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.

大规模在时间变化和空间分离的分布式能源（DER），存储设备和灵活负载的扩展表现出对主动配电系统管理的固有计算挑战。同样，这些设备的操作和配置要求（例如微电网形成）的时间尺度涉及将动态系统模型集成到优化框架内。考虑到这些挑战，该项目旨在开发新的算法，以在功率分配系统中的最佳功率流，具有大量有源设备的功率分配系统中的子网模型以及一个辅助控制框架，以确保与供应商驱动的控制器无缝集成。现实生活中的数据和本地公用事业的模型将用于证明该方法的可行性。 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该项目将调查北卡罗来纳州。使用大量有源设备的功率分配系统中的子网建模，c）一个二级控制框架，可提供改进的主动/反应性功率控制，并可以确保与供应商驱动的控制器的无缝集成，从而增强功率质量和稳定性，d）实现平台，包括与当地实用程序的通信数据，以证明该方法的真实数据和模型。所提出的优化框架可以在功率分配系统中的所有活动节点上为决策控制变量和设定点提供全局解决方案。此外，考虑每个子网，该方法可以在设备（例如DER和能量存储）等设备中结合随机或确定性变化。此外，该体系结构可以与现有的Vendend驱动的控制器无缝集成，因此能够适应分布式资源的高供应，并为决策和网格状态变量提供低成本的解决方案。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的criteria criteria crietia crietia crietia criteria criperia crietia crietia crietia crietia criteria crietia crietia criteria criperia criteria均值得一堂。