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Research on Multi-Time Scale Optimization Strategy of Cold-Thermal-Electric Integrated Energy System Considering Feasible Interval of System Load Rate

Author

Listed:
  • Bin Ouyang

    (Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China
    Department of Electrical Engineer, Tsinghua University, Beijing 100084, China)

  • Zhichang Yuan

    (Department of Electrical Engineer, Tsinghua University, Beijing 100084, China)

  • Chao Lu

    (Department of Electrical Engineer, Tsinghua University, Beijing 100084, China)

  • Lu Qu

    (Department of Electrical Engineer, Tsinghua University, Beijing 100084, China)

  • Dongdong Li

    (Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

The integrated energy system coupling multi-type energy production terminal to realize multi-energy complementarity and energy ladder utilization is of great significance to alleviate the existing energy production crisis and reduce environmental pollution. In this paper, the topology of the cold-thermal-electricity integrated energy system is built, and the decoupling method is adopted to analyze the feasible interval of load rate under the strong coupling condition, so as to ensure the “source-load” power balance of the system. Establishing a multi-objective optimization function with the lowest system economic operation and pollution gas emission, considering the attribute differences and energy scheduling characteristics of different energy sources of cold, heat and electricity, and adopting different time scales to optimize the operation of the three energy sources of cold, heat and electricity, wherein the operation periods of electric energy, heat energy and cold energy are respectively 15 min, 30 min and 1 h; The multi-objective problem is solved by standard linear weighting method. Finally, the mixed integer nonlinear programming model is calculated by LINGO solver. In the numerical simulation, the hotel summer front load parameters of Zhangjiakou, China are selected for simulation and compared with a single time scale system. The simulation results show that the multi-time scale system reduces the economic operation cost by 15.6% and the pollution gas emission by 22.3% compared with the single time scale system, it also has a wider feasible range of load rate, flexible time allocation, and complementary energy selection.

Suggested Citation

  • Bin Ouyang & Zhichang Yuan & Chao Lu & Lu Qu & Dongdong Li, 2019. "Research on Multi-Time Scale Optimization Strategy of Cold-Thermal-Electric Integrated Energy System Considering Feasible Interval of System Load Rate," Energies, MDPI, vol. 12(17), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3233-:d:259894
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    References listed on IDEAS

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    1. Cho, Heejin & Mago, Pedro J. & Luck, Rogelio & Chamra, Louay M., 2009. "Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme," Applied Energy, Elsevier, vol. 86(12), pages 2540-2549, December.
    2. Yongli Wang & Haiyang Yu & Mingyue Yong & Yujing Huang & Fuli Zhang & Xiaohai Wang, 2018. "Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss," Energies, MDPI, vol. 11(7), pages 1-21, June.
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