Download pdf
Research on Power Grid Dispatching Based on Particle Swarm Optimization
- 1 School of information science and engineering, Lanzhou University, China
* Author to whom correspondence should be addressed.
Abstract
The optimization and dispatching of microgrids is the main issue in achieving efficient operation of smart grids. In recent years, various optimization algorithms have been widely used in microgrid dispatching. This paper systematically reviews the research progress that has made in this field and compares the advantages and disadvantages of different optimization methods. First, the classic intelligent optimization algorithms, such as particle swarm optimization, genetic algorithm, and differential evolution are introduced, and in this essay, their applications and improvement strategies in microgrid dispatching are discussed. Secondly, the optimization methods based on reinforcement learning, including deep reinforcement learning (DRL), deep deterministic policy gradient (DDPG), and proximal policy optimization (PPO), are analyzed, focusing on their advantages in dealing with high-dimensional, nonlinear, and real-time dispatching problems. Additionally, the ‘prediction plus optimization’ combination strategy, such as Bayesian optimization, metaheuristic optimization, and multi-scenario optimization methods based on machine learning, is discussed to deal with the uncertainty and robustness problems of microgrids. These three aspects represent three kinds of most popular research on microgrid optimization. Finally, this paper summarizes the applicability of different optimization methods and looks forward to future development trends. Comprehensive analysis shows hybrid optimization strategies (such as PSO +GOA) are also worthy of further study in improving robustness.
Keywords
Power grid, Microgrid, Optimization, Reinforcement Learning
[1]. Zhanghua Zheng, Qian Ai. Present Situation of Research on Microgrid and Its Application Prospects in China[J]. Power System Technology,2008,(16):27-31+58.
[2]. P. Mundra, A. Arya, S. Gawre and S. Mehroliya, "Independent Demand Side Management System Based on Energy Consumption Scheduling by NSGA-II for Futuristic Smart Grid," 2020 IEEE-HYDCON, Hyderabad, India, 2020, pp. 1-6, doi: 10.1109/HYDCON48903.2020.9242816.
[3]. Yang, J.; Li, L. Micro-Energy Grid Energy Utilization Optimization with Electricity and Heat Storage Devices Based on NSGA-III Algorithm. Energies 2024, 17, 5563. doi: 10.3390/en17225563
[4]. Weng Cheng Liu, Zhi Zhong Mao, Microgrid economic dispatch using Information-Enhanced Deep Reinforcement Learning with consideration of control periods, Electric Power Systems Research, Volume 239, 2025, 111244, doi: 10.1016/j.epsr.2024.111244.
[5]. S. S. Shuvo and M. M. Islam, "Optimal Dispatch for A Microgrid with Distributed Generations and EV Charging Load," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066388.
[6]. LI Jiawei, JU Yuntao, ZHANG Lu, LIU Wenwu, WANG Jie. Multi-time scale distributed robust optimal scheduling of microgrid based on model predictive control [J]. Electric Power Engineering Technology, 2024, 43(4):45-55
[7]. Hassan, Q., Viktor, P., Al-Musawi, T.J., Ali, B.M., Algburi, S., Alzoubi, H.M., Al-Jiboory, A.K., Sameen, A.Z., Salman, H.M., Jaszczur, M.: The renewable energy role in the global energy transition. Renew. Energy Focus, 2024, 48, 100545. Doi: 10.1016/j.ref.2024.100545
[8]. Shen, H.; Shen, X.; Chen, Y. Real-Time Microgrid Energy Scheduling Using Meta-Reinforcement Learning. Energies, 2024, 17, 2367. https://doi.org/10.3390/en17102367
Cite this article
Chen,Y. (2025). Research on Power Grid Dispatching Based on Particle Swarm Optimization. Applied and Computational Engineering,151,75-81.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).