AI 中文总结
针对电力与计算能力相互依存关系被低估的问题，开发ECIS量化其与关键基础设施依存关系，以数据中心为模型并结合电力约束等提出恢复优化策略，案例显示该策略能在电力短缺时提高系统恢复能力超20%。
AI 中文摘要
由于人工智能革命，电力和计算能力高度相互依存，且在正常和恢复运行期间这种依存关系被严重低估。本文开发了电力-计算相互依存系统（ECIS），首先量化了ECIS与其他关键基础设施之间的相互依存关系。将数据中心建模为将电力转化为计算能力的设施，然后提出了考虑电力约束、计算能力供应和基础设施相互依存关系的ECIS恢复优化策略。案例研究表明，该策略根据系统状态动态调整数据中心电力配额，在电力短缺期间将系统恢复能力提高了20%以上。
英文摘要
Electrical power and computing power are highly interdependent especially due to the AI revolution. This dependency is largely underestimated during normal and recovery operation. To address this issue, an Electricity-Computing Interdependency System (ECIS) is developed in this paper, which first quantifies the interdependencies between ECIS and other critical infrastructures. The data center (DC) is modeled as a facility that transforms electrical power into computing power, and a recovery optimization strategy for ECIS is then proposed considering electrical power constraints, computing power supply, and infrastructure interdependencies. The case study demonstrates that the proposed strategy dynamically adjusts the DC electrical power quota according to system states, improving system resilience by more than 20% during power shortage.
