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A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination

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  • Zhang, Wenjie
  • Gandhi, Oktoviano
  • Quan, Hao
  • Rodríguez-Gallegos, Carlos D.
  • Srinivasan, Dipti

Abstract

Electric Vehicles have been receiving increasing attention. As the number of electric vehicles increases, uncoordinated charging of electric vehicles can lead to voltage and frequency instability in microgrids. Various methods have been proposed for electrical vehicle coordination, where most of them focused on controlling active power. Vehicle-to-grid var has been recently included in volt-var optimization approaches, which aim at improving voltage stability using var sources. However, most of these approaches are based on computationally inefficient heuristic methods, which are not applicable to handle fast-changing vehicle-to-grid var. Furthermore, the uncertainties and charging demands of electric vehicles have not been considered thoroughly. In this paper, an integrated volt-var optimization engine is proposed for distributed electric vehicle charging coordination and fast vehicle-to-grid var dispatch, considering the uncertainties and charging demands of electric vehicles. The proposed method is based on a multi-agent system, which distributes complex optimization processes to enhance computational efficiency. Case studies show that the proposed distributed method reduces up to 92% computational time without economic losses, compared with the central coordination. It is also observed that the costly usage of diesel generators can be reduced by employing more vehicle-to-grid var due to their similar functionality in voltage regulation. Surprisingly, it is found that when utilizing the power support from electric vehicles and diesel generators, the computational time decreases even when more decision variables are added.

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  • Zhang, Wenjie & Gandhi, Oktoviano & Quan, Hao & Rodríguez-Gallegos, Carlos D. & Srinivasan, Dipti, 2018. "A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination," Applied Energy, Elsevier, vol. 229(C), pages 96-110.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:96-110
    DOI: 10.1016/j.apenergy.2018.07.092
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    2. Mak, Davye & Choeum, Daranith & Choi, Dae-Hyun, 2020. "Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources," Applied Energy, Elsevier, vol. 261(C).
    3. Buonomano, A. & Calise, F. & Cappiello, F.L. & Palombo, A. & Vicidomini, M., 2019. "Dynamic analysis of the integration of electric vehicles in efficient buildings fed by renewables," Applied Energy, Elsevier, vol. 245(C), pages 31-50.
    4. Barone, Giovanni & Buonomano, Annamaria & Forzano, Cesare & Giuzio, Giovanni Francesco & Palombo, Adolfo, 2020. "Increasing self-consumption of renewable energy through the Building to Vehicle to Building approach applied to multiple users connected in a virtual micro-grid," Renewable Energy, Elsevier, vol. 159(C), pages 1165-1176.
    5. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    6. Anaya, Karim L. & Pollitt, Michael G., 2020. "Reactive power procurement: A review of current trends," Applied Energy, Elsevier, vol. 270(C).
    7. 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).
    8. 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).
    9. Ruifeng Shi & Jie Zhang & Hao Su & Zihang Liang & Kwang Y. Lee, 2020. "An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements," Energies, MDPI, vol. 13(22), pages 1-21, November.
    10. Buonomano, Annamaria, 2020. "Building to Vehicle to Building concept: A comprehensive parametric and sensitivity analysis for decision making aims," Applied Energy, Elsevier, vol. 261(C).
    11. Li, Mengyu & Lenzen, Manfred & Wang, Dai & Nansai, Keisuke, 2020. "GIS-based modelling of electric-vehicle–grid integration in a 100% renewable electricity grid," Applied Energy, Elsevier, vol. 262(C).
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    13. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).

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