Evolutionary optimization algorithms applied to demand dispatch via stochastic mixed-integer-programming

This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that the grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. The load not dispatched by the operator is not known with certainty, and is represented as a stochastic parameter in the optimization problem. Consumption restrictions associated with flexible demand are modeled by equality energy constraints. The performance of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, is presented.