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Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance

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  • Liu, Chunming
  • Wang, Chunling
  • Yin, Yujun
  • Yang, Peihong
  • Jiang, Hui

Abstract

Integrated energy systems have recently attracted interest for the purpose of energy development and utilization owing to the increase in environmental pollution and the shortage of fossil energy. However, the integration of a high proportion of renewable energies and controllable loads in the system increases the uncertainty of system operation significantly. In order to rapidly track the system fluctuations and accurately control the operating equipment, a bi-level and multi-timescale dispatch and control strategy based on model predictive control is proposed for a community integrated energy system (CIES) considering dynamic response performance. The upper-level optimizer completes the rolling forecast of renewable outputs and loads over a long-time horizon, and builds an economic rolling optimal scheduling model with consideration of feedback correction. The lower-level controller establishes a closed-loop dynamic performance optimization control model over a short time scale by real-time controlling the system’s upstream energy equipment. The simulation results on a modified CIES located in Beijing, China demonstrate that the proposed bi-level strategy increases the prediction accuracy while ensuring the economic efficiency of the system operation, and improves the dynamic tracking ability of the equipment and the overall energy supply dynamic response rate of the system, which provides a fundamental way for efficient application of CIES.

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  • Liu, Chunming & Wang, Chunling & Yin, Yujun & Yang, Peihong & Jiang, Hui, 2022. "Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922001106
    DOI: 10.1016/j.apenergy.2022.118641
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    1. Na Li & Rudi Hakvoort & Zofia Lukszo, 2021. "Cost Allocation in Integrated Community Energy Systems—Social Acceptance," Sustainability, MDPI, vol. 13(17), pages 1-24, September.
    2. Lei, Yang & Wang, Dan & Jia, Hongjie & Li, Jiaxi & Chen, Jingcheng & Li, Jingru & Yang, Zhihong, 2021. "Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties," Applied Energy, Elsevier, vol. 300(C).
    3. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
    4. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    5. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2021. "Cost allocation in integrated community energy systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Quitoras, Marvin Rhey & Campana, Pietro Elia & Rowley, Paul & Crawford, Curran, 2020. "Remote community integrated energy system optimization including building enclosure improvements and quantitative energy trilemma metrics," Applied Energy, Elsevier, vol. 267(C).
    7. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    8. Kang, Jing & Wang, Shengwei & Yan, Chengchu, 2019. "A new distributed energy system configuration for cooling dominated districts and the performance assessment based on real site measurements," Renewable Energy, Elsevier, vol. 131(C), pages 390-403.
    9. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    10. Wang, Haiyang & Zhang, Chenghui & Li, Ke & Liu, Shuai & Li, Shuzhen & Wang, Yu, 2021. "Distributed coordinative transaction of a community integrated energy system based on a tri-level game model," Applied Energy, Elsevier, vol. 295(C).
    11. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
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    3. Wenbo Hao & Maoda Xu & Junming Lin & Lida Fu & Xiaonan Cao & Qingquan Jia, 2024. "Multi-Time Scale Cloud-Edge Collaborative Scheduling Strategy for Distribution Network Considering Spatiotemporal Characteristics of Demand Response," Energies, MDPI, vol. 17(8), pages 1-28, April.
    4. Shen, Weijie & Zeng, Bo & Zeng, Ming, 2023. "Multi-timescale rolling optimization dispatch method for integrated energy system with hybrid energy storage system," Energy, Elsevier, vol. 283(C).
    5. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2022. "Economic model predictive control of integrated energy systems: A multi-time-scale framework," Applied Energy, Elsevier, vol. 328(C).
    6. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
    7. Zhang, Chaoyi & Jiao, Zaibin & Liu, Junshan & Ning, Keer, 2023. "Robust planning and economic analysis of park-level integrated energy system considering photovoltaic/thermal equipment," Applied Energy, Elsevier, vol. 348(C).
    8. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Huang, Congzhi, 2024. "A hierarchical reinforcement learning GPC for flexible operation of ultra-supercritical unit considering economy," Energy, Elsevier, vol. 289(C).

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