Collaborative Research: Robust Asset-and-User-Aware Dispatch of the Power Distribution Grid during Extreme Temperatures

合作研究：极端温度下配电网的鲁棒资产和用户感知调度

• 批准号: 1610703
• 负责人: Mina Sartipi
• 金额: $ 11.98万
• 依托单位: University of Tennessee Chattanooga
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610703&HistoricalAwards=false
• 关键词: Collaborative; Research; Robust; Asset; User

Extreme temperatures can push various power grid components to their operational limits. The available capacity of most generation resources and power system components becomes negatively affected as the temperature increases beyond certain thresholds. Not surprisingly, this temperature-induced reduction in available power generation and transmission capacities generally coincides with increased electricity demand on the system, mostly attributed to the increased utilization of air-conditioning (A/C) systems. Ignoring the effects of temperature on various grid assets could lead to overloading these assets, resulting in reduced lifetime and premature component failure. It is therefore crucial to incorporate the effects of ambient temperature into power grid operation in order to prevent stress on components and avoid blackouts that could result from failure to meet demand during periods of extreme temperature. This issue is becoming more important since climate models project an increase in the duration and frequency of heat waves. Loss of power during extreme temperature conditions is not merely an inconvenience, as it may also impact the availability of other critical infrastructures such as water sanitation plants, transportation systems, and hospitals and other urgent care units. In this project, the researchers will pursue a possible solution that involves design of a methodology for proactive dispatch of the energy resources in a distribution system exposed to extreme ambient temperatures. Electric utilities have traditionally addressed the issue at hand through two means: defining dynamic thermal ratings (DTR) for various components to adjust their available capacity based on ambient temperature and, more recently, offering incentivized demand response (DR) programs for remotely shutting down A/C units under stressed conditions. Although effective in many instances, are vulnerable to significant weaknesses. First, DTR are often assigned heuristically or experimentally, and are not usually amenable to closed form mathematical calculation. Also, A/C-based DR is usually implemented based on the contractual agreements between the utility and the users, and does incorporate users' well-being (i.e., the indoor temperature users will experience due to A/C shutdown). This, under severe heat wave events, can potentially lead to negative health impacts especially on infants and the elderly. The goal of this proposal is to improve the effectiveness of both these tools. The proposed solution models the effects of excess temperatures on available generation/transmission capacity of components, as well as on expected reduction in component lifespan due to overloading or operating under harsh conditions. Indoor temperatures at residential homes are incorporated into the DR dispatch by developing thermal models for houses, which can determine the indoor temperature based on internal and external gains. This creates a multi-objective design problem in which the aim is to optimize cost in conjunction with asset lifetime and user comfort. To address the inherent uncertainties in the model, a robust optimization approach is adopted. To ensure tractability of the optimization problem and the scalability of the proposed solution, standard restructuring techniques will be used to transform the nonlinear formulation into a convex, mixed-integer quadratically constrained programming problem. Furthermore, to enable awareness on user conditions, algorithms will be built based on non-invasive monitoring to detect human occupancy and status of A/C units using the aggregate measurements available from smart meters.

极端的温度可以将各种电网组件推向其操作限制。随着温度升高，大多数发电资源和电力系统组件的可用容量会对超出某些阈值的升高产生负面影响。毫不奇怪，这种温度引起的可用发电能力和传输能力的降低通常与该系统的电力需求增加一致，这主要归因于空调（A/C）系统的利用率增加。忽略温度对各种网格资产的影响可能会导致这些资产过载，从而导致寿命减少和过早的组件故障。因此，至关重要的是将环境温度的影响纳入电网操作中，以防止对组件的压力并避免停电，而停电可能是由于在极端温度时期无法满足需求而导致的。由于气候模型会增加热浪的持续时间和频率，因此这个问题变得越来越重要。在极端温度条件下失电的不便不仅是不便，因为它也可能影响其他关键基础设施的可用性，例如水卫生厂，运输系统，医院以及其他紧急护理部门。在该项目中，研究人员将寻求一种可能的解决方案，该解决方案涉及设计一种方法，以主动调度具有极端环境温度的分配系统中的能源。传统上，电力公司通过两种方法解决了当前的问题：定义动态热等级（DTR），以使各种组件根据环境温度调整其可用容量，并且最近提供激励性需求响应（DR）程序，以远程关闭压力条件下的A/C单元。尽管在许多情况下有效，但容易受到重大弱点的影响。首先，DTR通常是通过启发性或实验分配的，通​​常不适合封闭形式的数学计算。此外，通常基于A/C的DR是根据公用事业公司与用户之间的合同协议实施的，并且确实合并了用户的福祉（即，由于A/C的关闭，室内温度用户将体验到）。在严重的热浪事件下，这可能会导致负面影响负面影响，尤其是对婴儿和老年人。该提案的目的是提高这两种工具的有效性。提出的解决方案模拟了过高温度对组件可用发电/传输能力的影响，以及由于在恶劣条件下过载或运行而导致的组件寿命的预期降低。住宅的室内温度通过开发房屋的热模型纳入了DR调度，该模型可以根据内部和外部增益来确定室内温度。这会产生一个多目标设计问题，其目的是通过资产寿命和用户舒适性优化成本。为了解决模型中固有的不确定性，采用了强大的优化方法。为了确保优化问题的障碍和提议的解决方案的可伸缩性，标准重组技术将用于将非线性公式转换为凸，混合构成四边形约束的编程问题。此外，为了使对用户条件的认识，将基于非侵入性监视构建算法，以使用智能电表可用的汇总测量值来检测人类的占用和A/C单元状态。