Intelligent Residential Energy Management in Smart Grid

Objectives: Providing efficient energy management for smart home through appropriate scheduling of household appliances is addressed in this paper, with two objectives namely, electricity peak demand minimization and electricity cost minimization. Methods/Statistical Analysis: The residential load scheduling problem requires the prior knowledge about the residential electricity demand and electricity price information, for scheduling the appliances. Two different algorithms namely, Discrete Non-dominated Multi-objective Particle Swarm Optimization (DNMPSO) algorithm and Manhattan distance based Non-dominated Multi-objective Genetic Algorithm (MNMGA) are proposed to solve the problem in this paper. Findings: Both the algorithms were simulated in order to evaluate their performance. The Peak-To-Average Ratio value is used as the measure to assessing the peak load in electricity demand. Based on the results, it is observed that the DNMPSO algorithm obtains significant cost reduction with the acceptable Peak-To-Average Ratio value than the MNMGA algorithm. In addition to that, the DNMPSO algorithm provides better diversity than the MNMGA algorithm. Application/Improvements: The proposed algorithm can be used to enhance the energy management for residential load in smart grid. Further, the cost efficiency can be improved by incorporating renewable energy resource.