Co-optimisation model for the long-term design and decision making in community level cloud energy storage system

作     者：Li, Xiangyu Chen, Guo Dong, Zhao Yang 

作者机构：Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia 

出 版 物：《IET RENEWABLE POWER GENERATION》 (IET. Renew. Power Gener.)

年 卷 期：2020年第14卷第17期

页      面：3518-3525页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0808[工学-电气工程] 08[工学] 

基　　金：Australian Research Council [DP180103217, FT190100156, IH180100020] UNSW Digital Grid Futures Institute, UNSW Sydney, under a cross-disciplinary fund scheme Australian Research Council [IH180100020, FT190100156] Funding Source: Australian Research Council 

主　　题：power generation economics decision making power grids optimisation reactive power demand side management battery storage plants photovoltaic power systems power conversion secondary cells power generation control co-optimisation model long-term design community level cloud energy storage system photovoltaic generation demand response pricy energy storage equipment battery energy storage system local residential consumers small commercial consumers CESS operator economic operating strategy power conversion system reactive power compensation equipment battery degradation cost CESS node power exchange centralised energy storage system multiple energy storage systems residential consumers price uncertainty decision making process mixed-integer linear programming Australia 

摘      要：Deploying the cloud energy storage system (CESS) is an economic and efficient way to store excess photovoltaic generation and participate in demand response without personal investment on pricy energy storage equipment. It is a shared battery energy storage system (BESS) for local residential and small commercial consumers, which is designed and controlled by the CESS operator. Based on the profit purpose, the CESS operator not only pursues the most economic operating strategy, but also tries to minimize the total investment on the design stage. This paper considers the investment on the batteries, power conversion system, reactive power compensation equipment and the cost including battery degradation cost and operation cost. The electricity price uncertainty and the voltage deviation of the CESS node caused by power exchange are also considered. Moreover, the cases of a largely centralized energy storage system and multiple distributed energy storage systems are all modelled. Finally, an original robust cooptimization model is transferred to a mixed integer linear programming model (MILP) and solved in GAMS. Numerical results based on historical data from 300 residential consumers in Australia present that the battery degradation cost and price uncertainty can t be neglected.