Optimization and Control Aspects of Micro Grids Considering EV Integration

Abstract

The rapid global adoption of electric vehicles (EVs) and the growing integration of renewable energy sources (RESs), such as solar and wind, have significantly influenced the design and operation of modern energy systems. These trends bring promising opportunities but also introduce challenges such as energy intermittency, grid stability, infrastructure readiness, and cost optimisation. This thesis explores the intelligent integration of EVs into autonomous hybrid microgrids, employing advanced modelling, optimisation, and control strategies. newlineThe study begins by analysing global trends in EV adoption and their implications for microgrids. EVs are examined as both dynamic loads and distributed storage units that, when effectively coordinated, can enhance the reliability and sustainability of microgrids. The increasing penetration of RESs reinforces the need for optimised energy flow management to support economic and clean electrification. newlineA detailed system modelling framework is developed, incorporating essential microgrid components including photovoltaic (PV) arrays, wind turbines, battery energy storage systems, inverters, and EV charging infrastructure. A comprehensive model of an EV-integrated hybrid microgrid is proposed, and its performance is analysed through power flow and stability studies under various operating conditions. newlineTo improve system efficiency and reduce operational costs, a Self-Adaptive Grey Wolf Optimisation (SA-GWO) algorithm is introduced. This enhanced algorithm adapts control parameters dynamically based on system behaviour, outperforming traditional optimisation methods. Simulation results demonstrate that SA-GWO offers superior performance in steady-state power flow analysis, leading to more efficient energy dispatch and better handling of fluctuating loads and renewable inputs. newlineAn economic and financial assessment of EV charging station integration is conducted, evaluating lifecycle costs, capital investments, operational expenses and battery degradation. The SA-GWO algorithm is show