Balancing Demand and Supply of Energy for Smart Homes

Abstract

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumer’s electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search (HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of minimum cost, reduced peak to average ratio (PAR), waiting time and equally distributed energy consumption pattern in each hour of a day to benefit both utility and end users.