Economic Dispatch Optimization of Microgrid Based on an Improved Particle Swarm Optimization Algorithm
DOI:
https://doi.org/10.67541/istaer2627Keywords:
Microgrid economic dispatch; Improved particle swarm optimization; Inertia weight adaptive; Synergy of learning factors; Premature convergence inhibitionAbstract
The high proportion of renewable energy access makes the economic dispatch of microgrid face the dual challenges of bilateral source‑load uncertainty and high‑dimensional nonlinear constraints. The traditional particle swarm optimization algorithm tends to converge prematurely, and its convergence accuracy is insufficient when solving this problem. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed, which integrates a nonlinear adaptive inertia weight, a learning factor dynamic cooperative strategy, and a mutation disturbance mechanism based on population diversity monitoring. The algorithm adjusted the population fitness variance feedback inertia weight, establishes an exploration‑development stage criterion based on the evolutionary rate to realize asymmetric cooperation of learning factors, and uses the fitness variance as a premature judgment index to trigger Gaussian mutation disturbance. A typical grid‑connected microgrid including photovoltaic, wind power, diesel generator, and energy storage is taken as an example to verify under three scenarios: typical day, high fluctuation day and extreme weather day. The results show that the optimal operating cost of the improved algorithm is 1,983.5 yuan in typical daily scenarios, which is 6.95% lower than that of the standard PSO algorithm. The standard deviation across ten independent runs is 9.8 yuan, which is 56.6% lower than that of the standard algorithm. The source‑load bilateral strategy coordination reduces the cost by 8.03%, demonstrating a positive interaction effect. The cost in the extreme weather scenario is 2,851.9 yuan, which is still significantly lower than the comparison algorithm. This study provides an efficient and robust optimization solution for economic dispatch in microgrids under high uncertainty.
References
[1] Liu, Y., Gong, M., Chen, G., & Zhao, T. (2025, July). CNN-LSTM-based spatio-temporal prediction model for photovoltaic power generation. In Proceedings of the 2025 2nd International Conference on Big Data and Digital Management (pp. 570-575). https://doi.org/10.1145/3768801.3768892
[2] Papaefthymiou, G., & Dragoon, K. (2016). Towards 100% renewable energy systems: Uncapping power system flexibility. Energy Policy, 92, 69-82. https://doi.org/10.1016/j.enpol.2016.01.025
[3] Medina, C., Ana, C. R. M., & González, G. (2022). Transmission grids to foster high penetration of large-scale variable renewable energy sources–A review of challenges, problems, and solutions. International Journal of Renewable Energy Research (IJRER), 12(1), 146-169. https://doi.org/10.20508/ijrer.v12i1.12738.g8400
[4] Jia, D., Ren, Z., & Liu, K. (2025). Co-Optimization of Market and Grid Stability in High-Penetration Renewable Distribution Systems with Multi-Agent. Energies, 18(12), 3209. https://doi.org/10.3390/en18123209
[5] Di Leo, P., Malgaroli, G., Spertino, F., & Ciocia, A. (2026). Renewable energy integration in emerging electricity grids: Technologies, challenges, and system-level perspectives. Applied Sciences, 16(10), 5124. https://doi.org/10.3390/app16105124
[6] Zeng, Y., Wu, L., Li, J., Zhuang, X., & Wu, C. (2025). Resilient task allocation for UAV swarms: A bilevel PSO-ILP optimization approach. Drones, 9(9), 623. https://doi.org/10.3390/drones9090623
[7] Abualigah, L. (2025). Particle swarm optimization: Advances, applications, and experimental insights. Computers, Materials & Continua, 82(2), 1539-1592. https://doi.org/10.32604/cmc.2025.060765
[8] Rashed, N. A., Ali, Y. H., & Rashid, T. A. (2024). Advancements in optimization: critical analysis of evolutionary, swarm, and behavior-based algorithms. Algorithms, 17(9), 416. https://doi.org/10.3390/a17090416
[9] Tilman, A. R., Plotkin, J. B., & Akçay, E. (2020). Evolutionary games with environmental feedbacks. Nature communications, 11(1), 915. https://doi.org/10.1038/s41467-020-14531-6
[10] Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., & Busababodhin, P. (2025). Application and performance optimization of SLHS-TCN-XGBoost model in power demand forecasting. Computer Modeling in Engineering & Sciences, 143(3), 2883-2917. https://doi.org/10.32604/cmes.2025.066442
