Two-Layer Optimization Scheduling of Electric Vehicle Charging and Energy Storage Systems in Microgrids

Tiange Xiang

doi:10.54097/bb810g91

Authors

Tiange Xiang

DOI:

https://doi.org/10.54097/bb810g91

Keywords:

Microgrid, Electric Vehicle Charging, Energy Storage System, Two-Layer Optimization, Scheduling Strategy.

Abstract

In the context of the global energy structure transformation, microgrids, as a new type of power system, have garnered widespread attention for their flexibility and efficiency. Electric vehicles as a green transportation tool, introduce significant volatility in charging loads. This paper explores the challenges posed by the volatility of EV charging loads and the conflict in managing energy storage systems on the stability and economic performance of microgrids. It presents an optimization approach and establishes a two-layer optimization model to address the conflicts between charging demand and energy storage management. By employing dynamic scheduling strategies to enhance responsiveness, the paper aims to mitigate the impact of charging fluctuations on the microgrid. A case study using a mixed-line scenario is provided for two-layer optimization modeling. The paper summarizes the key issues and solutions of the research, and based on theoretical and practical needs, identifies future development trends, emphasizing higher precision, stability, and the integration of intelligent scheduling optimization methods.

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References

[1] I Bowen, Fan Ying.(2023). Research on power system planning under high proportion of renewable energy access[J]. Journal of management science, 2023, (10): 21-35.

[2] Fu Yang, Xing Xing-Yue, LI Zhen-Kun, et al.(2022)Multi-stage robust optimization planning of microgrid group based on master-slave game[J].Power automation equipment, 42(04): 1-8.

[3] Room Super Yun, Yang Kun & Chai Ruihuan.(2024).Two-layer multi-objective optimal scheduling of microgrid groups with electric vehicles at TOU.Journal of Electric Power Science and Technology(01),39:124-133.

[4] Choudhury Subhashree. (2022). Review of energy storage system technologies integration to microgrid: Types, control strategies, issues, and future prospects. Journal of Energy Storage,48:1-2

[5] AbuElrub A ,Hamed F ,Saadeh O .(2020).Microgrid integrated electric vehicle charging algorithm with photovoltaic generation [J]. Journal of Energy Storage, 32:1-2.

[6] Pang Kexin.(2023).Research on elastic microgrid expansion planning based on deep reinforcement learning[D].Nanjing University of Science and Technology.25-30.

[7] Mei Peng, Lin Zhixian, Li Tengfei.(2023).Cloud model based two-layer optimal scheduling method of distributed photovoltaic distribution network [J]. Radio engineering, 53 (09): 2158-2164.

[8] Dong Hai, Cao Xiaolan.(2023). Dynamic scheduling of hybrid energy microgrids based on policy drive[J].Journal of Solar energy, 44(07):22-29.

[9] Zhang Wei, Wang Lijuan, Peng Jingli.(2023) Nested genetic algorithm for multi-objective bilevel optimization: A case study of mixed line programming [J]. Mechanical and electrical engineering technology,52(11):38-42.