Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (RES) makes real-time energy optimization difficult. As a result, the Energy Management Controller (EMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, EDGE, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (EDE), is devised in this work. The EDGE algorithm is used to train the EMC to automatically react to DR signals and schedule the 3 classes of household loads—hybrid, non-interruptible, and interruptible—to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested EDGE algorithm performs better than benchmark methods.