Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

被引:9
作者
Youssef, Heba [1 ]
Kamel, Salah [1 ]
Hassan, Mohamed H. [2 ]
Mohamed, Ehab Mahmoud [3 ]
Belbachir, Nasreddine [4 ]
机构
[1] Aswan Univ, Fac Engn, Dept Elect Engn, Aswan 81542, Egypt
[2] Minist Elect & Renewable Energy, Cairo 11517, Egypt
[3] Prince Sattam bin Abdulaziz Univ, Coll Engn Wadi Addawasir, Dept Elect Engn, Wadi Ad Dawasir 11991, Saudi Arabia
[4] Univ Mostaganem, Dept Elect Engn, Mostaganem 27000, Algeria
关键词
Optimization; Costs; Renewable energy sources; Smart homes; Peak to average power ratio; Energy storage; Scheduling; Whale optimization algorithms; Demand side management; Energy management; Storage management; Beluga whale optimization; demand side management; leader beluga whale improvement; mini-grid; renewable energy source; storage system; SYSTEMS;
D O I
10.1109/ACCESS.2024.3367446
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic (PV) panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. Simulation results indicate that in case 2, LBWO and BWO achieved a remarkable reduction in electricity cost by 61% and 51% respectively. In case 3, the reduction was even more significant, with LBWO and BWO lowering the cost by 76% and 64% respectively. Moreover, LBWO generated a revenue of 154 (cents), while BWO generated a revenue of 118 (cents). The results confirm the effectiveness and robustness of the suggested scheme in reducing electricity costs and the PAR (Peak to Average Ratio), while simultaneously increasing profits for electricity consumers.
引用
收藏
页码:28831 / 28852
页数:22
相关论文
共 43 条
[1]  
Agrawal P., 2006, PROC S MICROGRID, P1
[2]   An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources [J].
Ahmad, Adnan ;
Khan, Asif ;
Javaid, Nadeem ;
Hussain, Hafiz Majid ;
Abdul, Wadood ;
Almogren, Ahmad ;
Alamri, Atif ;
Niaz, Iftikhar Azim .
ENERGIES, 2017, 10 (04)
[3]   INFO: An efficient optimization algorithm based on weighted mean of vectors [J].
Ahmadianfar, Iman ;
Heidari, Ali Asghar ;
Noshadian, Saeed ;
Chen, Huiling ;
Gandomi, Amir H. .
EXPERT SYSTEMS WITH APPLICATIONS, 2022, 195
[4]   An improved weighted mean of vectors algorithm for microgrid energy management considering demand response [J].
Alamir, Nehmedo ;
Kamel, Salah ;
Hassan, Mohamed H. ;
Abdelkader, Sobhy M. .
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (28) :20749-20770
[5]   An effective quantum artificial rabbits optimizer for energy management in microgrid considering demand response [J].
Alamir, Nehmedo ;
Kamel, Salah ;
Hassan, Mohamed H. ;
Abdelkader, Sobhy M. .
SOFT COMPUTING, 2023, 27 (21) :15741-15768
[6]   Demand-Side Management in the Smart Grid [J].
Alizadeh, Mahnoosh ;
Li, Xiao ;
Wang, Zhifang ;
Scaglione, Anna ;
Melton, Ronald .
IEEE SIGNAL PROCESSING MAGAZINE, 2012, 29 (05) :55-67
[7]   Genetic-Algorithm-Based Optimization Approach for Energy Management [J].
Arabali, A. ;
Ghofrani, M. ;
Etezadi-Amoli, M. ;
Fadali, M. S. ;
Baghzouz, Y. .
IEEE TRANSACTIONS ON POWER DELIVERY, 2013, 28 (01) :162-170
[8]   Towards Efficient Energy Management and Power Trading in a Residential Area via Integrating a Grid-Connected Microgrid [J].
Aslam, Sheraz ;
Javaid, Nadeem ;
Khan, Farman Ali ;
Alamri, Atif ;
Almogren, Ahmad ;
Abdul, Wadood .
SUSTAINABILITY, 2018, 10 (04)
[9]   Home energy management systems: A review of modelling and complexity [J].
Beaudin, Marc ;
Zareipour, Hamidreza .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 45 :318-335
[10]   Utilizing controlled plug-in electric vehicles to improve hybrid power grid frequency regulation considering high renewable energy penetration [J].
Elkasem, Ahmed H. A. ;
Khamies, Mohamed ;
Hassan, Mohamed H. ;
Nasrat, Loai ;
Kamel, Salah .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2023, 152