文献类型：期刊文献

英文题名：Robust optimization of renewable-based multi-energy micro-grid integrated with flexible energy conversion and storage devices

作者：Lekvan, Amir Aris[1]; Habibifar, Reza[2]; Moradi, Mehran[1]; Khoshjahan, Mohammad[3]; Nojavan, Sayyad[4]; Jermsittiparsert, Kittisak[5,6]

第一作者：Lekvan, Amir Aris

通讯作者：Jermsittiparsert, Kittisak

机构：[1] Department of Electrical and Computer Engineering, Tarbiat Modares University, Iran; [2] Electrical Engineering Department, Sharif University of Technology, Iran; [3] Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, United States; [4] Department of Electrical Engineering, University of Bonab, Bonab, Iran; [5] MBA School, Henan University of Economics and Law, Zhengzhou, China; [6] Political Science Association of Kasetsart University, Bangkok, Thailand

第一机构：Department of Electrical and Computer Engineering, Tarbiat Modares University, Iran

年份：2021

卷号：64

外文期刊名：Sustainable Cities and Society

收录：EI(收录号：20204209354091);Scopus(收录号：2-s2.0-85092502613)

语种：英文

外文关键词：Electric energy storage - Electric vehicles - Optimization - Energy conversion - Virtual storage - Decision making - Electric power transmission networks - Scheduling - Stochastic systems - Vehicle-to-grid - Stochastic models

摘要：This paper presents a new model for optimal scheduling of renewable-based multi-energy microgrid (MEM) systems incorporated with emerging high-efficient technologies such as electric vehicle (EVs) parking lots, power-to-gas (P2G) facility, and demand response programs. The proposed MEM is equipped with wind energy, multi-carrier energy storage technologies, boiler, combined heat and power unit, P2G, EVs, and demand response with the aim of total operational cost minimization. Meanwhile, the system operator can participate in three electricity, heat, and gas market to meet local demands as well as achieve desired profits through energy exchanges. The proposed MEM is exposed to high-level uncertainties due to wind energy, demand, the initial and final state of charge of EVs, arrival and departure times of EVs, as well as power price. A hybrid robust/stochastic framework is used to capture all random variables and distinguishes between the level of conservatism in the decision-making procedure. The electricity price uncertainty is addressed by a robust approach, while a stochastic framework models other uncertainties of the system. Simulations are provided for different cases, which results revealed that the integrated scheduling of MEM in the presence of emerging technologies, incorporated with vehicle-to-grid (V2G) capability, reduces the total operational cost by 14.2 %. ? 2020