Spiral Optimization-Based Framework for Minimizing Carbon Emissions and Energy Costs in Electric Vehicle Recharging Stations

This paper proposes a model where the Spiral Optimization Algorithm (SOA) would be used to optimize the system of minimized carbon emissions as well as energy costs of EV recharging stations. Four different charging modes are presented in the proposed framework: peak, off-peak, stochastic, and Electric Power Research Institute (EPRI) charging modes that represent a variety of different ways of consuming energy and different load patterns. The spiral inspired evolutionary algorithm SOA also provides a light weighted optimization mechanism whereby candidate solutions are repeatedly processed along the path of a spiral motion in an attempt to converge into the global optimum solution. Included in the approach is the smart scheduling techniques that could be used to distribute the load, minimize the load on a power grid and facilitate the functioning of a supply and demand balance, especially under the circumstances when the renewable energy sources are highly penetrated. The model was applied in MATLAB, Simulink, and the simulation study has been done testing it and comparing with those traditional optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) respectively.