Demand Side Management using DLC in Smart Grid
In smart grid, several optimization techniques are developed for residential load scheduling purpose. Most of these conventional techniques of demand side management aim at minimizing the energy consumption cost. Maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task to achieve. Therefore, in this paper, we focus on minimization of electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyze the performance of a traditional technique; dynamic programming (DP) and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load. Based on these techniques, we propose a hybrid scheme; GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi-dimensional knapsack is used to formulate the energy scheduling problem. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analyzed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with DP, and validate that the proposed model along with the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.