CyberSEES Type 2: Achieving Clean Power System Flexibility: Sensing, Modeling, and Optimal Control

Cyber​​SEES 类型 2：实现清洁电力系统灵活性：传感、建模和最优控制

• 批准号: 1539585
• 负责人: Duncan Callaway
• 金额: $ 119.99万
• 依托单位: University of California-Berkeley
• 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: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539585&HistoricalAwards=false
• 关键词: CyberSEES; Type; Achieving; Clean; Power

Growing renewable generation capacity is leading to unprecedented variability and uncertainty in power system operations, and a lack of power system flexibility threatens further increases in global sustainability in electricity sectors across the globe. This project seeks to aggregate and control demand-side resources to provide this flexibility through a distributed control framework that is robust to failures in cyber-infrastructure, and is scalable across institutional dynamics, energy markets, and levels of power system development infrastructure. Research objectives in this proposal will advance current applications and theories in computing, modeling and control, and the effectiveness and scalability of this work will be rigorously demonstrated through a field trial in Nicaragua. This project will result in new tools to facilitate sustainable energy systems throughout the globe, an open data platform that strengthens the ability of global researchers to develop global solutions to challenges underling the integration of renewable sources, and advances in education to equip the workforce with new tools to tackle sustainability challenges. The envisioned system employs a centralized computing infrastructure to produce optimal control laws for individual thermostatically controlled loads. The control laws can operate with or without reliable cyber-infrastructure in real time; in the absence of cyber-infrastructure, control can be based on time of use and local frequency measurements only. Control laws will be published by a central server and periodically accessed by local intelligence at each load. Challenges to this work include creating computing infrastructure that can actively distribute control laws and validate participation, modeling and control methods that are effective on real-world loads, and identifying control strategies with effective incentive mechanisms for consumers to participate. At the conclusion of the project the investigators will have: (1) built a framework by which an innovative, cloud-based data management platform called Mezuri can robustly track the provenance and processing of all data and disseminate control laws; (2) developed hierarchical statistical models for state estimation and prediction of large fleets of thermally controlled loads that directly include the effects of human behavior, and adapt models to require as few observations as possible; and (3) devised new stochastic optimal control formulations that account for human activity and accommodate both system- and local-level objectives. Taken together, these contributions build a new framework for control of demand-side resources that is globally scalable in a short time horizon.

不断增长的可再生能源发电能力正在导致电力系统运行的前所未有的可变性和不确定性，而电力系统灵活性的缺乏威胁着地球仪电力部门全球可持续性的进一步提高。 该项目旨在通过分布式控制框架来聚合和控制需求方资源，以提供这种灵活性，该框架对网络基础设施的故障具有鲁棒性，并且可以跨机构动态，能源市场和电力系统开发基础设施的水平进行扩展。 该提案中的研究目标将推动当前计算，建模和控制方面的应用和理论，这项工作的有效性和可扩展性将通过在尼加拉瓜的现场试验得到严格证明。 该项目将产生新的工具，以促进整个地球仪的可持续能源系统，一个开放的数据平台，加强全球研究人员的能力，以制定全球解决方案，解决可再生能源整合所带来的挑战，并促进教育，使劳动力掌握新的工具，以应对可持续性挑战。 设想的系统采用集中式计算基础设施，以产生最佳的控制规律，为个别恒温控制的负载。 控制律可以在真实的时间中在有或没有可靠的网络基础设施的情况下操作;在没有网络基础设施的情况下，控制可以仅基于使用时间和本地频率测量。 控制法将由中央服务器发布，并在每次加载时由本地智能定期访问。 这项工作面临的挑战包括创建计算基础设施，可以主动分发控制律和验证参与，建模和控制方法，是有效的现实世界的负载，并确定控制策略与有效的激励机制，消费者参与。 在项目结束时，研究人员将：（1）建立一个框架，通过这个框架，一个名为Mezuri的创新的基于云的数据管理平台可以强大地跟踪所有数据的来源和处理，并传播控制法律;（2）开发了用于状态估计和预测的直接包括人类行为影响的大型热控负荷群的分层统计模型，调整模型，使其需要尽可能少的观测;（3）设计新的随机最优控制公式，考虑人类活动，并适应系统和局部目标。 综合起来，这些贡献建立了一个新的框架，用于控制需求方资源，在短期内可在全球范围内扩展。