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Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning

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  • Wang, Yijian
  • Cui, Yang
  • Li, Yang
  • Xu, Yang

Abstract

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. In this paper, the Mixed-Attention is applied to fit the conditions of the equipment, and Multi-Agent Soft Actor-Critic(MA-SAC) , Multi-Agent Win or Learn Fast Policy Hill-Climbing (MA-WoLF-PHC) are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5 kW in 24 h and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance.

Suggested Citation

  • Wang, Yijian & Cui, Yang & Li, Yang & Xu, Yang, 2023. "Collaborative optimization of multi-microgrids system with shared energy storage based on multi-agent stochastic game and reinforcement learning," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015761
    DOI: 10.1016/j.energy.2023.128182
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    1. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    2. Jiang, Jianhua & Zhou, Renjie & Xu, Hao & Wang, Hao & Wu, Ping & Wang, Zhuo & Li, Jian, 2022. "Optimal sizing, operation strategy and case study of a grid-connected solid oxide fuel cell microgrid," Applied Energy, Elsevier, vol. 307(C).
    3. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    4. Wang, Zhaoqi & Zhang, Lu & Tang, Wei & Chen, Ying & Shen, Chen, 2022. "Equilibrium allocation strategy of multiple ESSs considering the economics and restoration capability in DNs," Applied Energy, Elsevier, vol. 306(PA).
    5. Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    6. dos Santos Neto, Pedro J. & Barros, Tárcio A.S. & Silveira, Joao P.C. & Ruppert Filho, Ernesto & Vasquez, Juan C. & Guerrero, Josep M., 2020. "Power management techniques for grid-connected DC microgrids: A comparative evaluation," Applied Energy, Elsevier, vol. 269(C).
    7. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    8. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    9. Stoppato, Anna & Cavazzini, Giovanna & Ardizzon, Guido & Rossetti, Antonio, 2014. "A PSO (particle swarm optimization)-based model for the optimal management of a small PV(Photovoltaic)-pump hydro energy storage in a rural dry area," Energy, Elsevier, vol. 76(C), pages 168-174.
    10. Zhao, Bo & Xue, Meidong & Zhang, Xuesong & Wang, Caisheng & Zhao, Junhui, 2015. "An MAS based energy management system for a stand-alone microgrid at high altitude," Applied Energy, Elsevier, vol. 143(C), pages 251-261.
    11. Abunima, Hamza & Park, Woan-Ho & Glick, Mark B. & Kim, Yun-Su, 2022. "Two-Stage stochastic optimization for operating a Renewable-Based Microgrid," Applied Energy, Elsevier, vol. 325(C).
    12. Nelson, James & Johnson, Nathan G. & Fahy, Kelsey & Hansen, Timothy A., 2020. "Statistical development of microgrid resilience during islanding operations," Applied Energy, Elsevier, vol. 279(C).
    13. Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
    14. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
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    1. Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos & Dasí-Crespo, Daniel, 2023. "Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods," Energy, Elsevier, vol. 284(C).

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