Energy management system for two islanded interconnected micro-grids using advanced evolutionary algorithms

Abstract

An efficient approach to optimal energy management and determine daily optimal operation schedule of two interconnected micro-grids (MGs), isolated from the grid, is proposed. The proposed energy management system consists of day-ahead and hour-ahead scheduling, and economic dispatch during real-time operation. The optimal day-ahead unit commitment can be achieved through two stages, management of the power generated from sources in both MGs and controllable load management. The day-ahead energy scheduling of each unit with technical constraints, being a complex nonlinear problem with several inequality constraints, needs to be optimized to achieve high quality operation as well as minimum daily forecasted energy consumption cost. Meta-heuristic algorithms seem more suitable to handle the task of optimal scheduling compared to the conventional analytic methods for power system economic dispatch. Considering the technical constraints, an advanced state-of-the-art meta-heuristic optimization technique, a modified version of the basic Porcellio Scaber algorithm (PSA) that offers improved efficiency in minimizing the objective function, is applied to solve the optimization problem. Results obtained demonstrate that the developed Global PSA is more efficient than a number of other meta-heuristic techniques to determine the optimal economic dispatch of multi-micro-grids incorporating various types of distributed generators.