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DSM pricing method based on A3C and LSTM under cloud-edge environment

Sun, Fangyuan, Kong, Xiangyu, Wu, Jianzhong ORCID:, Gao, Bixuan, Chen, Ke and Lu, Ning 2022. DSM pricing method based on A3C and LSTM under cloud-edge environment. Applied Energy 315 , 118853. 10.1016/j.apenergy.2022.118853

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Demand-side management (DSM) could realize “peak cutting and valley filling” of power load and improve the stability and efficiency of power system. With the development of information systems, more smart devices are being deployed on the demand side. It has become a challenge for DSM service providers to take full use of the edge computing capacity and demand-side information to improve the accuracy of DSM decision-making, without revealing user privacy. This paper proposes a distributed DSM pricing method for service provider, based on asynchronous advantage actor-critic (A3C) algorithm and long short-term memory (LSTM) network under cloud-edge environment. The on-site utilization of user information is realized through distributed training and centralized decision-making structure of A3C algorithm. The training process is accelerated by LSTM based virtual environment, which greatly reduces the training cost of the algorithm. Case study results shows that the proposed method is able to make pricing decision for DSM service provider under cloud-edge environment. Moreover, through the combination of LSTM based virtual environment and A3C algorithm, the proposed method requires less historical data than other methods and improves the profit of service providers.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0306-2619
Date of First Compliant Deposit: 4 May 2022
Date of Acceptance: 25 February 2022
Last Modified: 08 Apr 2023 16:21

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