Microgrid sizing with combined evolutionary algorithm and MILP unit commitment

Microgrids are small scale power systems with local resources for generation, consumption and storage, that can operate connected to the main grid or islanded. In such systems, optimal sizing of components is necessary to ensure secure and reliable energy supply to loads at the least cost. Sizing results are however dependent on the energy management strategy used for operating the system, especially when components with different dynamics are considered. Results are also impacted by uncertainty on load as well as renewable generation. In this paper, we propose a combined sizing and energy management methodology, formulated as a leader-follower problem. The leader problem focuses on sizing and aims at selecting the optimal size for the microgrid components. It is solved using a genetic algorithm. The follower problem, i.e., the energy management issue, is formulated as a unit commitment problem and is solved with a mixed integer linear program. Uncertainties are considered using a form of robust optimization method. Several scenarios are modeled and compared in simulations to show the effectiveness of the proposed method, especially with respect to a simple rule-based strategy.