基于水电储能调节的风光水发电联合优化调度策略

扫码查看

原文链接

万方数据
维普

中文摘要：为缓解新能源装机容量扩大引起的弃风弃光现象,在已有梯级水电上下电站之间加入储能泵站,提出风光水储短期优化调度策略.构建以风光水储系统负荷跟踪误差最小、梯级水电站发电量最大和梯级水电站发电耗水量最小的多目标优化调度模型;提出基于季节性自回归移动平均(seasonal auto-regressive lntegrated moving average,SARIMA)模型和Copula函数的风光出力预测模型作为优化调度模型的边界条件,通过SARIMA预测模型将风光出力历史数据分解为季节性分量、趋势分量以及随机噪声余项进行全天96个调度时段风光出力预测,并叠加上基于Copula函数生成风光出力预测误差,然后通过拉丁超立方采样以及K-means聚类进行场景生成和缩减得到5个风光出力场景.选取风光典型日出力数据为例进行算例分析,算例结果表明:所提预测模型较SARIMA模型可以显著提高预测准确度,模型预测风光出力均方根误差从33.34、229.49 MW 分别下降至0.697、9.534 MW;所提优化调度策略可以在全年丰、平、枯水期有效减少弃风弃光现象,并可将过剩新能源中的50%转化为上级水库储存水能.

外文标题：Joint Optimal Scheduling Strategy of Wind,Photovoltaic and Water Storage Power Generation Considering Hydropower Storage Regulation

外文摘要：To alleviate the phenomenon of wind and solar abandonment caused by the expansion of new energy installed capacity,a short-term optimal scheduling strategy for wind and water storage is proposed by adding energy storage pumping stations between the existing upper and lower power stations of cascaded hydropower stations.A multi-objective optimal scheduling model is constructed to minimize the load tracking error of the wind-water storage system,maximize the generation capacity and minimize the water consumption of of the cascaded hydropower.Meanwhile,a wind power prediction model based on the SARIMA and Copula functions is proposed as the boundary condition of the optimal scheduling model,and the historical data of the wind and solar power output is decomposed into the seasonal component,trend component and the random noise residual term for full-day wind and solar output prediction during 96 scheduling periods.The wind power prediction errors are generated by superimposing with the Copula function,and five wind and solar output scenarios are obtained by using Latin hypercube sampling and K-means clustering methods.Typical daily wind power and solar data are selected as examples for case analysis.The results show that the proposed prediction model can significantly improve the prediction accuracy compared with the SARIMA model,and the root-mean-square error of the model decreases from 33.34 MW and 229.49 MW to 0.697 MW and 9.534 MW respectively.Additionally,the proposed optimal scheduling strategy can effectively reduce the phenomenon of wind and solar abandonment in the year-round abundant,flat,and dry seasons and convert 50%of the excessive new energy into water energy stored in the upper reservoir.

外文关键词：

wind photovoltaic output forecastingseasonal auto-regressive integrated moving average(SARIMA)modelCopula functionwind-photovoltaic-water storage systemload tracking

作者：

何奇、张宇、邓玲、王海亮、谢琼瑶、王春、胡家旗

展开 >

作者单位：

国网宜昌供电公司,湖北宜昌 443000

梯级水电站运行与控制湖北省重点实验室(三峡大学),湖北宜昌 443002

关键词：

风光出力预测季节性自回归移动平均模型 Copula函数风光水储系统负荷跟踪

基金：

湖北省重点实验室开放基金国家电网湖北省电力公司科技项目

项目编号：

2022KJX07B715H023001R

出版年：

2024

DOI：

10.3969/j.issn.1007-290X.2024.03.002

广东电力

广东电网公司电力科学研究院，广东省电机工程学会

广东电力

CSTPCD北大核心

影响因子：0.527

ISSN：1007-290X

年,卷(期)：2024.37(3)

参考文献量20