Examining the influence of micro-grids topologies on optimal energy management systems decisions using genetic algorithm

Abstract

Micro-grids’ Energy Management Systems (EMSs) are systems of tools used to monitor, control and optimize the generation, delivery, and/or consumption of energy within micro-grids and the imported energy from their main grids. Unfortunately, there is no single EMS that can be implemented to improve energy efficiency, reduce greenhouse gas emissions, decrease fuel usage, minimize the amount of imported energy from the main grid, and increase the use of renewable energy for all micro-grids at the same time. Therefore, the main concern of this paper is to investigate the impact of micro-grid's topologies on developing an efficient micro-grid's EMS and an optimization model using Genetic Algorithm (GA) that aim to optimally select the objective function that suits each individual micro-grid's topology. Therefore, a comprehensive analysis is carried out taking into consideration three grid-connected micro-grids topologies; networks, loads profiles, embedded dispatchaple (biomass) and non-dispatchaple (PV) Distributed Generation (DG), storages elements, switching capacitors, and demand response. Three case studies (objective functions) are minimized; micro-grid's energy loss, imported energy from the main grid, and CO2 emissions, to select the most optimal objective function that suits each individual micro-grid topology. The obtained results are reported, evaluated, and discussed