CAREER:Open-Source Data Analytics for Distribution Systems Management and Operations

配电系统运行（DSO）的目的是在可预测的变化存在下保持可靠性。未来的配电系统将在一个截然不同的环境中运行，分布式能源（例如太阳能、电动汽车、储能、智能负载）的深度渗透以及新型网络管理设备的广泛采用将导致可变性增加。除非DSO能够适应这些条件，否则未来公用事业的业绩和收入将严重受损。未来的系统将产生来自消费者、线路传感器和网络设备的大量数据。利用这些数据进行学习，预测和资源协调具有挑战性，并且尚未完全理解。该提案旨在利用现代数据分析将“比特到瓦特”连接起来，以便为未来的分销网络提供可扩展且具有成本效益的DSO。特别是，该提案探讨了机器学习，优化和行为学习及其在电力系统中的应用的新方法。这些方法将在一个软件平台中实施：需求运营和管理的可视化和洞察力（VISDOM）。该提案的研究部分将实现减排和大规模管理电表背后的资源。它有助于新兴的智能电网数据分析行业，预计到2020年将达到60亿美元的市场规模。该提案的教育部分将创建一个新的数据思维课程和在线教育，以培养未来的数据分析人才。该项目将利用来自工业和公用事业的大型空间和时间数据集，探索机器学习，随机控制优化和行为经济学的新方法，以解决电力系统中的问题。将解决的中心问题是：（i）建立一个自适应的消费者行为学习框架，可扩展到大量的消费者;（ii）调查从个人住宅消费者到社区的多个尺度的概率需求预测和定价方法;（iii）开发一个新的网络重建和监测框架，从数据中学习配电网络;（iv）为住宅需求方资源创建数据和模拟驱动的安置和协调机制;以及（v）利用互动平台，让消费者实时参与开发新的随机试验方法，并将其应用于创新的行为计划。预计消费者需求灵活性的价值将增加50%以上。由此产生的方法将在VISDOM平台上以开放源码提供。 VISDOM可以支持一个蓬勃发展的学术界和行业合作伙伴社区，他们正在尝试需求侧管理。目前，每个项目都开发了不透明和有限的分析机制，消耗了时间和资源。更广泛地说，本提案中开发的基于时间序列数据的方法适用于其他领域，如营销，医疗保健和电子商务。教育部分将推进从数据思维到电力系统的概念。拟议的课程包括为本科生和硕士生提供能源系统数据分析的新实践课程;针对公用事业专业人员和更广泛受众的在线成人教育课程，以及与高中教师一起准备的K12实验实习访问PI的实验室在夏季计划中。此外，还将支持举办一次智能电网研讨会，邀请学术界和工业界的杰出人士发言，并在网上提供。

Distribution system operations (DSO) are designed to maintain reliability in the presence of predictable variability. Future distribution systems will operate in a dramatically different environment with deep penetration of distributed energy resources (e.g. solar, EVs, storage, smart loads) and widespread adoption of novel devices for network management resulting in increased variability. Unless DSO can be adapted to these conditions, performance and revenues of future utilities will be severely impaired. How to adapt Future systems will generate a wealth of data from consumers, line sensors and network equipment. Utilizing this data for learning, prediction and resource coordination is challenging and not fully understood. This proposal seeks to connect "bits to watts" utilizing modern data analytics in order to enable scalable and cost effective DSO for future distribution networks. In particular the proposal explores new approaches in machine learning, optimization and behavior learning and their applications in power systems. The methods will be implemented in a software platform: Visualization and Insight for Demand Operations and Management (VISDOM). The research component of the proposal will enable emissions reductions and massive scaling of the management of behind the meter resources. It contributes to the budding smart grid data analytics industry expected to reach a $6 billion market size by 2020. The education component of the proposal will create a novel curriculum and online education in data thinking to prepare the data analytics workforce of the future. The project will make use of large spatial and temporal data sets from industry and utilities to explore new approaches in machine learning, stochastic control & optimization and behavioral economics to address problems in power systems. The central problems that will be addressed are: (i) Build an adaptive consumer behavior learning framework that scales to large numbers of consumers; (ii) Investigate probabilistic demand forecasting and pricing methods at multiple scales ranging from individual residential consumers to communities; (iii) Develop a novel network reconstruction and monitoring framework to learn the power distribution network from data; (iv) Create data and simulation driven placement and coordination mechanisms for residential demand-side resources; and (v) Utilize an interactive platform that engages consumers in real-time to develop novel randomized trial approaches and apply it to innovative behavioral programs. Impacts such as increasing the value of consumer demand flexibility by more than 50% are expected. The resulting methods will be made available in open-source in the VISDOM platform. VISDOM can support a thriving community of academics and industry partners that experiment with demand side management. Currently, every project develops non-transparent and limited analysis mechanisms that consume time and resources. More broadly, the time-series data based approaches developed in this proposal are applicable to other fields such as marketing, healthcare and e-commerce. The education component will advance concepts from data thinking into power systems. The proposed curriculum includes a new hands-on course in data analytics for energy systems for undergraduate and masters students; online adult education courses directed at utility professionals and a broader audience and a K12 experimental practicum prepared with high school teachers visiting the PI's lab in a summer program. In addition, a smart grid seminar involving distinguished speakers from academia and industry will be supported and made available online.