Cost and load reduction using heuristic algorithms in smart grid

Due to smart grid applications the consumers and producers are able to meet the demand of each others and thus take part in demand side management and demand response program. Hence smart grid leads to optimization of energy consumption and reduce high cost in today extensive demand of energy. In this research work we are reducing electricity consumption cost and load consumption using scheduling the appliances. The twenty appliances are used to schedule their energy consumption and load using heuristics techniques i.e. binary particle optimization, genetic algorithm and wind driven optimization, using the same data set for each technique and their results are compared with each other in order to find which technique do better optimization. Simulations are performed in matlab to show the cost and load reduction by the above three techniques and validate the experiment. The simulation results show that binary particle swarm optimization perform better than the other two techniques and wind driven optimization is better than genetic algorithm but not able to perform as binary particle swarm optimization, similarly genetic algorithm is least efficient as compared to both methods. Our research work is beneficial to meet the demand side management and help in reducing electricity cost and load for consumers.