Optimization Methods for Energy Management in a Microgrid System Considering Wind Uncertainty Data

The management of energy in the microgrid system is usually expressed as an engineering optimization problem. This paper will concentrate on the design of a decentralized power management system for the efficient operation of the microgrid by employing linear and nonlinear optimization methods. Energy management is framed as an optimal power flow dispatch problem. In addition, a technical/economic and environmental study is performed to investigate the impact of microgrid energy exchange with the primary network by running two management scenarios. Meanwhile, the varying characteristics of renewable resources, particularly wind, make the optimal scheduling difficult. To enhance the performance dependability of the energy management system, a wind prediction model based on artificial intelligence of neural networks is applied. The simulation results proved the accuracy of the forecasting model as well as the comparability between the accuracies of the optimization methods to select the most suitable algorithm that provides optimal dispatching of the microgrid generators in the two energy management scenarios proposed making it possible to demonstrate the relevance of the bidirectionality between the microgrid and the main grid.