Optimal Day-Ahead Scheduling for Active Distribution Network Considering Uncertainty

In recent days, the adoption of the battery energy storage system has increased in the electric grid due to the massive integration of renewable energy sources. This paper proposes a two-stage stochastic optimal day-ahead scheduling of an active distribution network considering uncertainty. It seeks to define an optimal day-ahead dispatch of battery energy storage systems and switchable capacitor banks to diminish operational costs incurred. The scheduling problem includes second-order cone programming power flow to capture distribution network features and assures the global optimal solution. The normal and beta probability distribution functions have been employed to model the forecast error uncertainty of load demand and generation from renewable resources, respectively. The Monte Carlo simulation approach is utilized to generate a large number of scenarios. Further, to make the proposed algorithm computationally efficient, these large number of scenarios are reduced to a small number using the Kantorovich probability distance approach ensuring problem tractability. The proposed framework is developed as a mixed-integer second-order cone programming problem, coded in GAMS, and solved with a GUROBI solver. The presented results on the modified IEEE 33-bus distribution network show reduced operating costs, reduced energy losses, decreased peak demand, and a notably enhanced voltage profile.