A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.

Automated summary

This study investigates the impact of household appliance energy management on electricity providers in terms of electricity demand and generation. By using the Improved Cockroach Swarm Optimization Algorithm (ICSOA) and the Innovative Apartments Appliance Scheduling (IAAS) framework, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction for apartment appliances are analyzed. The results show that the proposed ICSA algorithm performs better than the BFOA and W/O scheduling cases, providing advantages to both utilities and consumers.