Modeling of optimal reconfigurable microgrid for electric vehicle integration

Abstract

The world is experiencing the impact of climate change, such as rising sea levels, extreme weather, natural disaster, and reduced food production. These issues strongly correlate with the global warming phenomenon caused by high carbon emissions; in fact, the transportation sector is recorded as one of the highest contributors. Therefore, many countries are phasing out internal combustion engine (ICE) usage by replacing them with more environment-friendly electric vehicles (EVs). However, as essential infrastructure in the EV ecosystem, the EV charging station must be installed in huge numbers at various locations. Excessive charging loads could cause challenges to the microgrid, such as harmonic distortion, voltage instability, and high-power losses. As a solution, microgrid reconfiguration modeling is needed. Hence, this research develops an optimum reconfigurable microgrid to minimize power losses and increase voltage stability. The most efficient metaheuristic method, Cuckoo Search Algorithm (CSA) is used to find the best reconfiguration as it is involved with the multi-objectives problem, in comparison to Genetic Algorithm (GA) as the second most efficient metaheuristic method and Particle Swarm Optimization (PSO) as the least most efficient metaheuristic method. The two different scales of bus networks, IEEE-33 bus, and IEEE-69 bus, are utilized as a microgrid test model in various charging conditions. The simulation results show the power losses decreased up to 99.47 %, while the voltage stability index (VSI) value increased up to 6.1386 approximately with integration of EVs load. Moreover, the compared results with GA and PSO algorithm show that the CSA performed better in terms of power loss reduction and voltage stability for all cases.