Inferring the behavior of distributed energy resources from incomplete measurements

虽然传感在电力系统中变得越来越普遍，但电力公用事业公司仍然经常缺乏分布式能源的行为的准确实时图像，例如电力负载和分布式太阳能。这些信息将帮助系统运营商、公用事业公司、能源效率提供商和需求响应提供商提高电力系统的可靠性、经济效率和环境影响。然而，传感基础设施是昂贵的，特别是考虑到我们可能有兴趣测量的大量数量。这项研究的目的是开发一种方法，从现有的电力系统测量数据中推断分布式能源资源集合的实时行为，这些测量数据具有层次性、异构性、不完整性和不同的质量。为了做到这一点，研究人员正在应用和推广利用动态系统模型的新兴在线学习技术。虽然方法论的发展植根于手头的电力系统应用，但这些扩展正在为信号处理提供新的研究方向。了解从现有数据可以推断出什么，不能推断出什么，将有助于公用事业公司确定额外传感器的价值，不同应用需要什么类型的传感器，以及将它们放置在哪里。这还将帮助政策制定者确定哪些基础设施投资是值得的，以及是否需要补贴。此外，结果将为有关消费者隐私的价值和成本的能源政策讨论提供信息，这将有助于制定政策，更好地平衡电力系统运营商、公用事业公司和第三方公司的目标与消费者的目标。这项研究正在应用一种名为在线动态学习(OLWD)的新兴技术，以确定从现有的电力系统测量和我们可能预期在短期内获得的测量中可以推断出什么，以及不能推断出什么。目前的在线学习算法不能处理时变现象，因为它们不包括动态模型，并且经典的在线估计算法对模型错误指定不具有鲁棒性。相反，OLWD使用一组模型(任意形式)，该算法同时估计状态并选择在下一个时间步长最好地预测状态的模型或模型组合。OLWD基于当前最成功的在线优化算法之一，继承了它的许多吸引人的特性。虽然这种方法很适合这个问题，但理论是不完整的。这项研究的一个关键组成部分是扩展OLWD，以处理分层的、异类的、不完整的和不同质量的测量。研究人员正在探索被动在线推理和主动在线推理，后者使用外部控制(例如，可控负载和可削减的太阳能光伏)来增强学习。此外，研究人员正在研究系统成本、推理准确性和消费者隐私之间的权衡。

While sensing is becoming more prevalent in power systems, electric utilities still often lack an accurate real-time picture of the behavior of distributed energy resources, such as electric loads and distributed solar power. Such information would help system operators, utilities, energy efficiency providers, and demand response providers improve power system reliability, economic efficiency, and environmental impact. However, sensing infrastructure is costly, especially when considering the large number of quantities we might be interested in measuring. The goal of this research is to develop methods to infer the real-time behavior of aggregations of distributed energy resources from existing power system measurements, which are hierarchical, heterogeneous, incomplete, and of varying quality. To do this, the researchers are applying and extending emerging online learning techniques that leverage dynamical system models. While methodological developments are grounded in the power system application at hand, the extensions are informing new research directions for signal processing. Knowledge of what can and cannot be inferred from existing data will help utilities determine the value of additional sensors, what type of sensors are needed for different applications, and where to put them. This will also help policy makers determine which infrastructure investments are worthwhile and the need, if any, for subsidies. Additionally, the results will inform energy policy discussions on the value and cost of consumer privacy, which will help develop policies that better balance the objectives of power system operators, utilities, and third-party companies with those of consumers. The research is applying an emerging technique, online learning with dynamics (OLWD), to determine what can and cannot be inferred from both existing power system measurements and measurements that we might expect to have in the near term. Contemporary online learning algorithms do not handle time-varying phenomena because they do not include dynamical models, and classical online estimation algorithms are not robust to model misspecification. In contrast, OLWD uses a collection of models (of arbitrary form) and the algorithm simultaneously estimates state and selects the model or combination of models that best predicts the state at the next time step. OLWD is based on one of the most successful current online optimization algorithms, inheriting many of its appealing properties. While the approach is well-suited to the problem, the theory is incomplete. A key component of the research is extend OLWD to handle measurements that are hierarchical, heterogeneous, incomplete, and of varying quality. The researchers are exploring both passive online inference and active online inference, where the latter uses external control (e.g., of controllable loads and curtailable solar photovoltaics) to enhance learning. Additionally, the researchers are characterizing trade-offs between system cost, inference accuracy, and consumer privacy.