A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION

The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss.