Inferring the behavior of distributed energy resources from incomplete measurements

从不完整的测量推断分布式能源的行为

• 批准号: 1508943
• 负责人: Johanna Mathieu
• 金额: $ 39.95万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1508943&HistoricalAwards=false
• 关键词: Inferring; behavior; distributed; energy; resources

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.

虽然传感在电力系统中变得越来越普遍，但电力公司仍然经常缺乏分布式能源（例如电力负载和分布式太阳能）行为的准确实时图片。这些信息将有助于系统运营商、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，特别是当考虑到大量的数量，我们可能有兴趣测量。本研究的目标是开发方法来推断现有的电力系统测量，这是分层的，异构的，不完整的，和不同的质量的分布式能源资源的聚合的实时行为。为此，研究人员正在应用和扩展利用动态系统模型的新兴在线学习技术。 虽然方法的发展是基于电力系统的应用，但扩展为信号处理提供了新的研究方向。从现有数据中可以推断出什么和不能推断出什么的知识将有助于公用事业确定额外传感器的价值，不同应用需要什么类型的传感器，以及将它们放在哪里。 这也将有助于决策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，研究结果将为能源政策讨论提供有关消费者隐私的价值和成本的信息，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与消费者的目标。 这项研究正在应用一种新兴的技术，动态在线学习（OLWD），以确定从现有的电力系统测量和我们可能期望在短期内获得的测量中可以推断出什么和不能推断出什么。当代在线学习算法不处理时变现象，因为它们不包括动态模型，并且经典在线估计算法对模型误指定不鲁棒。相比之下，OLWD使用一组模型（任意形式），并且算法同时估计状态并选择最佳预测下一个时间步的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，继承了其许多吸引人的特性。 虽然这种方法很适合这个问题，但理论是不完整的。研究的一个关键组成部分是扩展OLWD来处理层次化、异构、不完整和质量变化的测量。 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，可控制的负载和可削减的太阳能光伏发电），以提高学习。此外，研究人员正在描述系统成本，推理准确性和消费者隐私之间的权衡。