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Economic model predictive control of integrated energy systems: A multi-time-scale framework

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  • Wu, Long
  • Yin, Xunyuan
  • Pan, Lei
  • Liu, Jinfeng

Abstract

An integrated energy system (IES) that comprises diverse operating units typically exhibits time-scale multiplicity in its dynamics, which makes the application of predictive control schemes to such an IES challenging. In this work, a multi-time-scale framework for subsystem decomposition and the corresponding economic model predictive control (EMPC) design is proposed to explicitly address the time-scale-multiplicity and optimal control of IESs. Specifically, a stand-alone IES is considered. According to the time-scale multiplicity, the IES is decomposed into three reduced-order subsystems with slow, medium, and fast dynamics. A composite EMPC (CEMPC) designed based on the subsystems is proposed to dynamically coordinate the operations of the operating units, aiming at meeting customers’ electricity and cooling demands while reducing fuel consumption. In the design of the CEMPC, a zone tracking objective for the building temperature regulation is adopted to maintain thermal comfort, reduce operating costs, and achieve more control degrees of freedom for satisfying the electricity demands. The proposed CEMPC is composed of a short-term distributed EMPC and a long-term EMPC. The short-term distributed EMPC consists of three local EMPCs corresponding to the slow, medium and fast dynamics of the IES. The long-term EMPC optimizes the operations of the IES taking into account long-term forecasts for external conditions. These EMPCs communicate to cooperate in decision-making. Extensive simulations are carried out to evaluate the performance of the proposed CEMPC framework compared with a hierarchical real-time optimization (HRTO) framework. The simulation results show that the proposed CEMPC framework can increase the system’s overall performance by about 5% if set-point tracking is used and by about 30% if zone tracking is incorporated. The proposed CEMPC framework is also much more computationally efficient and reduces the computation time by over 65%. The proposed CEMPC framework fully leverages the capability of each operating unit to provide superior electricity load tracking and economic performance while maintaining customers’ thermal comfort. Our studies also indicate that the proposed zone tracking approach to building temperature significantly enhances the operational flexibility of IESs in satisfying customized requirements.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014441
    DOI: 10.1016/j.apenergy.2022.120187
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    1. Dong, Xiangxiang & Wu, Jiang & Xu, Zhanbo & Liu, Kun & Guan, Xiaohong, 2022. "Optimal coordination of hydrogen-based integrated energy systems with combination of hydrogen and water storage," Applied Energy, Elsevier, vol. 308(C).
    2. Dowling, Alexander W. & Kumar, Ranjeet & Zavala, Victor M., 2017. "A multi-scale optimization framework for electricity market participation," Applied Energy, Elsevier, vol. 190(C), pages 147-164.
    3. Gao, Xian & Knueven, Bernard & Siirola, John D. & Miller, David C. & Dowling, Alexander W., 2022. "Multiscale simulation of integrated energy system and electricity market interactions," Applied Energy, Elsevier, vol. 316(C).
    4. 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).
    5. Ghilardi, Lavinia Marina Paola & Castelli, Alessandro Francesco & Moretti, Luca & Morini, Mirko & Martelli, Emanuele, 2021. "Co-optimization of multi-energy system operation, district heating/cooling network and thermal comfort management for buildings," Applied Energy, Elsevier, vol. 302(C).
    6. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    7. Li, Peng & Wang, Zixuan & Wang, Jiahao & Guo, Tianyu & Yin, Yunxing, 2021. "A multi-time-space scale optimal operation strategy for a distributed integrated energy system," Applied Energy, Elsevier, vol. 289(C).
    8. Jin, Yuhui & Wu, Xiao & Shen, Jiong, 2022. "Power-heat coordinated control of multiple energy system for off-grid energy supply using multi-timescale distributed predictive control," Energy, Elsevier, vol. 254(PB).
    9. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    10. Yun Zhang & Chenghui Zhang & Naxin Cui, 2013. "An Adaptive Estimation Scheme for Open-Circuit Voltage of Power Lithium-Ion Battery," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-6, December.
    11. Pan, Zhaoguang & Guo, Qinglai & Sun, Hongbin, 2017. "Feasible region method based integrated heat and electricity dispatch considering building thermal inertia," Applied Energy, Elsevier, vol. 192(C), pages 395-407.
    12. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
    13. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    14. 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.
    15. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
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