Optimal scheduling for charging and discharging electric vehicles

Abstract

With the advancement in automobile and battery technology, Electric Vehicles (EV) have been made widely available commercially. With EV’s main source of energy coming from drawing electricity from the utility grid, it can be predicted that in the future, as the trend of consumers driving EVs increases, the amount of load demand EVs have on the grid increases significantly as well. In this project, a comparison of optimization methods to improve on EV charging scheduling will be done. With the use of MATLAB as the software platform to create mathematical programming models for EVs optimal charging scheduling and Gurobi solver will be used to solve the optimization problem. The comparison will be done on charging EVs without scheduling, a global optimization with centralized control scheduling and a local optimization with sliding window scheduling algorithm. A decentralized charging scheduling algorithm based on game theory with the objective of minimizing system energy cost and in which, also reducing Peak to Average Ratio, will also be discussed. All in all, while a global centralized control charging schedule produces the best solution and helps to reshape the base load of the grid, it is unrealistic to apply the model. Local optimization using local controllers with sliding window scheduling algorithm produce similar solution when compared with centralized control and much better than charging EVs without scheduling scheme. A decentralized charging scheduling with game theory provides an alternative by minimizing energy cost and hence, reducing Peak to Average Ratio of the total load demand, including EV, and optimize local variables turn by turn, in which, produce a global optimal solution while providing consumers incentive for doing so.