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Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty

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  • Jeon, Soi
  • Choi, Dae-Hyun

Abstract

Mobile energy storage systems (MESSs) are becoming crucial devices to maintain stable power distribution system operations under the operation of voltage regulators including on-load tap changers, capacitor banks, and smart inverters of photovoltaic (PV) systems. This study proposes a joint framework in which the operation of voltage regulators and the road routing of MESSs are co-optimized for Volt/VAR control (VVC) in both power distribution and transportation networks. The proposed framework aims to minimize real power loss and voltage deviation along with peak shaving in power distribution networks, and to reduce the MESS traveling cost in transportation networks. Under the uncertainty of the predicted error of the PV generation output, the proposed framework is formulated as a chance-constrained optimization problem using mixed-integer linear programming. The proposed joint optimization algorithm was simulated in the IEEE 13-bus and IEEE 33-bus systems, which were coupled with 9-node and 15-node transportation systems, respectively. The results show that, in contrast with a VVC method without MESS, the proposed algorithm using MESS reduces the total real power loss and peak load by 9.7% and 15.92%, respectively, in the IEEE 13-bus system, and 2.55% and 3.47%, respectively, in the IEEE 33-bus system.

Suggested Citation

  • Jeon, Soi & Choi, Dae-Hyun, 2022. "Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921017074
    DOI: 10.1016/j.apenergy.2021.118488
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    References listed on IDEAS

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    1. Davye Mak & Dae-Hyun Choi, 2018. "Hierarchical Look-Ahead Conservation Voltage Reduction Framework Considering Distributed Energy Resources and Demand Reduction," Energies, MDPI, vol. 11(12), pages 1-20, November.
    2. Mak, Davye & Choi, Dae-Hyun, 2020. "Optimization framework for coordinated operation of home energy management system and Volt-VAR optimization in unbalanced active distribution networks considering uncertainties," Applied Energy, Elsevier, vol. 276(C).
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    Cited by:

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    2. Zhuoxin Lu & Xiaoyuan Xu & Zheng Yan & Dong Han & Shiwei Xia, 2024. "Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    3. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    4. Kabir, Farzana & Yu, Nanpeng & Gao, Yuanqi & Wang, Wenyu, 2023. "Deep reinforcement learning-based two-timescale Volt-VAR control with degradation-aware smart inverters in power distribution systems," Applied Energy, Elsevier, vol. 335(C).
    5. Zhang, Mengling & Jiao, Zihao & Ran, Lun & Zhang, Yuli, 2023. "Optimal energy and reserve scheduling in a renewable-dominant power system," Omega, Elsevier, vol. 118(C).
    6. Liu, Shan & Yan, Jie & Yan, Yamin & Zhang, Haoran & Zhang, Jing & Liu, Yongqian & Han, Shuang, 2024. "Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate," Applied Energy, Elsevier, vol. 357(C).
    7. Jianxiao Wang & Liudong Chen & Zhenfei Tan & Ershun Du & Nian Liu & Jing Ma & Mingyang Sun & Canbing Li & Jie Song & Xi Lu & Chin-Woo Tan & Guannan He, 2023. "Inherent spatiotemporal uncertainty of renewable power in China," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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