Two-stage hybrid stochastic/robust optimal coordination of distributed battery storage planning and flexible energy management in smart distribution network

Abstract

Abstract This paper presents a two-stage formwork for the coordinated distributed battery energy storage systems (DBESSs) planning and the flexible energy management (FEM) in a smart distribution network (SDN) in the presence of electric vehicles’ (EVs’) parking lot and variable renewable energy sources (VRESs). In the first stage, from distribution system operator's (DSO's) viewpoint, the linear DBESSs planning problem minimizes the difference between summation of its investment, degradation and charging costs and its revenue due to injecting power into the network at the discharging mode, where this problem subjects to the linear SDN optimal power flow equations and VRES, DBESS and EVs’ parking lot constraints. Noted that the bounded uncertainty–based robust model (BURM) is used to model the uncertainty of load, charging/discharging price, VRES power and EV parameters according to the uncertainty levels. In addition, the FEM strategy is applied to the SDN to obtain the suitable flexibility, security and operational indices based on the second stage problem formulation. This strategy minimizes the difference between energy cost paid to the upstream network and flexibility benefit from DSO and flexibility operator (FO) viewpoints while it considers linear AC optimal power flow and renewable and flexible sources equations as problem constraints. Moreover, in order to achieve the robust flexibility capability of EVs’ parking lot in the SDN, the uncertainty of the FEM strategy is modelled in the proposed hybrid stochastic/robust optimization. Hence, the scenario-based stochastic programming (SBSP) is used for the uncertain parameters of load, energy price and VRES power, but, BURM models the EVs uncertainty. Finally, the proposed two-stage formwork is simulated on the 19-bus LV CIGRE benchmark grid using GAMS software to investigate the capability and efficiency of the model. According to numerical results, the proposed strategy calculates the optimal location and size for DBESSs depending on the energy price, consumers and EVs demand and VRESs size in the SDN, and thus, it can obtain flexible, secure and efficient operation indices in this network based on the lowest possible cost for the investment of DBESSs.