Hummingbird-zonal journey-needle optimization algorithm for harmonized economic dispatch of renewable energy power generation units and thermal power generation units

Amidst the renewable power-generation technologies advance rapidly, multi-source harmonized optimization scheduling in power systems faces the challenge of balancing economic efficiency and environmental sustainability. Addressing issues such as high costs, inadequate control of pollutant emissions, and low algorithm convergence efficiency in traditional scheduling methods during the joint operation of renewable energy and thermal power sources, this study proposes multi-objective power system optimal scheduling model considering ultra-high renewable energy integration rates, along with the Hummingbird-zonal journey-needle optimization algorithm. The model integrates Latin hypercube sampling technique to construct a renewable energy output database. The algorithm innovatively employs a four-stage collaborative optimization mechanism, combining dynamic search radius adjustment with constraint repair strategies to significantly enhance algorithmic convergence accuracy and computational efficiency. This approach ensures simultaneous consideration of exploration and exploitation performance while rapidly identifying global optimal solutions. The Hummingbird-zonal journey-needle optimization algorithm is simulated using 15 benchmark algorithms and 2 case studies involving the harmonized hybrid renewable-thermal generation dispatch, validating the feasibility and reliability of the Hummingbird-zonal journey-needle optimization algorithm. The Hummingbird-zonal journey-needle optimization algorithm reduces fuel costs by 38.7% and pollution costs by 14.8% compared to traditional algorithms.