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

EasyChair Preprint 4901

Abstract

Energy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of the microgrid using linear and nonlinear optimization methods. Energy management is defined as an optimal scheduling power flow problem. Furthermore, a technical-economic and environmental study is adopted to illustrate the impact of energy exchange between the microgrid and the main grid by applying two management scenarios. Nevertheless, the fluctuating effect of renewable resources especially wind, makes optimal scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the reliability of the forecasting model as well as the comparison between the accuracy of optimization methods to choose the most appropriate algorithm that ensures optimal scheduling of the microgrid generators in the two proposed energy management scenarios allowing to prove the interest of the bi-directionality between the microgrid and the main grid.

Keyphrases: Artificial Neural Network, Energy Management System, Microgrid, Set-points, Wind Forcasting, optimization algorithms

@booklet{EasyChair:4901,
  author    = {Yahia Amoura and Ana Isabel Pereira and José Lima},
  title     = {Optimization Methods for Energy Management in a Microgrid System Considering Wind Uncertainty Data},
  howpublished = {EasyChair Preprint 4901},
  year      = {EasyChair, 2021}}