Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153983
Title: Data-driven decision-making strategies for electricity retailers : a deep reinforcement learning approach
Authors: Liu, Yuankun
Zhang, Dongxia
Gooi, Hoay Beng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: 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
Journal: CSEE Journal of Power and Energy Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/153983
ISSN: 2096-0042
DOI: 10.17775/CSEEJPES.2019.02510
Rights: © 2019 CSEE. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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