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Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages

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Listed:
  • He, Yi
  • Guo, Su
  • Zhou, Jianxu
  • Ye, Jilei
  • Huang, Jing
  • Zheng, Kun
  • Du, Xinru

Abstract

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models; (2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ); (3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions.

Suggested Citation

  • He, Yi & Guo, Su & Zhou, Jianxu & Ye, Jilei & Huang, Jing & Zheng, Kun & Du, Xinru, 2022. "Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages," Renewable Energy, Elsevier, vol. 184(C), pages 776-790.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:776-790
    DOI: 10.1016/j.renene.2021.11.116
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    7. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    8. Guo, Jiacheng & Liu, Zhijian & Wu, Xuan & Wu, Di & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Two-layer co-optimization method for a distributed energy system combining multiple energy storages," Applied Energy, Elsevier, vol. 322(C).
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    10. Shang, Ce & Ge, Yuyou & Zhai, Suwei & Huo, Chao & Li, Wenyun, 2023. "Combined heat and power storage planning," Energy, Elsevier, vol. 279(C).
    11. Yi Yan & Xuerui Wang & Ke Li & Xiaopeng Kang & Weizheng Kong & Hongcai Dai, 2022. "Tri-Level Integrated Optimization Design Method of a CCHP Microgrid with Composite Energy Storage," Sustainability, MDPI, vol. 14(9), pages 1-29, April.
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