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

基本信息

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

项目摘要

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算分布式能源资源的能力，以提供电网的需求侧服务，尤其是从弹性载荷集合中的虚拟能源存储（VES）。虚拟能源存储是指负载的电力消耗（例如空调）的小小变化，因此所产生的变化看起来像是将电池充电和排放到其余电网上的充电和排放。由于不需要新的硬件，预计虚拟能源存储将比实际电池便宜。唯一的更改是软件。部署这项技术的瓶颈缺乏工具来回答有关大量负载的同等电池容量的问题，例如“我们需要提供与5MW/3MWH/3MWH Li-ion电池相同的服务？”。该项目将开发数学技术来回答此类问题以及更多内容，从而使网格操作和计划具有未来的智能负担。因此，该项目将有助于提高国家电力供应的可靠性和弹性。所提出方法的智力优点是它们是“未来证明”，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方法，这些方法不需要简单的减少难以获得的DER的订单模型。该项目的更广泛的影响是通过提供操作和计划的计算方法来帮助电网运营商采用智能负载技术。其他广播公司的影响包括对PI机构的课程增强，并通过研究结果吸引了代表性不足和少数族裔本科生进行STAM研究。由于每个负载必须满足消费者在提供网格支持方面的本地服务质量，因此大量负载提供VES的能力对这些本地服务限制的依赖性很高。除MW/MWH的数字外，电网运营商还需要确定对于收集负载的何种需求出发，因此没有人必须违反其本地限制。例如，当空调提供需求侧服务时，室内温度不应变得太热或冷。这些挑战以两个不同的时间尺度解决：短期计划（以小时）和长期计划（以几个月的形式）。对于短期计划，提出了一个确定性的框架，其中通过一组低维约束来描述可行的需求轨迹。一个关键的创新是，由于负载的锁定限制因素通过集成平均值而降低了高维整数约束。为了长期计划，提出了基于光谱密度的统计框架，它可以表征所有可能的可行需求出发信号的路径。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响标准来评估NSF的法定任务。