A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid

In smart grids, one of the most important objectives is the ability to improve the grid's situational awareness and allow for fast-acting changes in power generation. In such systems, an energy management system should gather all the needed information, solve an optimization problem, and communicate back to each distributed energy resource (DER) its correct allocation of energy. This paper proposes a memory-based genetic algorithm (MGA) that optimally shares the power generation task among a number of DERs. The MGA is utilized for minimization of the energy production cost in the smart grid framework. It shares optimally the power generation in a microgrid including wind plants, photovoltaic plants, and a combined heat and power system. In order to evaluate the performance of the proposed approach, the results obtained by the MGA are compared with the results found by a genetic algorithm and two variants of particle swarm optimization. Simulation results accentuate the superiority of the proposed MGA technique.