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Optimal Dispatch of Integrated Energy System Considering Energy Hub Technology and Multi-Agent Interest Balance

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  • Chengyu Zeng

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Yuechun Jiang

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Yuqing Liu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Zuoyun Tan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Zhongnan He

    (State Grid Yongzhou Power Supply Company, Yongzhou 425000, China)

  • Shuhong Wu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

With the gradual liberalization of the energy market, the future integrated energy system will be composed of multiple agents. Therefore, this paper proposes an optimization dispatch method considering energy hub technology and multi-agent interest balance in an integrated energy system. Firstly, an integrated energy system, including equipment for cogeneration, renewable energy, and electric vehicles, is established. Secondly, energy hub technologies, such as demand response, electricity storage, and thermal storage, are comprehensively considered, and the integrated energy system is divided into three agents: Integrated energy service providers, renewable energy owners, and users, respectively. Then, with the goal of balancing the interests of each agent, the model is solved by the non-dominated sorting genetic algorithm-III (NSGA-III) to obtain the Pareto frontier. Since the Pareto frontier is a series of values, the optimal solution of each agent in the Pareto frontier is found by the technical for order preference with a similar to ideal solution (TOPSIS). Ultimately, taking an integrated energy demonstration park in China as a case study, the function of energy hub technology is analyzed by simulation, and the proposed method is verified to be effective and practicable.

Suggested Citation

  • Chengyu Zeng & Yuechun Jiang & Yuqing Liu & Zuoyun Tan & Zhongnan He & Shuhong Wu, 2019. "Optimal Dispatch of Integrated Energy System Considering Energy Hub Technology and Multi-Agent Interest Balance," Energies, MDPI, vol. 12(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3112-:d:257349
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    References listed on IDEAS

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    5. Yining Zhang & Yubin He & Mingyu Yan & Chuangxin Guo & Yi Ding, 2018. "Linearized Stochastic Scheduling of Interconnected Energy Hubs Considering Integrated Demand Response and Wind Uncertainty," Energies, MDPI, vol. 11(9), pages 1-23, September.
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    Cited by:

    1. Ruiqiu Yao & Yukun Hu & Liz Varga, 2023. "Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review," Energies, MDPI, vol. 16(5), pages 1-36, March.
    2. Khalid Alnowibet & Andres Annuk & Udaya Dampage & Mohamed A. Mohamed, 2021. "Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework," Sustainability, MDPI, vol. 13(21), pages 1-32, October.
    3. Liu, Xinrui & Hou, Min & Sun, Siluo & Wang, Jiawei & Sun, Qiuye & Dong, Chaoyu, 2022. "Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method," Energy, Elsevier, vol. 254(PB).

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