Day-ahead scheduling for supply-demand-storage balancing - model predictive generation with interval prediction of photovoltaics

Large-scale penetration of photovoltaic (PV) power generators and storage batteries is expected into the power system in Japan. To maintain the supply-demand balance with energy storage, the optimal power generation and the charge/discharge power of storage batteries can be determined in a manner of the model predictive control of generators. In view of this, this paper addresses a problem of the day-ahead scheduling for the supply-demand-storage balance with explicit consideration of the model predictive power generation. This scheduling is performed by using demand prediction, whose uncertainty is expressed in terms of interval prediction. Formulating the day-ahead scheduling problem as an interval-valued allocation problem, we give a solution to it by taking an approach based on the monotonicity analysis with respect to the optimal solution. Finally, the efficiency of the proposed method is verified through a numerical simulation, where we use an interval prediction of PV power generation constructed by experimental data.