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

被引：0

作者：

Jrhilifa, Ismael ^{[1
]}

Ouadi, Hamid ^{[1
]}

Jilbab, Abdelilah ^{[2
]}

Gheouany, Saad ^{[1
]}

Mounir, Nada ^{[1
]}

El Bakali, Saida ^{[1
]}

机构：

[1] Mohammed V Univ Rabat, ERERA, ENSAM Rabat, Rabat, Morocco

[2] Mohammed V Univ Rabat, E2SN, ENSAM Rabat, Rabat, Morocco

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 13期

关键词：

Home Energy Management Systems; Meta-heuristic Algorithm; Load scheduling; Binary Sparrow Search Algorithm; Binary Particle Swarm Optimization; Meta-heuristic; Demand Side Management; ENERGY MANAGEMENT; OPTIMIZATION;

D O I：

10.1016/j.ifacol.2024.07.482

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

引用

页码：194 / 199

页数：6

共 28 条

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