Characterizing capacity of controllable DERs to provide energy storage service to the power grid

表征可控分布式能源为电网提供储能服务的能力

• 批准号: 2122313
• 负责人: Sean Meyn
• 金额: $ 35.97万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122313&HistoricalAwards=false
• 关键词: Characterizing; capacity; controllable; DERs; provide

The aim of this project is to develop methods to estimate the capacity of distributed energy resources to provide demand side services to the power grid, in particular, Virtual Energy Storage (VES) from a collection of flexible loads. Virtual energy storage refers to small controlled changes in the electrical power consumption of loads, such as air conditioners, so that the resulting variation appears like the charging and discharging of a battery to the rest of the power grid. Virtual energy storage is expected to be cheaper than real batteries since no new hardware is needed; the only change is software. A bottleneck in deploying this technology has been the lack of tools to answer questions about the equivalent battery capacity of large collections of loads, such as, "How many smart residential air conditioners do we need to provide the same service as a 5MW/3MWh Li-ion battery?". The project will develop mathematical techniques to answer such questions and more, and thereby enable grid operation and planning with smart loads of the future. The project will thus contribute to increasing the reliability and resiliency of the nation’s electricity supply. An intellectual merit of the proposed methods is that they are "future proof" since they are independent of the algorithms used to coordination the loads. Another intellectual merit is the data-driven methods proposed, which do not need simple reduced order models of DERs that are difficult to obtain. A broader impact of the project is to help grid operators adopt smart load technology by providing computational methods for operation and planning. Other broader impacts include enhancements to the curriculum in the PI's institution with research results and attracting underrepresented and minority undergraduates to STEM research. Because each load must satisfy the consumer's local quality of service in providing grid support, the capacity of a large collection of loads to provide VES has a complex dependency on these local quality of service constraints. Apart from MW/MWh numbers, grid operators also need to determine what kind of demand deviation is feasible for a collection of loads so that no one has to violate its local constraints. For instance, the indoor temperature should not become too hot or cold when an air conditioner is providing demand side service. These challenges are addressed in two distinct time scales: short term planning (in hours) and long-term planning (in months). For short term planning, a deterministic framework is proposed in which feasible demand trajectories are described via a set of low dimensional constraints. A key innovation is to reduce high-dimensional integer constraints due to lock-out constraints at loads into low-dimensional convex constraints for the collection through ensemble averages. For long term planning, a statistical framework based on spectral densities is proposed, which enables characterizing all possible samples paths of feasible demand deviation signals.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.

该项目的目的是开发方法来估计分布式能源的容量，以向电网提供需求侧服务，特别是来自灵活负载集合的虚拟储能（VES）。虚拟能量存储是指负载（如空调）的电力消耗的微小受控变化，因此产生的变化就像电池对电网其余部分的充电和放电一样。虚拟储能预计将比真实的电池便宜，因为不需要新的硬件;唯一的变化是软件。部署这项技术的一个瓶颈是缺乏工具来回答有关大量负载的等效电池容量的问题，例如，“我们需要多少智能住宅空调才能提供与5 MW/3 MWh锂离子电池相同的服务？".该项目将开发数学技术来回答这些问题，从而实现未来智能负荷的电网运行和规划。因此，该项目将有助于提高国家电力供应的可靠性和弹性。所提出的方法的一个智力优点是它们是“面向未来的”，因为它们独立于用于协调负载的算法。另一个智力优点是提出的数据驱动的方法，它不需要简单的降阶模型的DER是很难获得的。该项目更广泛的影响是通过提供运行和规划的计算方法，帮助电网运营商采用智能负荷技术。其他更广泛的影响包括增强PI机构的课程与研究成果，并吸引代表性不足和少数民族的本科生STEM研究。因为每个负载必须满足消费者的本地服务质量，在提供网格支持，一个大的负载集合，以提供VES的能力有一个复杂的依赖于这些本地服务质量的约束。除了MW/MWh数字，电网运营商还需要确定什么样的需求偏差对于负载集合是可行的，以便没有人必须违反其本地约束。例如，当空调器提供需求侧服务时，室内温度不应变得太热或太冷。这些挑战在两个不同的时间范围内得到解决：短期规划（以小时计）和长期规划（以月计）。对于短期规划，提出了一个确定性的框架，其中可行的需求轨迹描述通过一组低维约束。一个关键的创新是减少高维整数约束，由于锁定约束在负载到低维凸约束的集合，通过合奏平均。对于长期规划，提出了一个基于频谱密度的统计框架，它可以表征可行需求偏差信号的所有可能样本路径。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A unified framework for coordination of thermostatically controlled loads

• DOI: 10.1016/j.automatica.2023.111002
• 发表时间: 2021-08
• 期刊: ArXiv
• 作者: Austin R. Coffman;A. Bušić;P. Barooah

Aggregate Flexibility Capacity of TCLs With Cycling Constraints

TCL 受骑行限制的综合灵活性能力

• DOI: 10.1109/tpwrs.2022.3160071
• 发表时间: 2023
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Coffman, Austin R.;Cammardella, Neil;Barooah, Prabir;Meyn, Sean
• 通讯作者: Meyn, Sean

On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study

• DOI: 10.1016/j.apenergy.2022.119752
• 发表时间: 2022-10
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: N. Raman;Bo Chen;P. Barooah

On the economics of rooftop solar PV adoption

论屋顶太阳能光伏发电的经济性

• DOI: 10.1016/j.enpol.2023.113611
• 发表时间: 2023
• 期刊: Energy Policy
• 影响因子: 9
• 作者: Agdas, Duzgun;Barooah, Prabir
• 通讯作者: Barooah, Prabir