基于随机预测误差的分布式光伏配网储能系统容量配置方法

储能装置作为一种解决含分布式光伏配网系统功率波动的有效手段,其容量配置关系到补偿功率波动精度和经济性指标。为准确配置储能容量提出一种分布式光伏配网系统中储能设备容量配置方法:通过分析光伏出力短期预测误差和负荷短期预测误差的概率统计规律和随机过程,利用区间估计方法得出储能设备容量配置函数;然后利用该配置函数实现的配置方法,对储能容量在分布式配置和集中式配置两种方法下的预测误差的方差进行比较,可知储能容量分布式配置可以得到更精确的容量配置和更优的功率补偿效果。应用所提配置方法搭建实验样机,并应用于江西电网公司某分布式光伏配网系统中,验证了该方法的正确性和可行性。

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.