Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions

This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into two-stage unit commitment optimization. objective consists in minimizing costs and/or equivalent carbon dioxide (CO 2 ) emissions. Based distributions forecasting errors the net demand, LOLP-based risk assessment method proposed to determine an appropriate amount reserve (OR) each time step next day. Then, first stage, deterministic optimization within mixed-integer linear programming (MILP) generates generators with day-ahead PV and load demand prediction prescribed OR requirement. In second future are considered. Hence, stochastic optimized order commit enough flexible handle unexpected deviations from predic-tions. methodology implemented local community. Results regarding available reserve, CO emissions established compared. About 15% economic environmental saved, compared while ensuring targeted security level.