Consistent African vulture optimization algorithm for electrical energy exchange in commercial buildings

作     者：Yin, Linfei Tian, Jing Chen, Xiaofang 

作者机构：Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Guangxi Peoples R China Guangxi Univ Sch Publ Policy & Management Nanning 530004 Guangxi Peoples R China 

出 版 物：《ENERGY》 (Energy)

年 卷 期：2025年第318卷

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Guangxi Province (China) [AA22068071] 

主　　题：Optimization problems Electrical energy exchange in commercial buildings African vulture optimization algorithm Consistent optimization algorithm Regional commercial buildings 

摘      要：As the electricity market evolves and the penetration rate of distributed controllable resources in commercial buildings increases, the potential for commercial building electricity trading emerges. However, in regional commercial building electricity trading, problems such as little study and no information sharing exist. Deficiencies exist in the existing algorithms, such as the computational complexity of the African vulture optimization algorithm (AVOA), and the unreliability of the consistent optimization algorithm in optimization search. For this reason, this study proposes a consistent AVOA, which incorporates consistent optimization algorithm based on the AVOA. The proposed consistent AVOA can improve the accuracy of optimization, precisely optimize the electrical energy exchange of commercial buildings, and enhance the utilization efficiency of distributed power generation equipment. Variable load, variable photovoltaic power, variable ambient temperature, and extreme cases verify the feasibility and reliability of the consistent AVOA through simulation. Compared to the traditional algorithms multi-verse optimizer and sine cosine algorithm, the proposed consistent African vulture optimization algorithm has a higher average value of revenues for the power selling companies. The proposed algorithm offers favorable stability. The convergence time for the consistent African vulture optimization algorithm is 20.8 % shorter compared to both the comparison algorithms. Additionally, the proposed algorithm reduces the cost of running commercial buildings by 12.9 % and 7.2 %, respectively.