Abstract:

Due to the increasingly prominent non-renewability and polluting nature of traditional fossil fuels，the research and application of new types of clean energy have become more extensive and in-depth，with wind power generation accounting for a growing share in power systems. However，the intermittency and randomness of wind energy increase the difficulty of solving the economic scheduling problem in power systems with wind farms. To address this complex issue，an economic scheduling model for power systems with wind farms was established，considering both economic and environmental costs. The model aimed to improve both the economic efficiency and environmental sustainability of power grid scheduling，incorporating system load-power balance and unit output constraints as key constraints. Moreover，a multi-objective improved African vulture optimization algorithm was proposed，which integrated Tent chaos mapping and adaptive weight strategies to effectively tackle complex scheduling problems. Simulation experiments were conducted on the modified IEEE 30 system under different objective functions and operation statuses. Taking the low wind power penetration and low load scenario as an example，the compromise solution scores using the multi-objective improved African vulture optimization algorithm improved by 59.105 6%，88.451 8%，37.349 2%，10.147 7%，and 12.700 3% compared to the benchmark algorithms multi-objective particle swarm optimization，non-dominated sorting genetic algorithm，multi-objective grey wolf optimization，multi-objective atomic orbital search，and multi-objective African vulture optimization algorithm，respectively. the simulation results confirmed the feasibility and applicability of the proposed economic scheduling model and multi-objective improved Arican vulture optimization algorithm in real-world power systems.

Key words: wind power generation, multi-objective optimization, economic scheduling, African vulture optimization algorithm