摘要
在电力系统的“双高”背景下,针对配电网的电压支撑需求与经济运行需求,提出一种构网型储能的多目标双层优化模型。在上层选址定容模型中,以构网型储能的位置和容量为决策变量,考虑构网型储能的运行成本、向上级电网的购电成本、网损成本及碳排放成本,提高经济性;在下层优化运行模型中,以构网型储能的充放电状态为决策变量,考虑最小电压稳定指数、电压偏移、系统网损,提升配电网电压稳定性;利用电压稳定指数识别系统中电压稳定性薄弱的节点作为预选址节点,提高求解效率。最后,通过修改的IEEE33节点配电网验证该模型的有效性。结果表明:对构网型储能进行优化配置,可以提高配电网电压稳定性,降低运行成本。
Under the background of"double high"in power system,a multi-objective double-layer optimization model of grid forming(GFM)energy storage is proposed to meet the voltage support demand and economic operation demand of a distribution network.In the upper level site selection and capacity model,the location and capacity of GFM energy storage are used as decision variables,and the operating cost of GFM energy storage,the electricity purchasing cost from the superior power grid,the cost of grid loss and the cost of carbon emissions are considered,so as to improve economic efficiency.In the lower level optimization operation model,the charging and discharging states of GFM energy storage is taken as the decision variable,and the minimum voltage stability index,voltage offset,and system network loss are considered,so as to improve the voltage stability of the distribution network.The voltage stability index is used to identify nodes with weak voltage stability in the system as pre-selection nodes to improve solution efficiency.The effectiveness of the model is verified by the modified IEEE33 node distribution network.The results show that the optimization configuration of GFM energy storage can improve the voltage stability of the distribution network and reduce operating cost.
作者
李政凯
孙谊媊
杨桂兴
亢朋朋
赵洪峰
印欣
樊国伟
LI Zhengkai;SUN Yiqian;YANG Guixing;KANG Pengpeng;ZHAO Hongfeng;YIN Xin;FAN Guowei(College of Electric Engineering,Xinjiang University,Urumqi 830047,China;State Grid Xinjang Electric Power Co.,Ltd.,Urumqi 830001,China)
出处
《现代电子技术》
2023年第14期120-126,共7页
Modern Electronics Technique
基金
新疆高校科研计划项目(XJEDU2021I010)。
关键词
构网型储能
分层优化
灰狼算法
配电网
决策变量
优化运行
预选址
GFM energy storage
hierarchical optimization
grey wolf optimizer
distribution network
decision variable
optimizing operation
site pre-selection