Improved genetic algorithm for optimal demand response in smart grid

In Smart Grid system, the consumers are provided with the opportunity to schedule their home appliances in response to variations in electricity price over time. This paper presents the optimal scheduling of resident ap-pliances. This optimal scheduling is formulated as an optimization problem and is solved by applying improved Genetic Algorithm (GA). In this improved GA, the selection of chromosomes is carried out using entropy method, blended crossover and mutation is performed using correlation coefficient. The simulation is performed for a single resident load profile for a set of appliances using Python programming. The result shows the reduction in the electricity cost to that of the original. The number of iterations taken by the improved Genetic Algorithm (GA) is comparatively lesser than the standard GA and the execution time is reduced by 2.76 s. Thus the result proves the effectiveness of the proposed improvisation in Genetic Algorithm for optimal load scheduling.