Low Cost and Reliable Energy Management in Smart Residential Homes Using the GA Based Constrained Optimization

Recently smart grids have given chance to residential customers to schedule operation times of smart home appliances to reduce electricity bills and the peak-to-average ratio through the demand side management. This is apparently a multi-objective combinatorial optimization problem including the constraints and consumer preferences that can be solved for optimized operation times under reasonable conditions. Although there are a limited number of techniques used to achieve this goal, it seems that the binary-coded genetic algorithm (BCGA) is the most suitable approach to do so due to on/off controls of smart home appliances. This paper proposes a BCGA method to solve the above-mentioned problem by developing a new crossover algorithm and the simulation results show that daily energy cost and peak to average ratio can be managed to reduce to acceptable levels by contributing significantly to residential customers and utility companies