In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too. (C) 2015 Elsevier Ltd. All rights reserved.Postprint (author's final draft

Both performance optimization and scheduling of the distributed generation (DG) are relevant implementing an energy management system (EMS) within Microgrid (MG). Furthermore, optimization methods need to be applied to achieve maximum efficiency, improve economic dispatch as well as acquiring the best performance. This paper proposes an optimization method based on gravitational search algorithm to solve such problem in a MG including different types of DG units with particular attention to the technical constraints. This algorithm includes the implementation of some variation in load consumption model considering accessibility to the energy storage (ES) and demand response (DR). The proposed method is validated experimentally. Obtained results show the improved performance of the proposed algorithm in the isolated MG, in comparison with conventional EMS. Moreover, this algorithm which is feasible from computational viewpoint, has many advantages as peak consumption reduction, electricity generation cost minimization among other. (C) 2014 Elsevier Ltd. All rights reserved.Peer ReviewedPostprint (author’s final draft