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考虑微网优先自治的分布式智能配电系统优化调度方法

Optimization Scheduling Method for Distributed Intelligent Distribution Systems with Multiple Microgrids Considering Priority Autonomy
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摘要 随着可再生能源渗透率的不断提高,新型配电系统中存在可再生能源需要就地消纳的问题。为此,基于含多微网的分布式智能配电系统架构,提出一种考虑微网优先自治的分布式智能配电系统优化调度方法,在该系统的运行调度中考虑了微网优先自治的决策偏好。首先微网内部进行优化调度,其次采用虚拟发电机模型和虚拟储能模型对微网所有资源进行资源聚合后上报给配电系统,之后配电系统根据上报的可行域制定调度策略,最后配电系统将调度策略下发给微网进行调度。算例分析表明所提方法可以实现微网优先自治。 As the penetration rate of renewable energy sources continues to increase,a challenge has emerged in new types of distribution systems regarding the local consumption of renewable energy.To address this issue,this study proposes an optimization scheduling method for a smart distribution system with multiple microgrids,incorporating the decisionmaking preference for microgrid autonomy prioritization within its operational scheduling.Initially,an optimization schedule is conducted within the microgrid.Subsequently,a virtual power generator model and virtual storage model are employed to aggregate the resources of the microgrid,which are then reported to the distribution system.Thereafter,the distribution system formulates scheduling strategies based on the reported feasible domains and finally dispatches these strategies to the microgrids for implementation.Through a case study analysis,the proposed method is demonstrated to facilitate prioritized autonomy for microgrids.
作者 张俊潇 高崇 许志恒 李浩 郝鹏 黄淳驿 王承民 谢宁 ZHANG Junxiao;GAO Chong;XU Zhiheng;LI Hao;HAO Peng;HUANG Chunyi;WANG Chengmin;XIE Ning(Grid Planning&Research Center,Guangdong Power Grid Co.,Ltd.,CSG,Guangzhou 510000,China;Key Laboratory of Control of Power Transmission and Transformation,Ministry of Education(Department of Electrical Engineering,Shanghai Jiao Tong University),Shanghai 201100,China)
出处 《电力建设》 CSCD 北大核心 2024年第9期39-48,共10页 Electric Power Construction
基金 中国南方电网有限责任公司科技项目(030000KC23040067,GDKJXM20230379) 国家重点研发计划项目(2020YFB2104500)。
关键词 多微网 优先自治 分布式智能配电系统 聚合 优化调度 multiple microgrids priority autonomy distributed intelligent distribution system aggregation optimized scheduling
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