[11] Mei, F., Zhang, J., Lu, J., Lu, J., Jiang, Y., Gu, J., ... & Gan, L. (2021). Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations. Energy, 219, 119629. https://doi.org/10.1016/j.energy.2020.119629
[12] Gu, Y., Deng, J., Lu, H., Huang, Y., Qiao, J., & Jin, M. (2025, December). A Stochastic Optimization Method for Distribution Network Operation and Maintenance Driven by Load Uncertainty Scenarios. In 2025 IEEE 9th Conference on Energy Internet and Energy System Integration (EI2) (pp. 2437-2442). IEEE. https://doi.org/10.1109/EI268505.2025.11425219
[13] Patel, V., Giri, V. K., & Kumar, A. (2024). Efficient power management strategies for AC/DC microgrids with multiple voltage buses for sustainable renewable energy integration. Energy Informatics, 7(1), 97. https://doi.org/10.1186/s42162-024-00377-5
[14] Jabari, M., Ghoreishi, M., Bragatto, T., Santori, F., Cresta, M., Geri, A., & Maccioni, M. (2025). Advancing hybrid ac/dc microgrid converters: Modeling, control strategies, and fault behavior analysis. Energies, 18(23), 6302. https://doi.org/10.3390/en18236302
[15] Zhi, Y., Sun, T., & Yang, X. (2023). A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction. Journal of Building Engineering, 75, 106997. https://doi.org/10.1016/j.jobe.2023.106997
[16] Huang, C. J., & Kuo, P. H. (2019). Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE access, 7, 74822-74834. https://doi.org/10.1109/ACCESS.2019.2921238
[17] Qiao, Y., Song, Y., & Huang, K. (2019). A novel control algorithm design for hybrid electric vehicles considering energy consumption and emission performance. Energies, 12(14), 2698. https://doi.org/10.3390/en12142698
[18] Diao, R., Hu, Z., & Song, Y. (2023). Subgradient of cycle-based aging cost function and its application in optimal operation of battery energy storage system with multiple subsystems. IEEE Transactions on Energy Conversion, 39(1), 625-643. https://doi.org/10.1109/TEC.2023.3324917
[19] Chembian, W. T., Senthilkumar, G., Prasanth, A., & Subash, R. (2025). K-means pelican optimization algorithm based search space reduction for remote sensing image retrieval. Journal of the Indian Society of Remote Sensing, 53(1), 101-115. https://doi.org/10.1007/s12524-024-01994-z
[20] Gupta, M. K., & Chandra, P. (2022). Effects of similarity/distance metrics on k-means algorithm with respect to its applications in IoT and multimedia: a review. Multimedia Tools and Applications, 81(26), 37007-37032. https://doi.org/10.1007/s11042-021-11255-7
[21] Zhang, C., Xiang, C., Zhou, P., Qian, H., & Duan, Z. (2026). Optimal scheduling of PV-storage irrigation pump truck by dynamic historical particle swarm optimization. Agricultural Water Management, 334, 110674. https://doi.org/10.1016/j.agwat.2026.110674
[22] Udenwagu, N. E., Oni, A. A., & Ezenwoke, A. A. (2025, April). Improving load balancing in cloud computing for maximum infrastructure utilisation using multi-objective particle swarm optimization algorithm. In IOP Conference Series: Earth and Environmental Science (Vol. 1492, No. 1, p. 012005). IOP Publishing. https://doi.org/10.1088/1755-1315/1492/1/012005
[23] Wang, P., Su, Y., Huang, B., Zhu, D., Liu, W., Nedzved, A., ... & Leung, H. (2024). Low-rank tensor completion pansharpening based on haze correction. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-20. https://doi.org/10.1109/tgrs.2024.3405848
[24] Zhang, F., Jiang, H., Liu, F., Hou, T., & Liu, Y. (2026). A metaheuristic study: A partial multitask offloading strategy for vehicular fog computing based on adaptive particle swarm optimization. Computer Communications, 108455. https://doi.org/10.1016/j.comcom.2026.108455
[25] Jianhua, L., & Zhiheng, W. (2021). A hybrid sparrow search algorithm based on constructing similarity. Ieee Access, 9, 117581-117595. https://doi.org/10.1109/access.2021.3106269
[26] Aljaidi, M., Jangir, P., Arpita, Agrawal, S. P., Pandya, S. B., Parmar, A., ... & Khishe, M. (2025). A two phase differential evolution algorithm with perturbation and covariance matrix for PEMFC parameter estimation challenges. Scientific Reports, 15(1), 8676. https://doi.org/10.1038/s41598-025-92818-8
Published
Data Availability Statement
The data that support the findings of this study are available upon request from the corresponding authors, H.Y.
Issue
Section
License
Copyright (c) 2026 Haoyu You, Longtai Hua, Tianyi Su (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
X (FORMERLY TWITTER)