Optimized allocation of energy storage in a microgrid using genetic algorithms

Abstract

Microgrids combine intermittent and renewable energy sources with energy storage technologies and local loads. Metaheuristics-based algorithms can be suitable for optimally allocating storage elements throughout the grid, maximizing the use of intermittent resources, and reducing operating costs. In order to evaluate the impact of allocation on power quality, the present work used genetic algorithms to reduce power exchange with the distribution system while maintaining the voltage levels close to nominal. The genetic algorithm used was implemented in Matlab integrated with Simulink, where the microgrid was modelled. Furthermore, this study considered wind turbines and photovoltaic panels operating at maximum generation or out of operation. As a result, the algorithm prioritized the allocation to nodes at the ends of the branches, considering different combinations of generation and loading, improving the microgrid’s voltage profile and reducing power exchange with the distribution system.