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Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation

Author

Listed:
  • Mohamed El-Hendawi

    (Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71515, Egypt)

  • Zhanle Wang

    (Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada)

  • Xiaoyue Liu

    (College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China)

Abstract

This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic E V usages such as E V driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EV s for frequency regulation; meanwhile, the E V owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic E V usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions.

Suggested Citation

  • Mohamed El-Hendawi & Zhanle Wang & Xiaoyue Liu, 2022. "Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation," Energies, MDPI, vol. 15(12), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4446-:d:842110
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    References listed on IDEAS

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    Cited by:

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    2. Mohammad Kamrul Hasan & AKM Ahasan Habib & Shayla Islam & Mohammed Balfaqih & Khaled M. Alfawaz & Dalbir Singh, 2023. "Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations," Energies, MDPI, vol. 16(3), pages 1-20, January.
    3. Yuzhe Xie & Yan Yao & Yawu Wang & Weiqiang Cha & Sheng Zhou & Yue Wu & Chunyi Huang, 2022. "A Cooperative Game-Based Sizing and Configuration of Community-Shared Energy Storage," Energies, MDPI, vol. 15(22), pages 1-17, November.
    4. Rafael G. Nagel & Vitor Fernão Pires & Jony L. Silveira & Armando Cordeiro & Daniel Foito, 2023. "Financial Analysis of Household Photovoltaic Self-Consumption in the Context of the Vehicle-to-Home ( V2H ) in Portugal," Energies, MDPI, vol. 16(3), pages 1-21, January.
    5. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    6. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).

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