Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid

被引:0
|
作者
Alghamdi, Hisham [1 ]
Hua, Lyu-Guang [2 ]
Hafeez, Ghulam [3 ]
Murawwat, Sadia [4 ]
Bouazzi, Imen [5 ,6 ]
Alghamdi, Baheej [7 ,8 ]
机构
[1] Najran Univ, Coll Engn, Dept Elect Engn, Najran, Saudi Arabia
[2] Power China Hua Dong Engn Corp Ltd, Hangzhou, Peoples R China
[3] Univ Engn & Technol, Dept Elect Engn, Mardan, Pakistan
[4] Lahore Coll Women Univ, Dept Elect Engn, Lahore, Pakistan
[5] King Khalid Univ, Coll Engn, Dept Ind Engn, Abha, Saudi Arabia
[6] King Khalid Univ, Ctr Engn & Technol Innovat, Abha, Saudi Arabia
[7] King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Smart Grids Res Grp, Jeddah, Saudi Arabia
[8] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah, Saudi Arabia
来源
PLOS ONE | 2024年 / 19卷 / 11期
关键词
SIDE MANAGEMENT; MODEL; BUILDINGS;
D O I
10.1371/journal.pone.0307228
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.
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页数:28
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