Open AccessA collaborative planning methodology of energy stations and energy networks in integrated energy system
Author(s) -
Zhang Hanlin,
Zhou Suyang,
Gu Wei,
Liu Zhong,
Zhan Xin,
Liu Hengmen
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12551
Subject(s) - energy (signal processing) , computer science , topology (electrical circuits) , energy management , energy storage , energy flow , mathematical optimization , network topology , relaxation (psychology) , energy consumption , energy engineering , distributed computing , efficient energy use , power (physics) , engineering , computer network , electrical engineering , mathematics , psychology , social psychology , statistics , physics , quantum mechanics
Investments in the energy networks will significantly influence the outcome of Integrated Energy System (IES) planning. It is difficult to optimally plan the energy stations and energy networks of IES while taking into consideration the energy network investments. Thus, a collaborative planning methodology is proposed for energy stations and energy networks containing multiple energy carriers, which can optimize both the capacity of the energy devices and networks and the topology of the energy networks. The linearization for the energy storage devices and electric air conditioning models, as well as the second‐order relaxation for the power grid and gas network models are implemented, and the optimization model is subsequently transformed into a mixed‐integer second‐order cone programming (MISOCP) form which can be directly solved by the commercial solvers. Numerical analysis is carried out to prove the effectiveness of the proposed model and method and the results show that energy networks with optimal topology can significantly improve the energy utilization efficiency of the system and reduce the total cost. In addition, the investment cost of the energy networks plays a decisive role in the network topology as well as the way how the energy is supplied to energy stations.