Jrhilifa, ismael (ERERA, National High School of Arts and Crafts, Mohammed V Unive), Ouadi, Hamid (Ismra), Jilbab, Abdelilah (Mohammed V University in Rabat), Mounir, Nada (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), GHEOUANY, Saad (ERERA, National School of Arts and Crafts, Mohammed V University), EL BAKALI, Saida (ERERA, ENSAM, Mohammed V University, Rabat, Morocco)

Residential Appliances Scheduling Using Binary Sparrow Search Algorithm for Demand Side Management

Scheduled for presentation during the Regular Session "Optimal energy scheduling for residential buildings" (ThuS1T3), Thursday, July 11, 2024, 10:40−11:00, Session room 3

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

The rise in electricity consumption in conventional buildings has resulted in increased electricity rates in numerous countries. Additionally, inefficient energy management leads to higher carbon emissions. In contrast, modern smart buildings integrate advanced energy management systems. This paper presents a multi-objective load-shifting system that aims to minimize electricity costs, load peaks, and waiting time when scheduling daily appliance usage using Binary Sparrow Search Algorithm (BSAA). A comparison of BSSA and Binary Particle Swarm Optimization (BPSO) methodologies shows that SSA is superior in achieving cost reduction (27.6%) and Peak-to-Average-Ratio (PAR) minimization (40.32%) compared to BPSO, which achieved a cost reduction of 13.42% and a PAR minimization of 36.05%. However, the mean waiting times of the two approaches are similar.