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

配电系统运行(DSO)被设计为在存在可预测的变化性的情况下保持可靠性。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(如太阳能、电动汽车、存储、智能负载)的深入渗透，以及新型网络管理设备的广泛采用，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受到严重损害。如何适应未来的系统将产生来自消费者、线路传感器和网络设备的丰富数据。利用这些数据进行学习、预测和资源协调是具有挑战性的，也没有得到充分的理解。这项建议寻求利用现代数据分析将“比特到瓦特”连接起来，以便为未来的配电网络实现可扩展和经济高效的DSO。特别是，该提案探索了机器学习、优化和行为学习的新方法及其在电力系统中的应用。这些方法将在一个软件平台上实施：需求运营和管理的可视化和洞察(VISDOM)。该提案的研究部分将使减排和仪表资源管理的大规模扩展成为可能。它为萌芽中的智能电网数据分析行业做出了贡献，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课程和数据思维的在线教育，为未来的数据分析劳动力做好准备。该项目将利用来自工业和公用事业的大量空间和时间数据集，探索机器学习、随机控制和优化以及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)建立可扩展到大量消费者的适应性消费者行为学习框架；(Ii)在从个人住宅消费者到社区的多个尺度上调查概率需求预测和定价方法；(Iii)开发新的网络重构和监测框架，以从数据中学习配电网；(Iv)为住宅需求侧资源创建数据和模拟驱动的配置和协调机制；以及(V)利用实时吸引消费者的互动平台，开发新颖的随机试验方法，并将其应用于创新的行为计划。消费者需求弹性价值提升50%以上等影响在意料之中。由此产生的方法将在VISDOM平台上以开源方式提供。VISDOM可以支持一个由尝试需求侧管理的学者和行业合作伙伴组成的蓬勃发展的社区。目前，每个项目都开发了耗费时间和资源的不透明且有限的分析机制。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。教育部分将把从数据思维到电力系统的概念推向前进。拟议的课程包括为本科生和硕士学生开设的新的能源系统数据分析实践课程；面向公用事业专业人员和更广泛受众的在线成人教育课程；以及由高中教师在暑期项目中参观PI的实验室准备的K12实验实习。此外，还将支持由学术界和工业界杰出演讲者参加的智能电网研讨会，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.