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Deep neural network accelerated-group African vulture optimization algorithm for unit commitment considering uncertain wind power

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APPLIED SOFT COMPUTING 2024年 162卷

作者： Yin, Linfei Ding, Wenyu Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Guangxi Peoples R China Hechi Univ Key Laboratcry AI & Informat Proc Educ Dept Guangxi Zhuang Autonomous Reg Hechi 546300 Guangxi Peoples R China

The unit commitment (UC) of power systems serves to plan out the starting and shutdown of units and the power generation of units for a future period of time. However, the UC problem for large-scale systems faces the problems of long solution time and insufficiently accurate solution. Uncertainty in wind power generation poses a great challenge in solving the unit combination problem. This work proposes a deep neural network accelerated-double layer optimization method (DNNA-DOM) to improve the solution efficiency, accuracy, and economy of UC. The outer layer of DNNA-DOM utilizes the proposed deep neural network accelerated-group African vulture optimization algorithm (DNNA-GAVOA) to optimize the on-off condition of conventional coalfired power units. The inner layer of DNNA-DOM adopts quadratic programming to optimize the solution of the economic dispatch problem of load distribution. The GAVOA optimizes the simulated African vultures in four groups to obtain the optimal solution after diverse exploration, exploitation, and exploitation activities, with low complexity and high accuracy. This work first evaluates the performance of GAVOA by solving seven unimodal functions. Furthermore, the 10-unit simulation by incorporating wind power curves and related constraints is optimized through the DNNA-DOM. The results show that GAVOA outperforms the African vulture optimization algorithm (AVOA) and traditional metaheuristics like the particle swarm algorithm and gray wolf algorithm in terms of lower operating cost and optimization stability. The DNNA-DOM combined with DNNA-GAVOA in the outer layer results in an average daily cost reduction of $36.30 and a 45.1997 % speed increase compared to AVOA in the outer layer.

关键词： double-layer optimization method Group African vulture optimization algorithm Deep neural networks Quadratic programming Wind power Unit commitment

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