An intelligent stochastic method based on fuzzy cloud theory for modeling uncertainty effects in the renewable micro-grids

This paper suggests a new stochastic framework based on cloud theory to model the uncertainty effects in the optimal energy management in renewable micro-grids (MGs). Cloud theory is constructed based on the fuzzy theory and probability concept to provide an intelligent model called cloud model (CM). In comparison with the popular Monte Carlo simulation (MCS) method, CM can capture more uncertainty of the problem through the cloud drops. The basic idea is to incorporate the fuzziness and randomness of qualitative concepts and afterward transform them to the quantitative model. According to the high complexity and nonlinearity of the problem, a novel optimization algorithm called modified krill herd (MKH) algorithm is suggested to explore the problem search space globally. The proposed modification method will increase both the search ability and convergence of the algorithm effectively. The feasibility and satisfying performance of the proposed intelligent stochastic framework is demonstrated though a typical MG including different types of renewable energy sources and storage device. Comparative studies reveal the reliable superiority of the proposed stochastic framework to solve the problem.