Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2640-2646,2659

Real-time pricing strategy based on reinforcement learning with load uncertainty

Wang Jingqi
Gao Yan
Wu Zhiqiang
Li Renjie
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2022.02.0069

Abstract

Facing the current situation of load uncertainty, new energy grid integration, and dual carbon goal in the power system, this paper established a real-time pricing model considering load uncertainty and carbon trading in the context of the smart grid with full consideration of the welfare of both supply side and user side. Based on the advantages that reinforcement learning could handle variable complexity, non-convex, and nonlinear problems, this paper used the Q-learning algorithm in reinforcement learning to solve the model iteratively. Firstly, this paper transformed the real-time interaction process between the user and the power supplier into a Markov decision process corresponding to the reinforcement learning framework. Secondly, the process represented the information interaction between the user and the power supplier as the iterative exploration of the agent in a dynamic environment. Finally, this paper found the optimal value by the Q-learning algorithm in reinforcement learning, i. e., the maximal social welfare value. The simulation results show that the proposed real-time pricing strategy can effectively enhance social welfare and reduce total carbon emissions, which verifies the feasibility and effectiveness of the proposed model and algorithm.

Foundation Support

国家自然科学基金资助项目(72071130)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.02.0069
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Algorithm Research & Explore
Pages: 2640-2646,2659
Serial Number: 1001-3695(2022)09-012-2640-07

Publish History

[2022-05-06] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

王菁祺, 高岩, 吴志强, 等. 计及负荷不确定性的强化学习实时定价策略 [J]. 计算机应用研究, 2022, 39 (9): 2640-2646,2659. (Wang Jingqi, Gao Yan, Wu Zhiqiang, et al. Real-time pricing strategy based on reinforcement learning with load uncertainty [J]. Application Research of Computers, 2022, 39 (9): 2640-2646,2659. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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