IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p4942-d856846.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/4942/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/4942/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Basu, M., 2014. "Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II," Energy, Elsevier, vol. 78(C), pages 649-664.
    5. Chen, J.J. & Qi, B.X. & Peng, K. & Li, Y. & Zhao, Y.L., 2020. "Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch," Applied Energy, Elsevier, vol. 261(C).
    6. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    7. Duca, Victor E.L.A. & Fonseca, Thais C.O. & Cyrino Oliveira, Fernando Luiz, 2022. "Joint modelling wind speed and power via Bayesian Dynamical models," Energy, Elsevier, vol. 247(C).
    8. Chen, Min-Rong & Zeng, Guo-Qiang & Lu, Kang-Di, 2019. "Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources," Renewable Energy, Elsevier, vol. 143(C), pages 277-294.
    9. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Kıyan, Emel, 2022. "Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study," Energy, Elsevier, vol. 239(PB).
    10. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    11. 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.
    12. 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.
    13. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Jingliang Jin & Qinglan Wen & Xianyue Zhang & Siqi Cheng & Xiaojun Guo, 2021. "Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism," Energies, MDPI, vol. 14(7), pages 1-17, March.
    4. Wang, Yajun & Wang, Jidong & Cao, Man & Kong, Xiangyu & Abderrahim, Bouchedjira & Yuan, Long & Vartosh, Aris, 2023. "Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm," Energy, Elsevier, vol. 269(C).
    5. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
    6. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
    7. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2022. "Hybrid Chaotic Maps-Based Artificial Bee Colony for Solving Wind Energy-Integrated Power Dispatch Problem," Energies, MDPI, vol. 15(13), pages 1-26, June.
    8. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    9. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    10. 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).
    11. Kansal, Veenus & Dhillon, J.S., 2022. "Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework," Applied Energy, Elsevier, vol. 326(C).
    12. Jin, Jingliang & Zhou, Peng & Zhang, Mingming & Yu, Xianyu & Din, Hao, 2018. "Balancing low-carbon power dispatching strategy for wind power integrated system," Energy, Elsevier, vol. 149(C), pages 914-924.
    13. Liu, Zhi-Feng & Zhao, Shi-Xiang & Zhao, Shuang-Le & You, Guo-Dong & Hou, Xiao-Xin & Yu, Jia-Li & Li, Ling-Ling & Chen, Bo, 2023. "Improving the economic and environmental benefits of the energy system: A novel hybrid economic emission dispatch considering clean energy power uncertainty," Energy, Elsevier, vol. 285(C).
    14. Peng, Chao & Zou, Jianxiao & Lian, Lian, 2017. "Dispatching strategies of electric vehicles participating in frequency regulation on power grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 147-152.
    15. Heilmann, C. & Friedl, G., 2021. "Factors influencing the economic success of grid-to-vehicle and vehicle-to-grid applications—A review and meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    16. Jin, Jingliang & Zhou, Peng & Li, Chenyu & Bai, Yang & Wen, Qinglan, 2020. "Optimization of power dispatching strategies integrating management attitudes with low carbon factors," Renewable Energy, Elsevier, vol. 155(C), pages 555-568.
    17. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.
    18. Qinguo Zhang & Xiaojian Liu, 2022. "Optimization of the Quality of the Automatic Transmission Shift and the Power Transmission Characteristics," Energies, MDPI, vol. 15(13), pages 1-15, June.
    19. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    20. Juul, Nina, 2012. "Battery prices and capacity sensitivity: Electric drive vehicles," Energy, Elsevier, vol. 47(1), pages 403-410.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:4942-:d:856846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.