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A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market

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  • Su, Jun
  • Lie, T.T.
  • Zamora, Ramon

Abstract

The uncertainty of plug-in electric vehicle (EV) charging behaviour is a crucial factor that not only influences the peak power demand in distribution networks, but also the tariff plans of EV charging service. The uncertain upstream electricity price considerably complicates the issue regarding how to achieve specific economic goals for distribution network operators (DNOs) while guaranteeing EV users’ interest. A rolling horizon scheduling approach based on Genetic Algorithm (GA) is proposed in this paper to provide a win-win strategy for both DNOs and EV users. It deals with the online optimal scheduling problem of aggregated EVs in the energy exchange market. The objective of the scheduling strategy is to maximise DNOs’ profit margin by charging EVs in the low price time intervals as well as shifting peak charging loads. The operational constraints of EVs’ availability and electricity bidding are all considered in the time rolling horizon framework, meaning all this information will be updated, calculated and partially forecasted at each time interval until the end of the day. A case study is carried out with a 33-node distribution network to verify the effectiveness of the proposed scheduling strategy. In detail, specific tariff plans can be determined toward possible values of uncertain market price to satisfy utilities’ economic targets. In this way, both individuals and energy providers that participate in the energy market can benefit from the proposed rolling horizon strategy and keep the uncertainty under control.

Suggested Citation

  • Su, Jun & Lie, T.T. & Zamora, Ramon, 2020. "A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market," Applied Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:appene:v:275:y:2020:i:c:s0306261920309181
    DOI: 10.1016/j.apenergy.2020.115406
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    6. Bagheri Tookanlou, Mahsa & Pourmousavi, S. Ali & Marzband, Mousa, 2023. "A three-layer joint distributionally robust chance-constrained framework for optimal day-ahead scheduling of e-mobility ecosystem," Applied Energy, Elsevier, vol. 331(C).
    7. Tang, Lei & Guo, Jue & Zhao, Boyang & Wang, Xiuli & Shao, Chengcheng & Wang, Yifei, 2021. "Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis," Energy, Elsevier, vol. 224(C).
    8. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    9. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations," Energy, Elsevier, vol. 246(C).
    10. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    11. Lei, Xiang & Yu, Hang & Shao, Ziyun & Jian, Linni, 2023. "Optimal bidding and coordinating strategy for maximal marginal revenue due to V2G operation: Distribution system operator as a key player in China's uncertain electricity markets," Energy, Elsevier, vol. 283(C).
    12. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    13. Bahman Ahmadi & Elham Shirazi, 2023. "A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration," Energies, MDPI, vol. 16(19), pages 1-26, October.
    14. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).

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