The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions

This research work develops a novel framework for experimental designs of liberalised wholesale power markets, namely the Agent-based Computational Economics of Wholesale Electricity Market (ACEWEM) framework. The ACEWEM allows to further understand the effect of various market designs on market eff...

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Huvudupphovsman: Kiose, Daniil
Materialtyp: Lärdomsprov
Språk:English
Publicerad: 2015
Ämnen:
Länkar:https://repository.londonmet.ac.uk/1257/1/Daniil%20Kiose%20-%20Final%20PhDThesis.pdf
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author Kiose, Daniil
author_facet Kiose, Daniil
author_sort Kiose, Daniil
collection LMU
description This research work develops a novel framework for experimental designs of liberalised wholesale power markets, namely the Agent-based Computational Economics of Wholesale Electricity Market (ACEWEM) framework. The ACEWEM allows to further understand the effect of various market designs on market efficiency and to gain insights into market manipulation by electricity generators. The thesis describes a detailed market simulations whereby the strategies of power generators emerge as a result of a stochastic profit optimisation learning algorithm based upon the Generalized Additive Models for Location Scale and Shape statistical framework. The ACEWEM framework, which integrates the agent-based modelling paradigm with formal statistical methods to represent better real-world decision rules, is designed to be the foundation for large custom-purpose experimental studies inspired by computational learning. It makes a methodological contribution in the development of an expert computational laboratory for repeated power auctions with capacity and physical constraints. Furthermore, it contributes by developing a new computational learning algorithm. It integrates the reinforcement learning paradigm to engage past experience in decision making, with flexible statistical models adjust these decisions based on the vision of the future. In regard to policy contribution, this research work conducts a simulation study to identify whether high market prices can be ascribed to problems of market design and/or exercise of market power. Furthermore, the research work presents the detailed study of an abstract wholesale electricity market and real UK power market.
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spelling oai:repository.londonmet.ac.uk:12572017-08-23T14:56:57Z https://repository.londonmet.ac.uk/1257/ The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions Kiose, Daniil 330 Economics 380 Commerce, communications & transportation This research work develops a novel framework for experimental designs of liberalised wholesale power markets, namely the Agent-based Computational Economics of Wholesale Electricity Market (ACEWEM) framework. The ACEWEM allows to further understand the effect of various market designs on market efficiency and to gain insights into market manipulation by electricity generators. The thesis describes a detailed market simulations whereby the strategies of power generators emerge as a result of a stochastic profit optimisation learning algorithm based upon the Generalized Additive Models for Location Scale and Shape statistical framework. The ACEWEM framework, which integrates the agent-based modelling paradigm with formal statistical methods to represent better real-world decision rules, is designed to be the foundation for large custom-purpose experimental studies inspired by computational learning. It makes a methodological contribution in the development of an expert computational laboratory for repeated power auctions with capacity and physical constraints. Furthermore, it contributes by developing a new computational learning algorithm. It integrates the reinforcement learning paradigm to engage past experience in decision making, with flexible statistical models adjust these decisions based on the vision of the future. In regard to policy contribution, this research work conducts a simulation study to identify whether high market prices can be ascribed to problems of market design and/or exercise of market power. Furthermore, the research work presents the detailed study of an abstract wholesale electricity market and real UK power market. 2015-04 Thesis NonPeerReviewed text en https://repository.londonmet.ac.uk/1257/1/Daniil%20Kiose%20-%20Final%20PhDThesis.pdf Kiose, Daniil (2015) The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions. Doctoral thesis, London Metropolitan University.
spellingShingle 330 Economics
380 Commerce, communications & transportation
Kiose, Daniil
The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title_full The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title_fullStr The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title_full_unstemmed The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title_short The ACEWEM computational laboratory : an integrated agent-based and statistical modelling framework for experimental designs of repeated power auctions
title_sort acewem computational laboratory an integrated agent based and statistical modelling framework for experimental designs of repeated power auctions
topic 330 Economics
380 Commerce, communications & transportation
url https://repository.londonmet.ac.uk/1257/1/Daniil%20Kiose%20-%20Final%20PhDThesis.pdf
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