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Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models

Author

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  • Zeli Ye

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Wentao Huang

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Jinfeng Huang

    (Guangxi Power Grid Corporation Wuzhou Power Supply Bureau, Wuzhou 543000, China)

  • Jun He

    (Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China)

  • Chengxi Li

    (Guangxi Power Grid Corporation Wuzhou Power Supply Bureau, Wuzhou 543000, China)

  • Yan Feng

    (Wuhan Huayuan Electric Power Design Institute, Wuhan 430056, China)

Abstract

The economics of integrated community energy system (ICES) dispatch schemes are influenced by the accuracy of the parameters of the different energy-conversion-equipment models. Traditional equipment efficiency correction models only take into account the historical load factors and variations in the environmental factors, ignoring the fact that the input data do not come from the actual operating data of the equipment, which affects the accuracy of the equipment models and therefore reduces the economics of ICES dispatch solutions. Therefore, this paper proposes an optimal scheduling of a community-integrated energy system based on twin data, considering a device-correction model that combines an energy hub model and a twin data correction model. Firstly, a dynamic energy hub (DEH) model with a correctable conversion efficiency is developed based on the twin data; secondly, a physical model of the system and a digital twin are established, with the prediction data as the input of the digital twin and the twin data as the output. Polynomial regression (PR) and a back propagation neural network (BPNNS) are used to process the twin data to accurately extract the equipment conversion efficiency. Considering the lack of accuracy of traditional prediction methods, a prediction model combining a long- and short-term-memory neural network and digital-twin technology is constructed for renewable energy generation and load prediction. The simulation results show that using twin data to correct the equipment efficiency reduces the average absolute error and average relative error by 4.6706 and 1.18%, respectively, when compared with the use of historical data. Compared with the actual total cost of the dispatch, the total cost of the dispatch after the equipment efficiency correction was reduced by USD 850.19.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1360-:d:1048745
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    References listed on IDEAS

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    1. 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.
    2. Lin, Wei & Jin, Xiaolong & Jia, Hongjie & Mu, Yunfei & Xu, Tao & Xu, Xiandong & Yu, Xiaodan, 2021. "Decentralized optimal scheduling for integrated community energy system via consensus-based alternating direction method of multipliers," Applied Energy, Elsevier, vol. 302(C).
    3. You, Minglei & Wang, Qian & Sun, Hongjian & Castro, Iván & Jiang, Jing, 2022. "Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties," Applied Energy, Elsevier, vol. 305(C).
    4. Mu, Yunfei & Xu, Yurui & Cao, Yan & Chen, Wanqing & Jia, Hongjie & Yu, Xiaodan & Jin, Xiaolong, 2022. "A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model," Applied Energy, Elsevier, vol. 323(C).
    5. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    6. Ying Ji & Jianhui Wang & Jiacan Xu & Donglin Li, 2021. "Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-19, April.
    7. Shan Deng & Qinghua Wu & Zhaoxia Jing & Lilan Wu & Feng Wei & Xiaoxin Zhou, 2017. "Optimal Capacity Configuration for Energy Hubs Considering Part-Load Characteristics of Generation Units," Energies, MDPI, vol. 10(12), pages 1-19, November.
    8. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    9. Mu, Yunfei & Chen, Wanqing & Yu, Xiaodan & Jia, Hongjie & Hou, Kai & Wang, Congshan & Meng, Xianjun, 2020. "A double-layer planning method for integrated community energy systems with varying energy conversion efficiencies," Applied Energy, Elsevier, vol. 279(C).
    10. Saletti, Costanza & Morini, Mirko & Gambarotta, Agostino, 2022. "Smart management of integrated energy systems through co-optimization with long and short horizons," Energy, Elsevier, vol. 250(C).
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    1. Dedi Li & Yue Zong & Xinjie Lai & Huimin Huang & Haiqi Zhao & Shufeng Dong, 2023. "Chance-Constrained Dispatching of Integrated Energy Systems Considering Source–Load Uncertainty and Photovoltaic Absorption," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

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