摘要
不同典型天气下光伏出力预测误差具有明显差异,为保证分布式光伏集群友好接入,提出一种考虑光伏发电功率预测误差不确定性的共享储能容量配置方法。首先基于注意力机制和长短时记忆神经网络(long-term short-term memory neural network,LSTM)对分布式光伏出力进行预测,再与不同典型气象条件下的光伏出力实际值进行对比得到预测误差。然后以共享储能配置成本最优为目标,建立跟踪光伏出力计划偏差的共享储能容量配置模型,通过引入鲁棒机会规划约束来描述预测误差的不确定性,并采用凸逼近方法将原模型转化为确定性模型进行求解。仿真结果表明,所提方法在保证补偿效果的同时能最大提升储能配置的经济性。
To ensure the reliable integration of distributed photovoltaic clusters,this study proposes a shared energy storage capacity allocation method that takes into account the uncertainty of photovoltaic power prediction errors.First,the distributed photovoltaic output is predicted based on the attention mechanism and the long-term short-term memory neural network(LSTM),and then the prediction error is obtained by comparing it with the actual value of photovoltaic output under different typical meteorological conditions.Second,with the optimal cost as the goal,a shared energy storage capacity allocation model is established to track the deviation of photovoltaic output plan,and the uncertainty of prediction error is described by introducing robust opportunity planning constraints,and the convex approximation method is used to transform the original model into a deterministic model for solving.Finally,the simulation results show that the proposed method can maximize the economy of energy storage configuration while ensuring the compensation effect.
作者
钟士元
陈俊志
张华
王梦宇
朱自伟
夏鹞轩
宋冠宏
ZHONG Shiyuan;CHEN Junzhi;ZHANG Hua;WANG Mengyu;ZHU Ziwei;XIA Yaoxuan;SONG Guanhong(Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330200,Jiangxi,China;School of Information Engineering,Nanchang University,Nanchang 330031,Jiangxi,China)
出处
《电网与清洁能源》
CSCD
北大核心
2024年第6期30-38,47,共10页
Power System and Clean Energy
基金
江西省自然科学基金项目(20232BAB212021)
国网江西省电力有限公司重点研究科技项目(521825220001)。
关键词
分布式光伏
功率预测
神经网络
跟踪计划出力
储能配置
distributed photovoltaic
power forecasting
neural networks
tracking planned contributions
energy storage configuration
