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Data-Driven Operation and Control of Active Power Distribution Systems with High Penetration of Distributed Energy Resources and Energy Storage

分布式能源与储能高渗透主动配电系统的数据驱动运行与控制

基本信息

批准号：
1810174
负责人：
Sukumar Kamalasadan
金额：
$ 36万
依托单位：
University of North Carolina at Charlotte
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1810174&HistoricalAwards=false
关键词：
Data Driven Operation Control Active

项目摘要

The expansion of large scale temporally changing and spatially separated distributed energy resources (DERs), storage devices and, flexible loads exhibit inherent computational challenges for management of active power distribution systems. Also, time-scale of operation of these devices and configuration requirements such as micro grid formation, involves integrating dynamic system models within the optimization framework. Considering these challenges, this project aims to develop novel algorithms for optimal power flow in power distribution systems, distributed modeling of subnetworks in power distribution system with large number of active devices, and a secondary control framework that can ensure seamless integration with vendor driven controllers. Real-life data and models from the local utility will be utilized to demonstrate feasibility of the methodology. Educational and outreach activities of this project include a) engaging students in problem-based learning from diverse undergraduate and graduate groups including under-represented and minority students, b) designing advanced curriculum on power system control with convex optimization and distributed control approaches, c) providing a platform to motivate and attract students in engineering especially the underrepresented minorities and women, and d) developing a comprehensive dissemination platform through PIs research lab and the center at University of North Carolina at Charlotte.The project will investigate: a) a novel Receding Horizon Control (RHC) based mixed-integer second order cone programming (MISOCP) model for optimal power flow in power distribution systems that can scale up to integrate thousands of aggregated nodes and provide set points (integer or real) for passive and active devices considering unbalanced distribution system operation, b) a stochastic model predictive consensus framework for distributed modeling of subnetworks in power distribution system with large number of active devices, c) a secondary control framework that provides improved active/reactive power control and can ensure seamless integration with vendor driven controllers in turn enhancing power quality and stability, and d) an implementation platform including communication loops with real-life data and models from the local utility that proves feasibility of the methodology. The proposed optimization framework can provide global solutions for decision control variables and set points, including switches and transformer taps, at all active nodes in power distribution system. Also, the methodology can incorporate stochastic or deterministic changes in the devices such as DERs and energy storage, considering each subnetwork. Moreover, the architecture can be seamlessly integrated with the existing vender driven controllers thus capable of accommodating high in-feed of distributed resources and providing a low-cost solution to exponential increase in decision and grid state variables.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大规模的时间变化和空间分离的分布式能源（DER），存储设备和灵活的负载的扩展表现出固有的计算挑战的主动配电系统的管理。此外，这些设备的操作的时间尺度和配置要求，如微电网的形成，涉及集成动态系统模型的优化框架内。考虑到这些挑战，本项目的目标是开发新的算法在配电系统中的最优潮流，在配电系统中的子网络的分布式建模与大量的有源设备，和一个辅助控制框架，可以确保无缝集成与供应商驱动的控制器。将利用当地公用事业的实际数据和模型来证明该方法的可行性。该项目的教育和推广活动包括：a）让学生从不同的本科生和研究生群体（包括代表性不足的学生和少数民族学生）中进行基于问题的学习，B）利用凸优化和分布式控制方法设计电力系统控制高级课程，c）提供一个平台，激励和吸引工程专业的学生，特别是代表性不足的少数民族学生和妇女，以及d）通过PI研究实验室和位于夏洛特的北卡罗来纳州大学的中心开发一个综合传播平台。该项目将调查：a）一种新的基于滚动时域控制（RHC）的混合整数二阶锥规划（MISOCP）配电系统中的最优潮流模型，可以扩展到集成数千个聚合节点并提供设定点（整数或真实的），B）用于具有大量有源设备的配电系统中的子网络的分布式建模的随机模型预测一致性框架，c）辅助控制框架，其提供改进的主动/被动控制。无功功率控制，并可确保与供应商驱动的控制器无缝集成，进而提高电能质量和稳定性，以及d）包括具有来自本地公用事业的真实数据和模型的通信回路的实现平台，其证明了该方法的可行性。所提出的优化框架可以提供全局解决方案的决策控制变量和设置点，包括开关和Transformer抽头，在所有的有源节点的配电系统。此外，考虑到每个子网络，该方法可以在诸如DER和能量存储的设备中结合随机或确定性变化。此外，该架构可以与现有的供应商驱动的控制器无缝集成，从而能够适应分布式资源的高馈入，并为决策和网格状态变量的指数增长提供低成本的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。