Performance-propagating strategy framework for autonomous power trading

The Power Trading Agent Competition is a simulation platform for autonomous trading agents to operate between the wholesale market and the retail market, acting as the middleman who buys energy from the wholesale market and resells it to consumers in the retail market in the form of contracts. Many...

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Bibliographic Details
Main Author: Chen, Yongwei
Other Authors: Bo An
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/147894
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1478942021-04-16T05:56:00Z Performance-propagating strategy framework for autonomous power trading Chen, Yongwei Bo An School of Computer Science and Engineering boan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling The Power Trading Agent Competition is a simulation platform for autonomous trading agents to operate between the wholesale market and the retail market, acting as the middleman who buys energy from the wholesale market and resells it to consumers in the retail market in the form of contracts. Many research have been conducted to understand what types of strategies allow autonomous agents to earn the most profits in the long run. Some of the proven strategies focus on optimizing individual performance in each market and not the overall strategy, even though the performance in markets might be interrelated. It is known that the performance of a trading agent in one market affects its performance in another market, yet the interrelationship between the two markets is not well-explored due to the complex nature of it. Therefore, the research question for this work is: ”Can the complexity of multiple markets be broken down into simpler machine learning problems to be used to assemble a holistic and coherent agent in the PowerTAC environment?” This work develops a performance-propagating strategy framework that can be used by many brokers to incorporate a holistic strategy. It involves analysing the relationships within each market and between each market, and then developing a strategy for each relationship in view of the propagating effect. Empirical results from this work have discovered out that while agents deploying a performance-propagating strategy is less effective in sustaining profitability when there are only a few competing brokers, agents with a performance-propagating strategy tends to outperform other competing brokers when more brokers are in the market. Therefore, the performance-propagating strategy framework is a viable framework that can be applied to any other agent’s current individual strategies to improve the total profitability of the agent. Bachelor of Engineering (Computer Science) 2021-04-16T05:56:00Z 2021-04-16T05:56:00Z 2021 Final Year Project (FYP) Chen, Y. (2021). Performance-propagating strategy framework for autonomous power trading. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147894 https://hdl.handle.net/10356/147894 en SCSE20-0253 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Chen, Yongwei
Performance-propagating strategy framework for autonomous power trading
description The Power Trading Agent Competition is a simulation platform for autonomous trading agents to operate between the wholesale market and the retail market, acting as the middleman who buys energy from the wholesale market and resells it to consumers in the retail market in the form of contracts. Many research have been conducted to understand what types of strategies allow autonomous agents to earn the most profits in the long run. Some of the proven strategies focus on optimizing individual performance in each market and not the overall strategy, even though the performance in markets might be interrelated. It is known that the performance of a trading agent in one market affects its performance in another market, yet the interrelationship between the two markets is not well-explored due to the complex nature of it. Therefore, the research question for this work is: ”Can the complexity of multiple markets be broken down into simpler machine learning problems to be used to assemble a holistic and coherent agent in the PowerTAC environment?” This work develops a performance-propagating strategy framework that can be used by many brokers to incorporate a holistic strategy. It involves analysing the relationships within each market and between each market, and then developing a strategy for each relationship in view of the propagating effect. Empirical results from this work have discovered out that while agents deploying a performance-propagating strategy is less effective in sustaining profitability when there are only a few competing brokers, agents with a performance-propagating strategy tends to outperform other competing brokers when more brokers are in the market. Therefore, the performance-propagating strategy framework is a viable framework that can be applied to any other agent’s current individual strategies to improve the total profitability of the agent.
author2 Bo An
author_facet Bo An
Chen, Yongwei
format Final Year Project
author Chen, Yongwei
author_sort Chen, Yongwei
title Performance-propagating strategy framework for autonomous power trading
title_short Performance-propagating strategy framework for autonomous power trading
title_full Performance-propagating strategy framework for autonomous power trading
title_fullStr Performance-propagating strategy framework for autonomous power trading
title_full_unstemmed Performance-propagating strategy framework for autonomous power trading
title_sort performance-propagating strategy framework for autonomous power trading
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/147894
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