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Optimal vehicle to grid planning and scheduling using double layer multi-objective algorithm

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  • Tan, Kang Miao
  • Ramachandaramurthy, Vigna K.
  • Yong, Jia Ying

Abstract

Vehicle to grid is a revolutionary technology that allows energy exchange between electric vehicles and power grid for mutual advantages. The implementation of appropriate vehicle to grid energy management system can maximize the potential of electric vehicles to provide grid ancillary services. This paper proposes an optimal vehicle to grid planning and scheduling by utilizing a novel double layer multi-objective algorithm. This optimization algorithm utilizes the grid-connected electric vehicles to perform peak load shaving and load levelling services to minimize the power grid load variance in the first layer optimization. Meanwhile, the second layer optimization minimizes the reactive power compensation for grid voltage regulation and therefore, optimizes the vehicle to grid charger's capacitor sizing. The second layer optimization algorithm utilizes an approximated formula from the simulation of a vehicle to grid charger. The proposed vehicle to grid optimization algorithm considers various power grid and electric vehicle constraints for practicality purpose. With the real time implementation of the proposed algorithm, the optimization results show that the power load curve is effectively followed the preset constant target loading, while the grid voltage is successfully regulated to the predetermined voltage level with minimal amount of reactive power supply from the optimal charger's capacitor.

Suggested Citation

  • Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Optimal vehicle to grid planning and scheduling using double layer multi-objective algorithm," Energy, Elsevier, vol. 112(C), pages 1060-1073.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:1060-1073
    DOI: 10.1016/j.energy.2016.07.008
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    4. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    5. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    6. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tariq, Mohd, 2021. "Experimental verification of a flexible vehicle-to-grid charger for power grid load variance reduction," Energy, Elsevier, vol. 228(C).
    7. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    8. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(C).
    10. Ma, Haiping & Yang, Zhile & You, Pengcheng & Fei, Minrui, 2017. "Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging," Energy, Elsevier, vol. 135(C), pages 101-111.
    11. Colmenar-Santos, Antonio & Muñoz-Gómez, Antonio-Miguel & Rosales-Asensio, Enrique & López-Rey, África, 2019. "Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario," Energy, Elsevier, vol. 183(C), pages 61-74.
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