Open AccessRobust energy hub optimization with cross‐vector demand response
Author(s) -
Zhu Feng,
Fu Jingqi,
Zhao Pengfei,
Xie Da
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12559
Subject(s) - demand response , renewable energy , power to gas , computer science , energy storage , flexibility (engineering) , electricity , electric power system , automotive engineering , engineering , power (physics) , electrical engineering , economics , chemistry , physics , electrolysis , management , electrode , quantum mechanics , electrolyte
Summary The integration of multi‐energy systems increases renewable penetration and efficiency of energy use, and reduces costs. This integration is further reinforced by new technologies, such as cross‐vector demand response and power‐to‐gas technologies, bringing big flexibility to system operation. This paper studies the optimal operation of multi‐vector energy systems, considering cross‐vector demand response with power‐to‐gas technology by using robust optimization. An energy hub system (EHS) is considered as the realization of a multi‐vector energy system, which consists of (a) the two‐way multi‐energy converters between electricity and gas, (b) an energy storage system and (c) renewable energy resources. The demand response to energy price variations in both electricity and heat is considered, where customers can change their energy consumption behaviors motivated by pricing mechanisms. In the EHS, energy conversion, storage, production and consumption are all modeled. To handle the uncertainty from renewable power generation, robust optimization is used with a hybrid box‐polyhedral uncertainty set for solving the built model. Case studies demonstrate that the proposed method can benefit the system operators from reducing the EHS operation cost with sound computational tractability.