A model-based analysis of strategic consolidation in the German electricity industry

Bower, John; Bunn, Derek W.; Wattendrup, Claus

doi:10.1016/s0301-4215(01)00034-9

Cited by 58 publications

(13 citation statements)

References 3 publications

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“…Successive publications from the London Business School illustrate a trend in the field towards more complex algorithms and improved behavioural models [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Lincoln

Galloway

Stephen

et al. 2012

IEEE Trans. Power Syst.

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This version is available at https://strathprints.strath.ac.uk/33071/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model.

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“…Successive publications from the London Business School illustrate a trend in the field towards more complex algorithms and improved behavioural models [28], [29].…”

Section: Related Workmentioning

confidence: 99%

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Lincoln

Galloway

Stephen

et al. 2012

IEEE Trans. Power Syst.

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“…For example, EdF still has a monopoly in France, PowerGen (now E.ON) has a market share of about 22 percent in the UK, and the four biggest suppliers in Germany, RWE, E.ON Energie, Vattenfall Europe and EnBW, together control more than two thirds of the market (cf. Bower et al, 2001;Matthes et al, 2005). Second, assuming the production costs of green power to be the same for all producers is not compatible with certificate trading.…”

Section: Duopoly Market and Quasi-symmetric Costsmentioning

confidence: 99%

Promoting Renewable Electricity Generation in Imperfect Markets: Price vs. Quantity Policies

Madlener

Neustadt²,

Zweifel

2008

SSRN Journal

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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 AbstractThe search for economically efficient policy instruments designed to promote the diffusion of renewable energy technologies in liberalized markets has led to the introduction of quota-based tradable 'green' certificate (TGC) schemes for renewable electricity. However, there is a debate about the pros and cons of TGC, a quantity control policy, compared to guaranteed feed-in tariffs, a price control policy. In this paper we contrast these two alternatives in terms of social welfare, taking into account that electricity markets are not perfectly competitive, and show that the price control policy dominates the quantity control policy in terms of social welfare.

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“…Later they test NETA with their model [3]- [5]. In their model, the learning model of agents is modified GA. Further they utilize their model to analyze the EU electricity market, especially undertake a simulation analysis on reform strategy of Germany electric industry [6].…”

Section: A Overview Of Selective Literaturementioning

confidence: 99%

Agent-based computational economics: methodology and its application in electricity market research

Shun-kun

Yuan

2005

2005 International Power Engineering Conference

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Abstract--The restructured electricity market is a complex adaptive system and worldwide experiences show that market design is a complicated task. Recently, under the paradigm of Agent-based computational economics (ACE) a new research focus is forming and a large number of literatures are springing up, but there is still no the discussion on ACE's theoretical value and insufficiency in the research of electricity market in the hierarchy of methodology, therefore the author's research is an attempt in this aspect. By means of analyzing the evolution of economics methodology from mathematical deduction to simulation induction and their inherent relevance, the unique superiority of ACE on the level of methodology is expounded. The further selective survey on existing literatures shows that with the ACE model the marketization process can be understood clearly in deeper level and wider scope. Finally to give a reference to theoretical progress, the prospect application of ACE, especially its potential in China's electric sector restructuring is discussed.

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A model-based analysis of strategic consolidation in the German electricity industry

Cited by 58 publications

References 3 publications

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Promoting Renewable Electricity Generation in Imperfect Markets: Price vs. Quantity Policies

Agent-based computational economics: methodology and its application in electricity market research

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