文献类型：期刊文献

英文题名：Optimal economic management of an electric vehicles aggregator by using a stochastic p-robust optimization technique

作者：Sriyakul, Thanaporn[1];Jermsittiparsert, Kittisak[2,3,4]

第一作者：Sriyakul, Thanaporn

通讯作者：Jermsittiparsert, K[1];Jermsittiparsert, K[2];Jermsittiparsert, K[3]

机构：[1]Mahanakorn Univ Technol, Fac Business Adm, Bangkok 10530, Thailand;[2]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[3]Duy Tan Univ, Fac Informat Technol, Da Nang 550000, Vietnam;[4]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China

第一机构：Mahanakorn Univ Technol, Fac Business Adm, Bangkok 10530, Thailand

通讯机构：[1]corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[2]corresponding author), Duy Tan Univ, Fac Informat Technol, Da Nang 550000, Vietnam;[3]corresponding author), Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.|[1048419]河南财经政法大学MBA学院;[10484]河南财经政法大学;

年份：2020

卷号：32

外文期刊名：JOURNAL OF ENERGY STORAGE

收录：;EI(收录号：20204409435202);Scopus(收录号：2-s2.0-85094316780);WOS:【SCI-EXPANDED(收录号:WOS:000600396800001)】；

语种：英文

外文关键词：Electric vehicle aggregator; Market price uncertainty; Stochastic p-robust optimization technique; Stochastic optimization technique; Maximum relative regret; Day-ahead energy and reserve markets

摘要：Electricity market price is one of the significant uncertainties of the electricity market, which imposes a severe pack of challenges on electric vehicle aggregators (EVAs). Optimal scheduling of the EVA in day-ahead energy and reserve markets can be challenging due to the electricity market price. In this paper, we proposed a robust and optimal scheduling method for an electric vehicle aggregator (EVA) to establish robustness parameters that are needed for the day-ahead scheduling. This proposed method is one of the newest optimization approaches, which is called stochastic p-robust optimization technique (SPROT), in which the purpose is to maximize the expected profit of the EVA and to minimize the maximum relative regret (MRR) in the worst case. In this regard, this proposed technique is applied through SPROT-based form versus the stochastic optimization technique (SOT) one. This problem forms mixed-integer linear programming (MILP) model, which is solved in GAMS optimization software using CPLEX solver. As results indicated, there is a slow reduction of 3.9% in the expected profit of the EVA and a sharp falling of 46.91% of MRR in the SPROT-based model in comparison with the SOT based model. Therefore, results reveal the robustness and effectiveness of the proposed SPROT-based model.