Optimal Configuration of Energy Storage System Based on Probabilistic Power Flow and the MILP Optimization

Energy storage systems(ESS) play an important role in the new power system to alleviate network congestion and improve renewable energy consumption. When optimizing the design of ESS, the heavy bilateral fluctuation of source and load sides must be considered in the planning model. Probabilistic load flow has advantages in handling multi spatiotemporal generation and load uncertainties. This work proposes a novel optimal planning model of ESS based on probabilistic load flow constraints, aiming to minimize the total cost of system investment and operation. In order to deal with the nonlinear probabilistic load flow constraints, the cumulant method was used to transfer the original nonlinear model into a mixedinteger linear programming model, which can be solved by calling Cplex solver. The case study shows that under high penetration of renewable energy, the optimal ESS planning model based on probabilistic load flow constraints can reduce renewable energy curtailment economically, reducing the risk of redundant investment with a reasonable set of confidence probabilistic load flow.