A two-stage optimization operation method for low-carbon demand response in molten salt manufacturing process considering dynamic carbon potential and price signals of grid electricity

To increase the proportion of renewable energy in power grid and enhance its stability and flexibility, high electricity consumers should actively participate in demand response. Molten salt manufacturing enterprises are typical high electricity consumers. The molten salt manufacturing process has numerous production stages and equipment. Carbon emissions of this process are not only related to the energy efficiency of all equipment but also determined by dynamic carbon potential of external electricity and steam. In this paper, we establish a carbon-energy flow model for the molten salt manufacturing process based on State Task Network, and propose a two-stage optimization method that can simultaneously achieve energy reservation and carbon reduction while improving low-carbon demand response capability. In the first stage, an intelligent optimization algorithm is used to determine the optimal operating rates of all production equipment. In the second stage, the production scheduling time is added as a new optimization variable, and solved by forming a low-carbon demand response optimization problem considering both the dynamic carbon potential and price signals of grid electricity. By simulating short-term orders with a production cycle of approximately 4.8 h and daily orders operating on a 24-h cycle, the results indicate that the proposed method achieves reductions in electricity costs and carbon emissions by 29.73 % and 0.64 % for short-term orders, and 15.85 % and 2.26 % for daily orders.