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Optimal Evolutionary Dispatch for Integrated Community Energy Systems Considering Uncertainties of Renewable Energy Sources and Internal Loads

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  • Xinghua Liu

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Shenghan Xie

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Chen Geng

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Jianning Yin

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Gaoxi Xiao

    (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Hui Cao

    (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

For the future development of integrated energy systems with high penetration of renewable energy, an integrated community energy systems (ICES) dispatch model is proposed including various renewable energy sources and energy conversion units. Energy coupling matrices of ICES based on traditional energy hub (EH) models are constructed. Uncertainties of long-term forecast data of renewable energy sources and internal loads are depicted by multi-interval uncertainty sets (MIUS). To cope with the impacts caused by uncertainties of renewable energy sources and internal loads, the whole dispatch process is divided into two stages. Considering various constraints of ICES, we solved the dispatch model through the improved particle swarm optimization (IPSO) algorithm in the first stage. The optimal evolutionary dispatch is then proposed in the second stage to overcome the evolution and errors of short-term forecast data and obtain the optimal dispatch plan. The effectiveness of the proposed dispatch method is demonstrated using an example considering dramatic uncertainties. Compared with the traditional methods, the proposed dispatch method effectively reduces system operating costs and improves the environmental benefits, which helps to achieve a win-win situation for both energy companies and users.

Suggested Citation

  • Xinghua Liu & Shenghan Xie & Chen Geng & Jianning Yin & Gaoxi Xiao & Hui Cao, 2021. "Optimal Evolutionary Dispatch for Integrated Community Energy Systems Considering Uncertainties of Renewable Energy Sources and Internal Loads," Energies, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3644-:d:577696
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    References listed on IDEAS

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    Cited by:

    1. Daniela Proto, 2022. "Renewable Energy Communities as an Enabling Framework to Boost Flexibility and Promote the Energy Transition," Energies, MDPI, vol. 15(23), pages 1-4, November.
    2. Xinyu Sun & Hao Wu & Siqi Guo & Lingwei Zheng, 2022. "Day-Ahead Optimal Scheduling of Integrated Energy System Based on Type-II Fuzzy Interval Chance-Constrained Programming," Energies, MDPI, vol. 15(18), pages 1-17, September.
    3. Zeli Ye & Wentao Huang & Jinfeng Huang & Jun He & Chengxi Li & Yan Feng, 2023. "Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models," Energies, MDPI, vol. 16(3), pages 1-22, January.

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