Optimal scheduling of electric arc furnaces with multiple flexible resources

Under the "dual-carbon" goals, the green transition of the steel industry is crucial. The electric arc furnace (EAF), with its scrap recycling benefits, is key—yet its volatile load challenges grid stability. This study proposes a smart grid coordinated dispatch model incorporating wind, solar photovoltaic, battery storage, diesel generators, and EAF load to optimize both economic and environmental objectives. A multi-energy system model is established, accounting for renewable forecasts, storage dynamics, and load flexibility. A hybrid Particle Swarm Optimization–Differential Evolution (PSO-DE) algorithm is applied to balance cost and emissions under system constraints. Simulations across three scenarios—normal, high load with renewable shortage, and low load with surplus—show that the strategy achieves 90.5% renewable penetration, reduces operating costs by 12.14%, and cuts carbon intensity by 11.37%. This approach offers a viable pathway for industrial load optimization under high renewable integration, supporting both steel industry transformation and power system development.