A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles

Abstract The high penetration of renewable energy sources in varied types along with the electric vehicles charging demand has created new challenges against the optimal operation and management of these systems. Therefore, this article attempts to formulate, model and operate the renewable microgrids considering different types of renewable sources including solar units, wind units and storages in the presence of electric vehicles. Due to the volatile and random nature of renewable sources and the charging demands by the electric vehicles, it is needed to find a solution for optimal control of these technologies. In this way, a new policy is devised for control of the charging demand in three different schemes, so-called the smart scheme, coordinated scheme and uncoordinated scheme. In order to capture the influence of uncertainties rooting from the renewable sources in the problem, a novel intelligent approach based on fuzzy cloud theory is used which generates an entropy-entropy concept for its task. Through this model, not only the uncertainty of the random variables is handled, but also the uncertainty of the probability density function (PDF) type is also deployed. In order to solve the problem, a swarm optimization based on honeybee mating algorithm (HMA) is proposed which is equipped by the polar search operators for making a symmetrical searching frame. The simulation results on a typical renewable microgrid test system advocate the quality and appropriate efficacy of the model.