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.

配电系统运营 (DSO) 旨在在存在可预测变化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，分布式能源资源（例如太阳能、电动汽车、存储、智能负载）的深入渗透以及网络管理新型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绩效和收入将受到严重损害。如何适应 未来的系统将从消费者、线路传感器和网络设备产生大量数据。利用这些数据进行学习、预测和资源协调具有挑战性，而且尚未得到充分理解。该提案旨在利用现代数据分析将“比特与瓦特”连接起来，以便为未来的配电网络提供可扩展且具有成本效益的 DSO。该提案特别探索了机器学习、优化和行为学习的新方法及其在电力系统中的应用。这些方法将在软件平台中实施：需求运营和管理的可视化和洞察（VISDOM）。该提案的研究部分将实现减排和大规模扩展表后资源的管理。它为新兴的智能电网数据分析行业做出了贡献，预计到 2020 年，市场规模将达到 60 亿美元。该提案的教育部分将创建数据思维方面的新颖课程和在线教育，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型时空数据集，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）建立一个可扩展到大量消费者的适应性消费者行为学习框架； (ii) 研究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) 开发新型网络重建和监控框架，从数据中了解配电网络； (iv) 创建数据和模拟驱动的住宅需求方资源安置和协调机制； (v) 利用互动平台让消费者实时参与，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价值提高 50% 以上等影响。由此产生的方法将在 VISDOM 平台上以开源形式提供。 VISDOM 可以支持由学术界和行业合作伙伴组成的蓬勃发展的社区，以尝试需求方管理。目前，每个项目都开发了不透明且有限的分析机制，耗费时间和资源。更广泛地说，该提案中开发的基于时间序列数据的方法适用于营销、医疗保健和电子商务等其他领域。教育部分将把数据思维的概念推进到电力系统。拟议的课程包括为本科生和硕士生开设能源系统数据分析的新实践课程；针对公用事业专业人士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 实验室而准备的 K12 实验实习。此外，还将支持并在线提供由学术界和工业界杰出演讲者参加的智能电网研讨会。