电力建设2024，Vol.45Issue(7)：54-67,14.DOI:10.12204/j.issn.1000-7229.2024.07.005
基于tscTFCSNPS路径选择的区域综合能源系统低碳优化方法
Low-Carbon Optimization Method for Regional Integrated Energy Systems Based on tscTFCSNPS Path Selection
摘要
Abstract
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.关键词
区域综合能源系统/脉冲神经膜系统/路径优化/遗传算法Key words
regional integrated energy system/spiking neural P systems/path optimization/genetic algorithm分类
信息技术与安全科学引用本文复制引用
刘佳良,王涛,潘怡..基于tscTFCSNPS路径选择的区域综合能源系统低碳优化方法[J].电力建设,2024,45(7):54-67,14.基金项目
This work is supported by the National Key R&D Program of China(No.2021YFB2601500). 国家重点研发计划资助项目(2021YFB2601500) （No.2021YFB2601500）
