A Cost-Effective Optimization for Scheduling of Household Appliances and Energy Resources

In literature, proposed approaches mostly focused on household appliances scheduling for reducing consumers' electricity bills, peak-to-average ratio, electricity usage in peak load hours, and enhancing user comfort level. The scheduling of smart home deployed energy resources recently became a critical issue on demand side due to a higher share of renewable energy sources. In this paper, a new hybrid genetic-based harmony search (HGHS) approach has been proposed for modeling the home energy management system, which contributes to minimizing consumers' electricity bills and electricity usage during peak load hours by scheduling both household appliances and smart home deployed energy resources. We have comparatively evaluated the optimization results obtained from the proposed HGHS and other approaches. The experimental results confirmed the superiority of HGHS over genetic algorithm (GA) and harmony search algorithm (HSA). The proposed HGHS scheduling approach outperformed more efficiently than HSA and GA. The electricity usage cost for completing one-day operation of household appliances was limited to 1305.7 cents, 953.65 cents, and 569.44 cents in the proposed scheduling approach for case I, case II, and case III, respectively and was observed as lower than other approaches. The electricity consumption cost was reduced upto 23.125%, 43.87% and 66.44% in case I, case II, and case III, respectively using proposed scheduling approach as compared to an unscheduled load scenario. Moreover, the electrical peak load was limited to 3.07 kW, 2.9478 kW, and 1.9 kW during the proposed HGHS scheduling approach and was reported as lower than other approaches.