Day-ahead optimal scheduling of microgrid with considering demand side management under uncertainty

In this paper, a new model for day-ahead optimal scheduling of microgrid in the presence of energy storage system, distributed generation resources and demand side management is proposed. The proposed model consists of two layers: the clustering layer and the planning layer. In the clustering layer, the customers of microgrid are clustered based on their consumption by C-Means algorithm. In the planning layer, the objective functions minimize the operation cost and emission pollution using column and constraint generation (C&CG) optimization algorithm. Also, the electrical demand profile is flattened by optimal shifting of electrical loads. Then, the best solution available among the non-dominated Pareto solutions is selected by the fuzzy decision-making method. Moreover, the uncertain behavior of renewable energy sources output, the stochastic modelling electrical loads and electrical energy price are modelled by Monte Carlo technique. Finally, a case study is analyzed to confirm the proposed approach. Numerical results and comparison with epsilon-constraint and particle swarm optimization approaches show the proposed model capability in scheduling a uniform microgrid operation.