基于动态规划的江苏省电源结构优化研究OA

Jiangsu Province is a major economic,energy-consuming,and carbon-emitting region in eastern China.To optimize its power generation structure and promote net-zero carbon emissions in the power sector,this study first applies a multiple linear regression model to verify the correlations between electricity demand and factors such as GDP and population.An artificial neural network is then employed to forecast Jiangsu's electricity demand from 2025 to 2050.Based on the demand projections,a carbon budget is allocated to the power sector,and a dynamic programming model is developed with the objective of minimizing total system costs.The model incorporates carbon capture and storage(CCS)constraints to ensure net-zero emissions.Three scenarios of no-carbon budget,loose carbon budget and strict carbon budget are set up to analyze the optimal power structure and the best path to carbon neutrality under different scenarios.Results indicate that although the cumulative emissions under the relaxed carbon budget scenario are higher than those in the stringent scenario,it achieves the lowest overall cost and a more feasible emission trajectory.It is a more suitable decarbonization strategy for Jiangsu's economic development and resource allocation.