Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach

With the continuous development of the electricity market, the electricity retailers, as the intermediaries between producers and consumers, have emerged in some of the liberalized electricity markets. Meanwhile, the electricity retailer faces many increasingly significant challenges from the comple...

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Main Authors: Liu, Yuankun, Zhang, Dongxia, Gooi, Hoay Beng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/153983
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1539832022-01-24T08:23:23Z Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach Liu, Yuankun Zhang, Dongxia Gooi, Hoay Beng School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Artificial Intelligence Electricity Market With the continuous development of the electricity market, the electricity retailers, as the intermediaries between producers and consumers, have emerged in some of the liberalized electricity markets. Meanwhile, the electricity retailer faces many increasingly significant challenges from the complexities and uncertainties in both the supply and consumption sides. This paper applies a data-driven decision-making strategy via Advantage Actor-Critic (A2C) and Deep Q-Learning (DQN) for the electricity retailers. The retailers' profits and consumers' costs are both taken into account. This study verifies that the applied data-driven methods can handle the decision-making problem as well as promote the profitability of retailers in the electricity market. Furthermore, A2C is more appropriate than DQN in our simulation. The effectiveness of the applied datadriven methods is validated by using real-world data. Published version 2022-01-24T08:23:23Z 2022-01-24T08:23:23Z 2021 Journal Article Liu, Y., Zhang, D. & Gooi, H. B. (2021). Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach. CSEE Journal of Power and Energy Systems, 7(2), 358-367. https://dx.doi.org/10.17775/CSEEJPES.2019.02510 2096-0042 https://hdl.handle.net/10356/153983 10.17775/CSEEJPES.2019.02510 2-s2.0-85103254909 2 7 358 367 en CSEE Journal of Power and Energy Systems © 2019 CSEE. This is an open-access article distributed under the terms of the Creative Commons Attribution License. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Artificial Intelligence
Electricity Market
spellingShingle Engineering::Electrical and electronic engineering
Artificial Intelligence
Electricity Market
Liu, Yuankun
Zhang, Dongxia
Gooi, Hoay Beng
Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
description With the continuous development of the electricity market, the electricity retailers, as the intermediaries between producers and consumers, have emerged in some of the liberalized electricity markets. Meanwhile, the electricity retailer faces many increasingly significant challenges from the complexities and uncertainties in both the supply and consumption sides. This paper applies a data-driven decision-making strategy via Advantage Actor-Critic (A2C) and Deep Q-Learning (DQN) for the electricity retailers. The retailers' profits and consumers' costs are both taken into account. This study verifies that the applied data-driven methods can handle the decision-making problem as well as promote the profitability of retailers in the electricity market. Furthermore, A2C is more appropriate than DQN in our simulation. The effectiveness of the applied datadriven methods is validated by using real-world data.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Liu, Yuankun
Zhang, Dongxia
Gooi, Hoay Beng
format Article
author Liu, Yuankun
Zhang, Dongxia
Gooi, Hoay Beng
author_sort Liu, Yuankun
title Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
title_short Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
title_full Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
title_fullStr Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
title_full_unstemmed Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
title_sort data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
publishDate 2022
url https://hdl.handle.net/10356/153983
_version_ 1723453410814984192