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

Chengyu Zeng, Yuechun Jiang, Yuqing Liu, Zuoyun Tan, Zhongnan He and Shuhong Wu
Additional contact information
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

Energies, 2019, vol. 12, issue 16, 1-17

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.

Keywords: integrated energy; energy hub technology; multi-agent; renewable energy; NSGA-III (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/16/3112/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/16/3112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:16:p:3112-:d:257349

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().