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Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method

Author

Listed:
  • Yang, Xiaohui
  • Leng, Zhengyang
  • Xu, Shaoping
  • Yang, Chunsheng
  • Yang, Li
  • Liu, Kang
  • Song, Yaoren
  • Zhang, Liufang

Abstract

The integration of microgrids and the combined cooling heating and power (CCHP) systems can foster a better utilization of energy. In order to achieve economic optimization and peak-load reduction of the CCHP microgrids model, this paper proposes a multi-objective optimal scheduling model for CCHP microgrids integrated with renewable energy, energy storage system and incentive based demand response. First, linearization methods are applied to change the original nonlinear optimization model into a mixed-integer linear programming (MILP) problem. Then, an augmented ε-constraint (AUGMECON) method is implemented to solve the multi-objective optimization problem (MOP). Finally, the final scheme is selected from the obtained Pareto optimal set by fuzzy clustering method according to the preference of decision maker. The results show that the CCHP microgrids is effective in reducing pollutant gas emissions and reducing the cost of treating them. And compared with the other four intelligent algorithms, the proposed MILP method has better accuracy and computational efficiency. In addition, with the inclusion of the peak-load shifting function, the interruptible load and the battery can effectively respond to peak load changes by shifting the peak of the exchange power curve in the point of common coupling (PCC) of the CCHP microgrids. In the end, the sensitivity analysis is carried out and the results present that electricity price, natural gas price, and the efficiency of PV have varying degrees of impact on model performance.

Suggested Citation

  • Yang, Xiaohui & Leng, Zhengyang & Xu, Shaoping & Yang, Chunsheng & Yang, Li & Liu, Kang & Song, Yaoren & Zhang, Liufang, 2021. "Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method," Renewable Energy, Elsevier, vol. 172(C), pages 408-423.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:408-423
    DOI: 10.1016/j.renene.2021.02.165
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    2. Liu, Zhijian & Fan, Guangyao & Sun, Dekang & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Lin, Xianping & Ai, Lei, 2022. "A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings," Energy, Elsevier, vol. 239(PE).
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    5. Chang, Jinwei & Li, Zhi & Huang, Yan & Yu, Xiaonan & Jiang, Ruicheng & Huang, Rui & Yu, Xiaoli, 2022. "Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved M-PSO algorithm," Energy, Elsevier, vol. 239(PE).
    6. Negri, Simone & Tironi, Enrico & Superti-Furga, Gabrio & Carminati, Marco, 2021. "VSC-based LVDC distribution network with DERs: Equivalent circuits for leakage and ground fault currents evaluation," Renewable Energy, Elsevier, vol. 177(C), pages 1133-1146.
    7. Yuan, Yu & Bai, Zhang & Zhou, Shengdong & Zheng, Bo & Hu, Wenxin, 2022. "Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Flexible demand response characteristics," Applied Energy, Elsevier, vol. 325(C).
    8. Bishwajit Dey & Soham Dutta & Fausto Pedro Garcia Marquez, 2023. "Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System," Sustainability, MDPI, vol. 15(3), pages 1-15, January.

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