Multi-Stage Energy Storage Configuration Method Considering the Evolution Path of New Energy Penetration

Under the impetus of the “dual-carbon” goals, the rapid increase in new energy penetration has imposed higher demands on the flexibility and economy of power systems. Traditional static planning methods struggle to adapt to the dynamic evolution of new energy and the iterative technoeconomics of energy storage. To address this, this paper proposes a multi-stage collaborative planning method for wind-thermalstorage hybrid systems. First, based on the logistic growth model and learning curve, the evolution of new energy penetration under different policy scenarios is quantified. Second, a mixedinteger linear programming model is established to optimize the economy by coordinating the intertemporal coupling of energy storage lifecycle costs, thermal power retrofitting costs, and peakvalley arbitrage revenue. Simulation results from a case study in a large-scale new energy base in Northwest China demonstrate that the proposed multi-stage planning reduces typical daily total costs by 37.89% compared to static planning, while thermal coal costs decrease by 1.16%. This research provides an economically viable decision-making framework for energy storage temporal planning and thermal power transition in high-penetration renewable energy systems.