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

极端温度会使各种电网组件达到其运行极限。当温度升高超过一定的阈值时，大多数发电资源和电力系统组件的可用容量都会受到负面影响。不足为奇的是，这种由温度引起的可用发电和输电能力的减少通常与系统电力需求的增加相吻合，这主要归因于空调系统利用率的增加。忽略温度对各种网格资产的影响可能导致这些资产过载，导致寿命缩短和组件过早失效。因此，将环境温度的影响纳入电网运行是至关重要的，以防止组件受力，避免在极端温度期间因无法满足需求而导致停电。这个问题正变得越来越重要，因为气候模型预测热浪的持续时间和频率都会增加。在极端温度条件下停电不仅带来不便，还可能影响其他关键基础设施的可用性，如水处理厂、运输系统、医院和其他紧急护理单位。在这个项目中，研究人员将寻求一种可能的解决方案，包括设计一种方法，用于在极端环境温度下的配电系统中主动调度能源。传统上，电力公司通过两种方法来解决手头的问题：为各种组件定义动态热额定值（DTR），以根据环境温度调整其可用容量；最近，提供激励需求响应（DR）计划，用于在压力条件下远程关闭空调机组。虽然在许多情况下有效，但容易受到重大弱点的影响。首先，DTR通常是启发式的或实验性的，通常不适合封闭形式的数学计算。此外，基于空调的DR通常是根据公用事业公司和用户之间的合同协议实施的，并且确实考虑了用户的福祉（即，由于空调关闭，用户将体验到的室内温度）。在严重的热浪事件下，这可能会对健康造成负面影响，尤其是对婴儿和老年人。本建议的目标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，以及由于过载或在恶劣条件下运行而导致的组件寿命预期减少。住宅的室内温度通过开发房屋热模型纳入DR调度，该模型可以根据内部和外部增益确定室内温度。这就产生了一个多目标设计问题，其目标是在资产寿命和用户舒适度的基础上优化成本。为了解决模型中固有的不确定性，采用了鲁棒优化方法。为了确保优化问题的可追溯性和所提出的解决方案的可扩展性，将使用标准重构技术将非线性公式转换为凸混合整数二次约束规划问题。此外，为了能够了解用户状况，将基于非侵入式监控构建算法，使用智能电表提供的综合测量数据来检测人员占用和空调单元的状态。

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.