Research on Coordinated Optimization Control Strategies for Multi-energy Storage System

DOI: https://doi.org/10.62517/jes.202502204

Author(s)

Xue He, Lili Jiang*, Shanqiao Fu, Chang Yan

Affiliation(s)

Yunnan Water Resources and Hydropower Vocational College, Yunnan Kunming, Yunnan, China *Corresponding Author

Abstract

This paper studies the coordinated optimization control strategy of multi-energy storage system (MESS), especially improving the energy utilization efficiency and economic benefits of the system through model predictive control (MPC) and intelligent algorithm optimization methods. With the rapid development of renewable energy and smart grids, how to efficiently dispatch various energy storage devices has become a key issue. The paper analyzes the advantages and disadvantages of centralized and distributed control strategies, and proposes to optimize the collaborative scheduling of energy storage equipment through the multi-agent system (MAS). The simulation results show that distributed control performs better than centralized control in terms of energy loss, economic benefits and system stability. The distributed control strategy can significantly reduce the total energy loss and improve economic benefits. Finally, the research points out that in the future, the control strategy should be further optimized to enhance the robustness and adaptability of the system in order to cope with the challenges in complex dynamic environments.

Keywords

Multi-energy Storage System; Distributed Control Strategy; Energy Storage Equipment; Energy Utilization

References

[1]Zhao, Y., Li, H., Wang, X. (2020). "A model predictive control strategy for multi-energy storage system.". Energy Reports, 6, 93-105. [2]Wang, Y., Zhang, X., Li, S. (2018). "Multi-objective optimization for multi-energy storage system using particle swarm optimization and support vector machines.". Renewable Energy, 127, 42-51. [3]Li, J., Zhang, W., Xu, J. (2021). "Deep learning-based optimization control for multi-energy storage system.". Journal of Energy Storage, 35, 101232. [4]Zhang, L., Li, Z., Xu, H. (2019). "Constraint-based optimization for energy storage system coordination.". Applied Energy, 241, 9-18. [5]Khan, M. R., Ahmed, M., Zhang, Q. (2020). "Cooperative game theory-based control for MESS.". IEEE Transactions on Smart Grid, 11(5), 4231-4241. [6]Chen, Z., Li, Z., Wu, J. (2017). "Robust control for multi-energy storage system with uncertain parameters.". IEEE Transactions on Power Systems, 32(2), 1367-1376. [7]Xu, Y., Li, B., Zhang, L. (2018). "Economic and environmental optimization of multi-energy storage system.". Energy, 143, 1-10. [8]Zhang, W., Wang, J., Xu, S. (2019). "Optimal operation of multi-energy storage system in microgrids considering cost and environmental impacts.". Energy Conversion and Management, 182, 438-450. [9]Zhu, Y.Y., Wang, Z.J., Wang, H., Wei, D., Shao, N.L., Jiang, X.C. (2021). "Research on hierarchical control of micro power grid hybrid energy storage coordination optimization strategy.". Acta Energiae Solaris Sinica, 42(3): 235-242. [10]Bocklisch, T. (2016). “Hybrid energy storage approach for renewable energy applications.”. Journal of Energy Storage, 8, 311-319. [11]Hemmati, R. Saboori, H. (2016). "Emergence of hybrid energy storage systems in renewable energy and transport applications – A review.". Renewable and Sustainable Energy Reviews, 65, 11-2.