Unilateral and collusive market power in the electricity pool of England and Wales

Bunn, Derek W.; Martoccia, Maria

doi:10.1016/j.eneco.2005.02.001

Cited by 44 publications

(20 citation statements)

References 12 publications

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“…Recently the described model has been applied to the analysis of market power on the electricity market in England and Wales (Bunn and Martoccia, 2005). A new direction of research seeks to extend the developed simulation platform for the analysis of the impact of crossholdings and vertical integration.…”

Section: Analysis Of the Market Power Of Utilities: Evolution To Multmentioning

confidence: 99%

Agent-based Simulation of Electricity Markets -A Literature Review-

Sensfuß

Ragwitz

Genoese

et al. 2007

Energy Studies Review

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractLiberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets.

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Section: Analysis Of the Market Power Of Utilities: Evolution To Multmentioning

confidence: 99%

Agent-based Simulation of Electricity Markets -A Literature Review-

Sensfuß

Ragwitz

Genoese

et al. 2007

Energy Studies Review

View full text Add to dashboard Cite

show abstract

“…Additionally, we graph the MAE per each type of hour per month in figure (10). The graph demonstrates the results provided in table (7) but in a more observable manner, we can see that hour 1 type which represent the peak hours experience higher error forecast with respect to other hour types especially in the month of December in which we will see later that December has the highest forecast error.…”

Section: Figure 9: Forecast Error By Hourmentioning

confidence: 59%

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Harasheh

2016

Preprint

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Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decisionmaking process.JEL Codes: C14, C45, C53, Q47.

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“…In table (7) we classify the forecasting error by each type of hour (peak, base, and off-peak) for each month of the forecast. Additionally, we graph the MAE per each type of hour per month in figure (10). The graph demonstrates the results provided in table (7) but in a more observable manner, we can see that hour 1 type which represent the peak hours experience higher error forecast with respect to other hour types especially in the month of December in which we will see later that December has the highest forecast error.…”

Section: Breakdown Of the Forecasting Errormentioning

confidence: 59%

“…(4) Peak MAPE = (12) Where in equations (9), (10) and (11) is the actual price for a certain hour, and is the corresponding forecasted price for the same hour.…”

Section: Data and Model Calibrationmentioning

confidence: 99%

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

Harasheh

2016

ESJ

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Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of the energy sector on a global scale since the late 1990s. Given these facts, this paper is an attempt to establish and demonstrate a precision based applicable forecasting model for wholesale electricity prices with respect to the Italian power market on an hourly basis. Artificial intelligence models such as neural networks and bagged regression trees are utilized, although they are rarely used to forecast electricity prices. After model calibration, bagged regression trees with exogenous variables comprised the final model. The selected model outperformed neural network and bagged regression with a single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm certain theoretical specifications of the model. As a policy tool, this model could be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

show abstract

Unilateral and collusive market power in the electricity pool of England and Wales

Cited by 44 publications

References 12 publications

Agent-based Simulation of Electricity Markets -A Literature Review-

Agent-based Simulation of Electricity Markets -A Literature Review-

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

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