A novel stochastic framework based on fuzzy cloud theory for modeling uncertainty in the micro-grids

• Introducing a new artificial optimization algorithm based on KH evolutionary algorithm.
• Introducing a new stochastic framework based on cloud theory to model the uncertainties of the problem.
• Proposing a new modification method for krill herd algorithm to improve its search ability.

This article proposes a novel stochastic framework based on cloud theory to handle the uncertainty effects in the optimal operation of microgrids. In respect of the Monte Carlo simulation (MCS) method, cloud theory can contain more uncertainty of the problem using the cloud drops. The main concept is to include the fuzziness and randomness of qualitative parameters and then change them to the quantitative form. Due to the high difficulty and nonlinearity of the problem, a new optimization algorithm based on krill herd (KH) is devised to search the problem space globally. Also a new modification method based on Levy flight is proposed to increase the local search ability of the algorithm. In order to see the high performance and ability of the proposed method, a typical grid connected microgrid with several dispatchable and non-dispatchable units are considered as the case study.