基于最大信息系数的用户光伏出力分解技术

近年来,分布式光伏系统(Distribution Photovoltaic Systems, DPVSs)由于相关政策的支持、低安装成本和易操作性,得到了越来越多用户的青睐。然而大量小于10 kWp的小规模DPVSs是安装于表后侧(Behind-the-Meter, BTM)的,光伏发电信息并未被单独计量,这导致了光伏发电对表前侧(配电网侧)的不可见性。若配网侧仅以净负荷数据进行能量管理,将无法进行最优的配电网调度和需求响应策略的实施,导致一系列的安全经济问题。为解决上述问题,本文提出了一种针对小规模DPVS的BTM净负荷分解方法,该方法可在不依赖于天气数据和光伏设备物理模型的情况下,对BTM光伏发电进行估计。所提算法在社区用户的DPVS通常表现出近似的光伏发电特性这一假设的情况下,利用邻近用户的净负荷来提取光伏发电信息作为彼此间相互的光伏代理。最终的光伏发电分解结果将根据最大信息系数(Maximal Information Coefficient, MIC)进行确定。实验结果表明,所提分解算法在不依赖气象数据的情况下拥有较高的分解精度。

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.