Sequential Detection and Prediction for Solar Situation Awareness in Power Networks

该项目的总体研究目标是开发统计工具，以帮助电网运营商提高太阳能发电机的态势感知。目标包括使用顺序数据从实时电力供应和消耗数据推断太阳能发电机、其位置和状态。我们采取了一种新的方法，利用这一事实，即太阳能发电机的开和关将引入变点的电力供应和消费的差异时间序列，并提出了一个统计框架，通过检测变点和估计其幅度从顺序数据的太阳能态势感知。我们使用一种自下而上的方法，这是一个自然的适合电力网络的层次结构：我们在单元级检测变点，然后将它们聚集在一个网络级，使用多维点过程模型，以及考虑到固有的稀疏和低维性质的观察。将变点检测与多保真度模型相结合，实现网络级的发电量预测。结果将在模拟的高保真时间序列和动态电网的实际数据上得到验证。太阳能发电装置在住宅和商业领域都在增加。然而，很难知道这些光伏板的确切数量、位置、装机容量和生产状态，特别是由于光伏板的表后安装数量越来越多，以及太阳能生产的内在随机性。不知道网络中太阳能发电机的状态可能对配电和输电系统的稳定性和安全性构成重大挑战。配电系统中住宅光伏机组的数量、位置、容量和运行状态的精确知识对于配电系统的日常运行和规划以及最终对输电系统的日常运行和规划至关重要。目前，还没有有效的方法来实时推断太阳能发电机在大规模电网中的状态。拟议的研究将改变景观，并显着推进最先进的统计方法用于太阳态势感知。公用事业公司、行业监管机构和太阳能电池板营销人员是少数几个将从为态势感知开发的算法中受益的群体。例如，获得特定社区太阳能光伏发电状态的详细信息可以使当地公用事业公司更好地平衡该地区的电力供应和需求，提高最终用户的电力服务质量，并提高配电网的可靠性和安全性。该研究计划将与教育部分紧密结合，研究成果将作为开源软件提供给国家实验室，研究成果将通过会议和期刊出版物传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The overarching research objective of this project is to develop statistical tools to help power network operators in increasing the solar generator situational awareness. The goals include inferring the solar power generators, their locations, and status, from the real-time power supply and consumption data using sequential data. We take a novel approach leveraging the fact that the on and off of the solar generators will introduce change-points in the difference time series of the power supply and consumption, and propose a statistical framework for solar situational awareness by detecting change-points and estimating their magnitudes from sequential data. We use a bottom-up approach, which is a natural fit to the hierarchical structure of the power networks: we detect change-points at the unit level, and then aggregate them on a network level, using multi-dimensional point process model, as well as considering the inherent sparse and low-dimensional nature of the observations. The change-point detection will be combined with multi-fidelity models to achieve network level prediction of power generation. The results will be verified on simulated high-fidelity time-series and real-data over dynamic power networks.Solar power installations have been increasing in both the residential and commercial areas. However, it is very difficult to know the exact numbers, locations, installed capacities, and production status of these PV panels, especially due to the increasing number of behind-the-meter installations of PV panels and the intrinsic stochasticity in solar production. Not knowing the status of the solar generators in the network can pose a significant challenge to stability and security of power distribution and transmission systems. The precise knowledge of the number, location, capacity, and operational status of residential PV units within a distribution system will be very critical to the daily operation and planning of distribution system and eventually that of transmission systems. Currently, there is no effective way to infer in real-time the solar generators' status in a large-scale power network. The proposed research will change the landscape and significantly advance the state-of-the-art in using statistical methods for solar situational awareness. Utility companies, industry regulators and solar panel marketers are a few of the groups that will benefit from the algorithm developed for situational awareness. For example, having access to detailed information of the status of solar PV production in a given neighborhood can enable local utilities to better balance the area's power supply and demand, improve the quality of electricity service to end users, and increase the reliability and security of distribution power grids. The proposed research will be tightly integrated with education components, and the research results will be made available to national labs as open source software and research findings will be disseminated via conferences and journal publications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.