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

职业：用于配电系统管理和运营的开源数据分析

• 批准号: 1554178
• 负责人: Ram Rajagopal
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1554178&HistoricalAwards=false
• 关键词: CAREER; Open; Source; Data; Analytics

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.

配电系统运行（DSO）的目的是在可预测的变异性存在的情况下保持可靠性。未来的配电系统将在一个完全不同的环境中运行，分布式能源（如太阳能、电动汽车、储能、智能负载）的深度渗透，以及网络管理新设备的广泛采用，导致变异性的增加。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。未来的系统将从消费者、线路传感器和网络设备中产生大量数据。利用这些数据进行学习、预测和资源协调是具有挑战性的，而且还没有被完全理解。该提案旨在利用现代数据分析将“比特到瓦”连接起来，以便为未来的配电网络实现可扩展和具有成本效益的DSO。该提案特别探讨了机器学习，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现：需求操作和管理的可视化和洞察（VISDOM）。该提案的研究部分将使减排和大规模管理仪表后面的资源成为可能。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颖的课程和数据思维在线教育，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。将解决的核心问题是：(i)建立一个适应消费者行为的学习框架，可扩展到大量消费者；调查从个人住宅消费者到社区等多个尺度的概率需求预测和定价方法；发展一种新的网络重建和监测框架，从数据中学习配电网；为住宅需求方资源建立数据和模拟驱动的安置和协调机制；(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。预计会产生诸如将消费者需求灵活性的价值提高50%以上等影响。生成的方法将在VISDOM平台上以开源方式提供。VISDOM可以支持一个蓬勃发展的学术界和行业合作伙伴社区，他们可以尝试需求侧管理。目前，每个项目都开发了不透明和有限的分析机制，这消耗了时间和资源。更广泛地说，本提案中制定的基于时间序列数据的方法适用于市场营销、医疗保健和电子商务等其他领域。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开设一门新的能源系统数据分析实践课程；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，以及在暑期项目中访问PI实验室的高中教师准备的K12实验实习。此外，来自学术界和工业界的杰出演讲者将参加智能电网研讨会，并将在网上提供支持。