Peak Shaving Power Construction Path for Renewable Energy Consumption Considering Medium and Long-term Carbon Prices Changes

With the investment of large-scale renewable energy power bases, enhancing the peaking capacity of power systems to ensure long-term economic benefits has become the focus of attention. Compared to energy storage-based peaking, leveraging gas turbines and deeply retrofitted thermal power plants for peaking presents more economic advantages. However, such peak shaving operations, when implemented with modified coal-fired power plants and gas turbines, will inevitably increase carbon dioxide emissions, leading to additional peaking costs under carbon quota policies. To address this, a long-term dynamic change oriented renewable energy power base retrofitting method is proposed in this paper, taking into account the long-term economic benefits of thermal power plant retrofits, gas turbine investments, and energy storage. However, the traditional, crude linearization of carbon emissions models for deeply modulated gas turbines can lead to deviations from economically optimal decisions. Thus, a refined, nonlinear carbon emission model is established in this paper and solved by Particle Swarm Optimization (PSO) algorithm. Case studies based on a real-world power system in Northwest China is employed to validate the effectiveness of the proposed approach.