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面向多用能需求场景的公路交通自洽微网系统规划方法

Planning method of highway-traffic self-contained microgrid system orientedto multiple energy demand scenarios
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摘要 面向交通用能绿色化、清洁化转型需求,提出了基于交通基础设施蕴含的风、光可再生资源禀赋构建绿色、弹性、自洽和可持续发展的公路交通自洽微网系统规划方法;基于公路交通基础设施空间范围内的可再生能源发电装置、储能装置、用能负荷设备及微网系统,构建了“源-网-荷-储”协同联动的公路交通自洽微网系统架构;针对中国不同地域特点、不同自然资源禀赋以及不同电网支撑条件确定的应用场景,提出了西藏、吉林、浙江3种典型场景下的公路交通自洽微网基础模型,在此基础上,考虑微网建设经济性与微网内可再生能源消纳率等因素,以最小化微网年均综合成本为目标,在各能源设备、储能装置的出力约束及微网内的功率平衡约束下构建系统规划模型,采用粒子群优化算法对微网内各能源设备的配置容量进行优化求解;为验证粒子群算法的全局搜索能力与收敛特性,减小算法求解过程中的随机因素,对3种典型场景的规划方案分别进行了50次独立求解的仿真试验。研究结果表明:利用构建的模型和求解方法,在无大电网支撑场景下公路交通自洽微网系统依靠蓄电池和氢储能协同工作,可实现可再生能源自洽率99.95%的配置效果,弃风弃光率仅为1.34%,表明合理的储能配置可以有效减少风光出力间歇性和波动性带来的问题,实现良好的可调节性,保证微网系统的供电可靠性,可为中国公路交通自洽微网系统规划设计提供决策支持。 To address the demand for green and clean energy consumption in transportation,a method for planning a green,resilient,self-contained,and sustainable highway-traffic self-contained microgrid system was proposed.Based on the endowment of wind and solar renewable resources within transportation infrastructure,initially,the architecture of a highway-traffic self-contained microgrid system was constructed around the renewable energy generation devices,energy storage devices,energy consumption loads,and the microgrid system within the spatial scope of highway transportation infrastructure.The architecture facilitated a synergistic integration of"source-grid-load-storage".Subsequently,based on varying regional characteristics,natural resource endowments,and power grid support conditions in China,the basic models of highway-traffic self-contained microgrid under three typical scenarios of Xizang,Jilin and Zhejiang were proposed.Building upon this,considering the factors such as the economic feasibility of microgrid construction and the renewable energy consumption rate within the microgrid,a system planning model was developed.The goal of the model was to minimize the average annual comprehensive cost of the microgrid,subject to the output constraints of various energy and storage devices and the power balance constraints within the microgrid.Finally,the particle swarm optimization algorithm was employed to optimize the capacity configuration of each energy device within the microgrid.To validate the global search capability and convergence characteristics of the particle swarm algorithm,and to reduce the random factors in the solving process,50 independent simulation experiments were conducted on the planning schemes for three typical scenarios.Research results indicate that the proposed model and solution method enable a highway-traffic self-contained microgrid system without major grid support to achieve a renewable energy self-contained rate of 99.95%through the coordinated operation of batteries and hydrogen energy storage,while the abandonment rate of wind and solar energy is only 1.34%.It demonstrates that a appropriate storage configuration can effectively mitigate the intermittency and variability of wind and solar outputs,ensure adjustability and reliability of the microgrid's power supply,and provide decision supports for the planning and design of China's highway-traffic self-contained microgrid system.5 tabs,6 figs,31 refs.
作者 师瑞峰 唐可意 高毓钦 贾利民 SHI Rui-feng;TANG Ke-yi;GAO Yu-qin;JIA Li-min(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;China Institute of Energy and Transportation Integrated Development,North China Electric Power University,Beijing 102206,China;Power China Chengdu Engineering Corporation Limited,Chengdu 610000,Sichuan,China;State Key Laboratory of Advanced Power Transmission Technology,State Grid Smart Grid Research Institute Co.,Ltd.,Beijing 102209,China;State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China)
出处 《交通运输工程学报》 EI CSCD 北大核心 2024年第4期31-42,共12页 Journal of Traffic and Transportation Engineering
基金 国家重点研发计划(2021YFB2601300)。
关键词 交通与能源融合 公路交通自洽微网系统 粒子群优化 用能需求 多场景分析 氢储能 integration of transportation and energy highway-traffic self-contained microgrid system particle swarm optimization energy demand multiple scenario hydrogen energy storage
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