基于最大信息系数的用户光伏出力分解技术
Maximal Information Coefficient Based Residential Photovoltaic Power Generation Disaggregation

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。
Due to policy support, low cost and easy applicability, distribution photovoltaic systems (DPVSs) are increasingly popular among residential community. However, small-scale DPVSs of less than 10 kWp are always installed behind the meter (BTM), without metering the photovoltaic (PV) power generation separately, which results in the invisible of the PV power generation. Only access of net load data can result in non-optimal distribution network control and optimization, leading to a series of energy management problems. In order to solve the aforementioned problems, this paper proposes a BTM net load disaggregation method focusing on small-scale DPVSs, with only net load data of residential users in a community, without relying on weather data and models assumption. Considering that community users’ DPVSs usually exhibit approximate output characteristics, neighboring net load is used to extract PV power generation information as mutual proxies. After obtaining approximate PV proxy data by subtracting composite power of inter-users, Maximal Information Coefficient (MIC) is performed to obtain final PV power generation disaggregation results. Testing results show that the proposed method achieves considerable disaggregation accuracy in the absence of weather data.