Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid

Abstract

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.

Keywords

• Coordination
• dynamic programming
• knapsack
• multi-objective optimization
• Pareto front
• meta-heuristic