摘要
电网在线优化调度和直调风电自动发电控制(automatic generation control,AGC)系统策略优化都需要建立准确的风电场可用发电功率估算模型。基于单机信息系统,提出了一种考虑发电工况和站内损耗的风电场可用出力精确估算方法。利用斯皮尔曼相关系数确定了估算时刻的关键历史时刻数量,基于长短期记忆网络建立了风电机组理论发电功率估算模型,并将风机运行细分为待风、发电、停运等6种工况,建立了风电场站内损耗等值电路模型。最后,采用某风场实际数据进行了仿真计算。计算结果表明:单机理论功率模型考虑历史风速时,均方根误差下降了40%;风电场可用发电功率模型考虑发电工况和站内损耗时,均方根误差下降了76.9%。所提出的风电场可用发电功率估算模型,将有助于在线调度和直调风电AGC系统策略的优化,提高风电消纳水平。
Optimization of direct-regulated wind power automatic generation control(AGC)system and grid online optimal dispatch system requires the establishment of an accurate wind farm available power estimation model.Based on the new energy stand-alone information management system,this paper put forward a method of accurate estimation of wind power plant considering power generation conditions and station losses.The Spearman correlation coefficient was used to determine the number of key historical moments in the estimation moment,and the theoretical power estimation model of wind turbines was established based on the long short-term memory(LSTM)network.The operating conditions of the wind turbines were subdivided into six types(named as waiting wind,power generation and outage etc.),and the equivalent circuit model of the loss in the wind farm was established.Finally,the actual data of a wind farm was used to perform simulation calculations.The calculation results show that the root mean square error is reduced by 40%when the theoretical power model of a single machine considers the historical wind speed;When the available power model of wind farm takes into account the power generation condition and in-station loss,the root square error is reduced by 76.9%.The available power estimation model for wind farms proposed in this paper will facilitate the optimization of online dispatching and direct-regulation wind power AGC system strategies,and improve the level of wind power consumption.
作者
杨健
柳玉
黄坤鹏
罗亚洲
牛四清
王伟
环加飞
张雷
张沛
李华伟
YANG Jian;LIU Yu;HUANG Kunpeng;LUO Yazhou;NIU Siqing;WANG Wei;HUAN Jiafei;ZHANG Lei;ZHANG Pei;LI Huawei(North China Branch of State Grid Corporation of China,Xicheng District,Beijing 100053,China;School of Electrical Engineering,Beijing Jiaotong University,Haidian District,Beijing 100089,China)
出处
《发电技术》
CSCD
2023年第2期235-243,共9页
Power Generation Technology
基金
国家电网公司科技项目(SGNCOOOODKJS2000265)。
关键词
风电
斯皮尔曼相关系数
长短期记忆网络
理论发电功率
站内损耗
可用发电功率
wind power
Spearman correlation coefficient
long short-term memory(LSTM)network
theoretical power
station loss
available power