Research on Low-Carbon Optimization Model of Power System Driven by Carbon Capture and Ladder Electricity Price

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The power system faces huge challenges in reducing carbon emissions and improving economic benefits. The traditional electricity price collaborative optimization model cannot fully combine the synergy of carbon capture technology and the tiered electricity price mechanism. There is an urgent need to propose a new low-carbon optimization model to cope with energy transformation. And sustainable development goals requirements. The power system structure is mapped through a multi-energy coupled digital twin system to achieve dynamic perception and modelling of power generation, load, and carbon emission processes. Construct a response mechanism driven by carbon-electricity collaboration, combine multi-modal data fusion technology, and use the LSTM-CNN deep neural network to mine the collaborative rules among carbon capture devices, electricity markets, and user behaviour. In terms of optimization algorithms, a dual-objective reinforcement learning model based on a deep Q network (DQN) is proposed to find a dynamic balance between economy and low carbon and integrate mixed integer programming methods to deal with complex system constraints to improve solution efficiency and feasibility. In the synergy model of carbon capture and tiered electricity price, when the carbon capture efficiency reaches 45.67%, the carbon emission intensity of high-carbon units drops from 91.12 g/kWh to 62.34 g/kWh, a decrease of 31.5%. When the coverage rate of the secondtiered electricity price is 23.89%, the peak load of industrial users is reduced by 17.56%, and the peak load is increased by 100% × 34.23% (relative to the benchmark). The collaborative strategy enabled the system’s comprehensive carbon emission reduction rate to reach 34.23%, which was 23.1% higher than that of single carbon capture, verifying the coupling and efficiency of price signals and technical means.

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