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Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm

Author

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  • Mengmeng Qiao

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Zexu Yu

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Zhenhai Dou

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Yuanyuan Wang

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Ye Zhao

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Ruishuo Xie

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

  • Lianxin Liu

    (School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer’s optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm’s skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

Suggested Citation

  • Mengmeng Qiao & Zexu Yu & Zhenhai Dou & Yuanyuan Wang & Ye Zhao & Ruishuo Xie & Lianxin Liu, 2022. "Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-31, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5174-:d:864553
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    References listed on IDEAS

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    1. Chengtian Ouyang & Donglin Zhu & Yaxian Qiu, 2021. "Lens Learning Sparrow Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, May.
    2. Wang, Jiang-Jiang & Yang, Kun & Xu, Zi-Long & Fu, Chao, 2015. "Energy and exergy analyses of an integrated CCHP system with biomass air gasification," Applied Energy, Elsevier, vol. 142(C), pages 317-327.
    3. Yang, G. & Zhai, X.Q., 2019. "Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition," Energy, Elsevier, vol. 174(C), pages 647-663.
    4. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
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    Cited by:

    1. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.

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