An Optimized Planning Model for Management of Distributed Microgrid Systems

Abstract

As energy consumption rises, so do greenhouse gas emissions which result in harming our environment. As a result of the integration of renewable energy sources into power networks monitoring and managing the increasing load, demand is crucial. Microgrids have been identified as a possible structure for integrating the rapidly expanding distributed power generating paradigm. Various academics already have offered a significant number of strategies to fulfill the rising load requirement. The major disadvantage of these models was the scheduling of devices and was power generation capabilities which make traditional schemes complex and time-consuming. In order to overcome these problems, this paper proposed an effective approach for three power generating systems; these are PV systems, wind systems, and fuel cell systems. The Grey Wolf Optimization (GWO) algorithm optimizes the scheduling process by analyzing and selecting the optimal fitness value for various device combinations. The fitness value that is closest to the load requirement is picked as the best. In terms of power generating capacities, the performance of the suggested GWO model is determined and compared to the standard model in MATLAB simulation system. The simulated experiments demonstrated that the proposed approach is much more productive and efficient to meet cost and load demands with reduced time processing and complexity.