基于信息共享的光伏清洁能源输出功率预测方法

A Forecasting Method of Photovoltaic Clean Energy Output Power Based on Information Sharing

与传统能源不同,光伏发电-储电-用电的历史信息数量多、约束条件复杂度较高,且缺少必要的数据共享过程,导致光伏清洁能源输出功率预测的准确率较差。在光伏清洁能源输出功率预测方法中引入信息共享方法,利用信息集成模块集成生产管理系统、电网规划系统、主管信息系统、调度系统、历史生产数据、市场交易等光伏清洁能源数据,设计一种信息共享方法,降低光伏清洁能源数据的差异化程度。通过PSOEM优化BP神经网络算法,预测光伏清洁能源输出功率,引入带扩展记忆的粒子群算法来改善算法陷入局部最优值的缺陷,提升光伏清洁能源信息预测精度以及收敛速度,构建光伏清洁能源输出功率预测模型。模型测试结果表明,该方法能够准确实现光伏发电信息、储电信息以及用电信息的实时共享,预测光伏电力系统输出功率的HM准确率达96.3%,RMSE准确率达93%。

Unlike traditional energy sources,the large amount of history information of photovoltaic power generation-storageuse,the high complexity of constraints,and the lack of necessary data sharing process often lead to a poor accuracy of power prediction of PV clean energy output.To this end,the information sharing method is introduced into the PV prediction method.The information integration module is used to integrate PV clean energy data from production management system,grid planning system,supervisor information system,dispatching system,historical production data,market transactions,etc.An information sharing method is designed to reduce the degree of differentiation of PV clean energy data.The BP neural network algorithm is optimized by PSOEM to predict the PV clean energy output power,and the particle swarm algorithm with extended memory is introduced to improve the defects of the algorithm falling into local optima,and enhance the accuracy of PV clean energy information prediction and convergence speed,thus build a PV clean energy output power prediction model.The model test results show that the method can accurately realize the real-time sharing of photovoltaic power generation information,power storage information and power consumption information,and the HM accuracy of predicting the output power of PV power system reaches 96.3%,and the RMSE accuracy reaches 93%.