Day-ahead microgrid optimal self-scheduling: Comparison between three methods applied to isolated DC microgrid

Abstract

This paper presents three different methods to optimize a day-ahead self-scheduling for an isolated DC microgrid operation. Based on forecast data, a multi-objective cost function is formulated aiming to minimize the total energy cost by reducing the micro-turbine fuel consumption, avoiding photovoltaic power limitation and load shedding, while respecting the storage parameters and microgrid operation constraints. The problem is solved with two different optimization algorithms and one rule-based algorithm. The comparison is made on the total energy cost and computational cost for each proposed approach. The results prove that the mixed integer linear programing optimization permits to obtain the lower total energy cost with a reasonable computational cost.