An Efficient Scenario-Based Stochastic Model for Dynamic Operational Scheduling of Community Microgrids with High Penetration Renewables

The supply of electrical energy is being increasingly sourced from renewable generation resources. The variability and uncertainty of renewable generation, compared to a dispatchable plant, is a significant dissimilarity of concern to the traditionally reliable and robust distribution systems. In order to reach the optimal operation of community Microgrids (MGs) including various Distributed Energy Resource (DER), the stochastic nature of renewable generation should be considered in the decisionmaking process. To this end, this paper proposes a stochastic scenario based model for optimal dynamic energy management of MGs with the goal of cost and emission minimization as well as reliability maximization. In the proposed model, the uncertainties of load consumption and also, the available output power of wind and photovoltaic units are modeled by a scenario-based stochastic programming. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. Finally, an improved metaheuristic algorithm based on Cuckoo Optimization Algorithm (COA) is implemented to yield the best global optimal solution. The proposed framework is applied in the typical grid-connected MGs in order to verify its efficiency and feasibility. Keywords—Energy management, COA, Microgrids, Stochastic programming, Reliability, Energy Storage.