摘要
在“双碳”背景下,间歇式新能源接入比例不断提高,不仅给电网安全稳定带来挑战,同时也导致各地出现不同程度的弃风弃光。为此,提出一种计及电动汽车的有源配电网两阶段分布式能源消纳策略;在日前阶段考虑配电网相邻区域间电能互济互补能力,兼顾自身利益的同时最大限度地消纳新能源,利用交替方向乘子法(ADMM)进行分布式求解以降低计算压力;在日内阶段考虑预测偏差,基于模型预测控制方法,引导电动汽车及储能系统参与快速响应,通过调整相邻区域联络线功率,实现日内阶段新能源消纳,并跟踪日前阶段调度计划。通过算例分析,所提出的两阶段新能源消纳策略可有效协调区域间的能源分配问题,促进过剩能源的消纳。
Under the background of “double carbon”,the proportion of intermittent new energy access continues to increase,which not only brings challenges to the security and stability of the power grid,but also leads to different degrees of abandonment of wind and solar energy everywhere.To this end,a two-stage distributed energy consumption strategy for active distribution networks taking into account electric vehicles is proposed.In the previous stage,considering the mutual complementarity of electrical energy between adjacent areas of the distribution network,taking into account their own interests while maximizing the consumption of new energy,the alternating direction multiplier method(ADMM) is adopted to carry out distributed solutions to reduce the computational pressure.Considering the prediction deviation in the intraday stage,the model prediction control method is employed to guide the electric vehicle and energy storage system to respond quickly,thus to realize the intraday new energy consumption by adjusting the power of the contact line in the adjacent area,and track the scheduling plan before the day.Through the example analysis,the proposed two-stage new energy consumption strategy can effectively coordinate the energy distribution problem between regions and promote the consumption of excess energy.
作者
李清涛
卢钺
刘洋
刘顺
刁晓虹
张元星
马强
LI Qingtao;LU Yue;LIU Yang;LIU Shun;DIAO Xiaohong;ZHANG Yuanxing;MA Qiang(State Grid Beijing Haidian Electric Power Supply Company,Beijing 100000,China;China Electric Power Research Institute,Beijing 100192,China)
出处
《热力发电》
CAS
CSCD
北大核心
2022年第9期54-62,共9页
Thermal Power Generation
基金
国网北京市电力公司科技项目(52020421009G)。
关键词
新能源消纳
电动汽车
能源分配
ADMM
模型预测控制
new energy consumption
electric vehicle
energy distribution
ADMM
model predictive control