Collaborative Research: A Framework for Assessing the Impact of Extreme Heat and Drought on Urban Energy Production and Consumption

这项研究将产生新的，严格的和综合的方法，这将有助于社会的能力，有效地管理城市规模的电力系统在极端炎热和干旱时期。包括电力消费者和生产者在内的现代电网面临着重大挑战和不确定性，包括热浪和干旱的频率和强度增加。与此同时，电力生产和消费的基础过程变得越来越复杂，部分原因是电网连接技术和控制它们的需求方管理计划的进步。这项研究将提高对特定城市未来极端高温和干旱情景的认识，以及量化这些情景下极端事件对城市电力消费和生产的影响。这项研究的结果有可能对城市和电力公司在极端高温和干旱条件下管理电网的方式产生重大影响。该项目解决了气候建模，消费和生产之间的重要联系，并寻求为以下科学进步做出贡献：1）开发有效方法，利用不同保真度的不同数据源，生成精确的未来极端高温和干旱情景，例如历史观测; 2）开发一个灵活的电力消耗模型，该模型采用自下而上的能源和需求建模方法，与需求方管理战略相结合; 3）开发一个数据驱动的随机优化方法，用于考虑各种不确定性来源的稳健发电决策战略，包括天气和电力的消费和生产。

This research will produce novel, rigorous and integrative methods that will contribute to society's capacity for effectively managing city-scale electricity systems during periods of extreme heat and drought. The modern electric grid, which includes both electricity consumers and producers, faces significant challenges and uncertainties, including increased frequency and intensity of heat waves and droughts. Concurrently, the processes underlying electricity production and consumption are becoming more complex, due in part to advances in electric grid-connected technologies and the demand-side management programs that control them. This research will improve knowledge of city-specific future extreme heat and drought scenarios, as well as quantification of the impact of extreme events under these scenarios on a city's electricity consumption and production. Findings from this research have the potential to significantly impact the way cities and electric utility operators manage electric grids under extreme heat and drought conditions.This project addresses vital links between climate modeling, consumption and production, and seeks to contribute to the following scientific advancements: 1) development of effective methods for generating refined future extreme heat and drought scenarios utilizing disparate sources of data of varying fidelity, such as historical observations; 2) development of a flexible electricity consumption model integrated with demand-side management strategies using bottom-up energy and demand modeling methods; 3) development of a data-driven stochastic optimization method for robust power-generation decision strategies that considers various sources of uncertainty, including weather and the consumption and production of electricity.