An improved binary African vultures optimization approach to solve the UC problem for power systems

Unit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a recent metaheuristic algorithm, the African Vultures Optimization Algorithm (AVOA), which is a nature-inspired metaheuristic algorithm that mimics the hunting mechanism and behavior of African vultures. This algorithm has the advantage of creating various phase shift approaches to avoid premature convergence and local optimum trapping. AVOA uses two mechanisms for the exploration phase and four for the exploitation phase that ensure the algorithm's ability to diversify and intensify. The AVOA is a continuous algorithm that cannot tackle the mixed-integer nature of the UC. In this paper, a sigmoid transfer function is used to convert the algorithm into a binary algorithm to decide the on/off status of the generating units. To further enhance the performance of the proposed algorithm, new values of the controlling parameters are proposed. Finally, the performance of the proposed algorithm is tested on IEEE 30-, 14-, and 57-bus systems. The proposed Binary AVOA (BAVOA) has superiority over the other algorithms given in the case study. In addition, results revealed that the BAVOA gives better results in the discrete search space (DSS) compared to the continuous search space (CSS). For instance, the operation cost of an IEEE 30-bus system in the DSS is 12,768 dollars which is 7% less than the CSS.

Automated summary

This paper proposes a binary version of the African Vultures Optimization Algorithm (AVOA) to solve the unit commitment problem in electrical power systems. The algorithm mimics the hunting mechanism and behavior of African vultures and overcomes premature convergence and local optimum trapping. New controlling parameter values are introduced to enhance the algorithm's performance. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the case study for IEEE 30, 14, and 57-bus systems.