Optimization of distributed energy resources for electric vehicle charging and fuel cell vehicle refueling

Abstract A grid-connected set of distributed energy resources that supply power for electric vehicle charging and hydrogen production is investigated through detailed simulation studies. This work uses a genetic algorithm to find the minimum cost for a set of distributed energy resources, the component sizes and energy management strategy are optimized simultaneously. It was found that the optimal component sizes and optimal energy management strategy have a significant influence on each other, and therefore, simultaneous optimization of the two aspects is suggested for such distributed energy resources. The presented approach yields higher time resolution in the simulation compared to previous work. Hence, the model can capture short term changes in dynamic loads and generation, making the simulated energy-management performance more representative of real conditions. The stochastically varying load from electric vehicle charging is modeled based on probabilistic data from existing charging infrastructure. With the present model, the performance of several energy management strategies can be examined. The simulation results show that using the battery storage for peak shaving minimizes the distributed energy resources overall cost while simultaneously decreasing its dependence on the utility grid. Moreover, the results of this study suggest that local energy generation with photovoltaic arrays, in combination with local energy storage and connection to the utility grid, is a viable option.