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Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

Author

Listed:
  • Boyang Qu

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

  • Baihao Qiao

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

  • Yongsheng Zhu

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

  • Jingjing Liang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Ling Wang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

Suggested Citation

  • Boyang Qu & Baihao Qiao & Yongsheng Zhu & Jingjing Liang & Ling Wang, 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(12), pages 1-28, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1991-:d:121157
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    References listed on IDEAS

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    1. Zhao, Yang & Noori, Mehdi & Tatari, Omer, 2016. "Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis," Applied Energy, Elsevier, vol. 170(C), pages 161-175.
    2. Andersson, S.-L. & Elofsson, A.K. & Galus, M.D. & Göransson, L. & Karlsson, S. & Johnsson, F. & Andersson, G., 2010. "Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany," Energy Policy, Elsevier, vol. 38(6), pages 2751-2762, June.
    3. Yuchong Huo & Ping Jiang & Yuan Zhu & Shuang Feng & Xi Wu, 2015. "Optimal Real-Time Scheduling of Wind Integrated Power System Presented with Storage and Wind Forecast Uncertainties," Energies, MDPI, vol. 8(2), pages 1-21, February.
    4. Howlader, Abdul Motin & Urasaki, Naomitsu & Yona, Atsushi & Senjyu, Tomonobu & Saber, Ahmed Yousuf, 2013. "A review of output power smoothing methods for wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 135-146.
    5. Haque, A.N.M.M. & Ibn Saif, A.U.N. & Nguyen, P.H. & Torbaghan, S.S., 2016. "Exploration of dispatch model integrating wind generators and electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1441-1451.
    6. Sekyung Han & Soohee Han, 2013. "Economic Feasibility of V2G Frequency Regulation in Consideration of Battery Wear," Energies, MDPI, vol. 6(2), pages 1-18, February.
    7. Alham, M.H. & Elshahed, M. & Ibrahim, Doaa Khalil & Abo El Zahab, Essam El Din, 2016. "A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management," Renewable Energy, Elsevier, vol. 96(PA), pages 800-811.
    8. Truong H. Khoa & Pandian M. Vasant & Balbir Singh Mahinder Singh & V. N. Dieu, 2017. "Hybrid Mean-Variance Mapping Optimization for Non-Convex Economic Dispatch Problems," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 8(4), pages 34-59, October.
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    Cited by:

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    2. Qiao, Baihao & Liu, Jing, 2020. "Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm," Renewable Energy, Elsevier, vol. 154(C), pages 316-336.
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    5. 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.

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