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Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid

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  • Harsh, Pratik
  • Das, Debapriya

Abstract

The growth in the energy demand of the microgrid due to the inclusion of electric vehicles (EV) and other non-EV loads introduces several challenges for the operators in scheduling energy for the microgrid. The inclusion of demand response (DR) program in the operational planning of microgrid can decrease the burden on the operator, but it requires aggregators for the efficient coordination between the operator and several potential DR participants of the microgrid. In this work, an optimization model is proposed to include a novel incentive-based DR program in the energy management problem of the reconfigured grid-connected microgrid. Two different aggregators for EV and non-EV loads are included in the work as an interface between the operator and DR participants. The objective of the proposed DR program is to maximize the incentives offered to the DR participants while maintaining uniformity in terms of rewards and distress delivered to the DR participants. The proposed work is analyzed on a static model of a 33-bus grid-connected microgrid consisting of EV charging stations, renewable energy sources, and diesel generators at different locations. The microgrid is reconfigured at each operating interval to minimize the power lost in the network. The result confirms that optimality is achieved at the source, distribution, and load side of the microgrid. For a day-ahead operation, it has been found that the energy dependency of a microgrid on the utility grid and conventional energy source is reduced by 9.62% and 29.06%, respectively.

Suggested Citation

  • Harsh, Pratik & Das, Debapriya, 2022. "Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:rensus:v:160:y:2022:i:c:s1364032122001721
    DOI: 10.1016/j.rser.2022.112251
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    2. Zejun Tong & Chun Zhang & Xiaotai Wu & Pengcheng Gao & Shuang Wu & Haoyu Li, 2023. "Economic Optimization Control Method of Grid-Connected Microgrid Based on Improved Pinning Consensus," Energies, MDPI, vol. 16(3), pages 1-31, January.
    3. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.
    4. Lihui Gao & Shuanghao Yang & Nan Chen & Junheng Gao, 2024. "Integrated Energy System Dispatch Considering Carbon Trading Mechanisms and Refined Demand Response for Electricity, Heat, and Gas," Energies, MDPI, vol. 17(18), pages 1-20, September.
    5. Mohseni, Soheil & Khalid, Roomana & Brent, Alan C., 2023. "Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics," Applied Energy, Elsevier, vol. 341(C).
    6. Moon-Jong Jang & Taehoon Kim & Eunsung Oh, 2023. "Data-Driven Modeling of Vehicle-to-Grid Flexibility in Korea," Sustainability, MDPI, vol. 15(10), pages 1-16, May.

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