基于tscTFCSNPS路径选择的区域综合能源系统低碳优化方法OA北大核心CSTPCD

Determining the optimal energy supply path in the practical application of a regional integrated energy system(RIES)is a complex problem.As a solution,this study proposes an energy supply path optimization inference model based on a RIES-tagged fuzzy colored spiking neural P system with a time sequence constraint(RIES-tscTFCSNPS),which is used to study the advantages and disadvantages of different energy supply paths under the same conditions.First,given that the load demands are satisfied,the RIES-tscTFCSNPS-based model was used to select all energy supply paths that satisfy the constraints.The two objective functions of running cost and CO2 emissions were then weighted and combined to establish a new objective function,which was optimized using a genetic algorithm.Finally,the optimal energy supply scheme for different scenarios was obtained by analyzing the optimization results.For an example analysis,we set four different scenarios based on a comprehensive park.The simulation results showed that the proposed model can effectively improve the system economy and reduce CO2 emissions.