A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances

Manually operated appliances (MOAs) are manually operated based on users' real-time demands and their energy consumption is uncertain to other schedulable appliances (SAs). This paper represents energy consumption scheduling of home appliances under the uncertainty of the MOAs as a robust optimization problem, as uncertainty distribution of MOAs is usually unknown and not easily estimated. Among all possible energy consumption cases of the MOAs, the robust approach takes into account the worst case to reduce electricity payment of all home appliances, based on the real-time electricity pricing scheme combined with inclining block rate. Intergeneration projection evolutionary algorithm, which is a nested heuristic algorithm with inner genetic algorithm and outer particle swarm optimization algorithm, is adopted to solve the robust optimization problem. Case studies are based on one day case, and one month case with various combinations of SAs and MOAs. Simulation results illustrate the effectiveness of the proposed approach in reduction of electricity payment compared with the approach without considering the uncertainty of MOAs, and the approach considering MOAs with fixed pattern.