With the intensification of energy transition and power system complexity, forecasting and assessing the power supply capacity of the grid has become the key to guaranteeing the dynamic balance between supply and demand. In this paper, a computational modeling and assessment framework for power supply capacity integrating multi-scenario analysis is proposed with City C as the research object. A data preprocessing model based on feature iteration is constructed to improve the efficiency of supply chain data de-weighting through density clustering and dynamic iterative optimization. Establish a multi-scenario analysis model for power supply to quantify the impact of different policies and technology paths on the balance of power supply and demand. Combining LEAP model and nonlinear optimization method, we forecast the evolution of power demand and supply structure in City C from 2025 to 2030. The empirical results show that the overall trend is consistently the highest probability density in the M2 range, indicating that the forecast error is concentrated in the [-1%,0.5%) range, and the Markov-corrected electricity consumption of the whole society is projected to be in the range of 940.6 billion to 1243.5 billion kWh. Without the implementation of demand-side management measures, the peak-to-valley difference in electricity load is significant, and the power supply curve after the implementation of demand-side response demonstrates significant structural optimization. To achieve the balance of electricity supply and demand in City C, the synergy and cooperation of both the power supply side and the demand side are required.
