-X and VEC-X models (%) (Pre-filtered dataset) ................. 147
Table C.21: RMSE errors from AR and VAR models (Pre-filtered dataset).................................. 148

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Table C.22: RMSE errors from AR-X and VAR-X models (Pre-filtered dataset) ......................... 149
Table C.23: RMSE errors from ARI and VEC models (Pre-filtered dataset) ................................ 150
Table C.24: RMSE errors from ARI-X and VEC-X models (Pre-filtered dataset) ........................ 151
Table C.25: Average monthly price values (VAR-X model) – August, 2013 (€/MWh)................ 152
Table C.26: Price changes across scenarios (VAR-X).................................................................... 152

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