Integrating AI and Multi-objective Optimization for Enhanced Microgrid Energy Management Using Quadratic Programming

Abstract

Using the AI approaches and the quadratic programming driven multiobjective optimisation, this paper proposes a universal framework for smart microgrid energy management. It considers its operational preconditions such as the loads demand and the renewable power source availability in order to reduce the microgrid operational cost and emission. An artificial neural network constructed models the predicted loads requirement, one-hours wind power output and solar generation for 24 hours. The simulated machine learning possesses good generalisation capacity and an excellent learning structure. Managing batteries or auxiliary devices in order to maximise microgrid operating efficiency runs counter to the traditional optimisation laws. This study used fuzzy logic advisor network to schedule the battery. Both the fuzzy environment of the microgrid operation as a whole and the uncertainty among the predicted parameters are manageable in the suggested solution. In comparison with microgrid energy management methods acquired from the literature, namely opportunity recharging and inductive schematic alternating battery management, the experimental results yield significant operational cost and emission reduction.