摘要
能源互联背景下,以储能、需求响应、分布式发电(DG)等为代表的多元融合资源将深度参与电网运行。配电网作为局部地区多类分布式资源的整合者,需要通过有效的手段,对包括DG、储能、可控负荷等多元融合的分布式资源进行优化调控,提高配电网的弹性。该文针对多元分布式资源接入后,集中式调度策略下的配电网将面临的各类问题,结合多元分布式资源的特性,建立了面向高弹性配电网的两层分布式协调调度模型。该文首先对高弹性配电网下的多元融合分布式资源进行协调整合,之后通过协调全局控制中心和本地控制中心,对上述资源进行合理调度,以保证在一定的电压质量要求下消纳更多的DG发电量,以提高电力中新能源的利用水平。文中算例采用IEEE14节点和69节点的配电网系统,对比传统集中式模型和该文提出的双层分布式模型的优化调度结果,验证了所提模型在配电网系统下的适用性及优越性。
With the development of energy interconnection system,diversified resources represented by energy storage,demand response and distributed generation(DG)are deeply involved in power grid operation.As an integrator of multiple distributed resources,distribution system needs to optimize and regulate distributed resources to improve the elasticity of distribution system.Centralized optimization strategy for the traditional distribution network has several limitations in the case of multiresource fusion.This paper analyzes the characteristics of multiple distributed resources firstly,and then establishes a two-layer distributed optimization model for highly elastic distributed network which regulates several kinds of distributed resources through coordinating global controller and local controllers in order to ensure more DG consumption under the certain voltage quality,and to improve the utilization level of renewable energy.IEEE 14 nodes and IEEE 69 nodes distribution network systems are used for case study.The results of the two optimization strategies are compared.The applicability and advantages of the two-layer distributed scheduling model are verified.
作者
倪萌
王蓓蓓
朱红
刘少君
Ni Meng;Wang Beibei;Zhu Hong;Liu Shaojun(School of Electrical Engineering Southeast University,Nanjing 210096 China;Nanjing Power Supply Company State Grid Jiangsu Electric Power Co.Ltd,Nanjing 210019 China)
出处
《电工技术学报》
EI
CSCD
北大核心
2022年第1期208-219,共12页
Transactions of China Electrotechnical Society
基金
国家自然科学基金项目(71471036)
国网公司科技项目(泛在物联背景下主动配电网电压控制深度强化学习策略研究)资助。
关键词
多元融合分布式资源
高弹性配电网
双层分布式调度
电压质量
Multiple distributed resources
highly elastic distributed network
two-layer distributed optimization model
voltage quality