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Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

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  • Hao, Ying
  • Dong, Lei
  • Liang, Jun
  • Liao, Xiaozhong
  • Wang, Lijie
  • Shi, Lefeng

Abstract

We propose herein an extended power forecasting-based coordination dispatch method for PV power generation microgrid with plug-in EVs (PVEVM) to improve the local consumption of renewable energy in the microgrid by guiding electric vehicle (EV) orderly charging. In this method, we use a clustering algorithm and neural network to build a power forecasting model (PFM) based on real data which can effectively characterise the uncertainty of PV power generation and EV charging load. Based on the interaction between the energy control centre (ECC) of the PVEVM and the EV users, a one-leader multiple-follower Stackelberg game is formulated, and the Stackelberg equilibrium is determined by using a power forecasting-based genetic algorithm (GA). As a main contribution of this paper, the PV power generation and EV charging load output from the PFM are used to generate a better quality initial population of the GA to improve its performance. A case study using real data from the Aifeisheng PV power station in China and EV charging stations in the UK verifies the good performance of the proposed extended coordination dispatch algorithm.

Suggested Citation

  • Hao, Ying & Dong, Lei & Liang, Jun & Liao, Xiaozhong & Wang, Lijie & Shi, Lefeng, 2020. "Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid," Renewable Energy, Elsevier, vol. 155(C), pages 1191-1210.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:1191-1210
    DOI: 10.1016/j.renene.2020.03.169
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    Cited by:

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    2. Shubham Mishra & Shrey Verma & Subhankar Chowdhury & Ambar Gaur & Subhashree Mohapatra & Gaurav Dwivedi & Puneet Verma, 2021. "A Comprehensive Review on Developments in Electric Vehicle Charging Station Infrastructure and Present Scenario of India," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    3. Qingbo Tan & Zhuning Wang & Wei Fan & Xudong Li & Xiangguang Li & Fanqi Li & Zihao Zhao, 2022. "Development Path and Model Design of a New Energy Vehicle in China," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Wang, Guotao & Liao, Qi & Wang, Chang & Liang, Yongtu & Zhang, Haoran, 2022. "Multiperiod optimal planning of biofuel refueling stations: A bi-level game-theoretic approach," Renewable Energy, Elsevier, vol. 200(C), pages 1152-1165.
    5. Yuan An & Kaikai Dang & Xiaoyu Shi & Rong Jia & Kai Zhang & Qiang Huang, 2021. "A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period," Energies, MDPI, vol. 14(4), pages 1-18, February.
    6. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.
    7. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    8. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    9. Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).

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