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Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

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  • Li Yan

    (School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Zhengyu Zhu

    (School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Xiaopeng Kang

    (School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Boyang Qu

    (School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China)

  • Baihao Qiao

    (Guangzhou Institute of Technology, Xidian University, Xi’an 710071, China)

  • Jiajia Huan

    (Guangdong Power Grid Co., Ltd., Guangzhou 510000, China)

  • Xuzhao Chai

    (School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China)

Abstract

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system ( DEED IWEv ) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEED IWEv model.

Suggested Citation

  • Li Yan & Zhengyu Zhu & Xiaopeng Kang & Boyang Qu & Baihao Qiao & Jiajia Huan & Xuzhao Chai, 2022. "Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:4942-:d:856846
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    References listed on IDEAS

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