发表机构
University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文开发ICP-AI框架，在规定预算下最小化AI数据中心的电网互连容量，结合PV、BESS资源与灵活工作负载调度，通过实验验证其可有效减少互连容量，为项目规划提供支持。
AI 中文摘要
保障电网互连容量已成为AI数据中心项目的瓶颈，其耗时甚至超过数据中心设施本身的建设时间。这种不匹配可能导致部署延迟数年，因此早期互连规划至关重要。本文从数据中心开发者的角度开发了ICP-AI互连容量规划框架，该框架在规定的现场投资预算下最小化电网输入容量，同时协同确定光伏（PV）和电池储能系统（BESS）的资源规模，并调度具有截止时间约束的工作负载灵活性。第二个优化步骤是在达到最小电网容量的解决方案中，选择投资最少的PV-BESS组合。该框架通过跨不同时间假设、负荷形状、灵活负荷比例和延迟窗口的月度综合压力剖面进行评估。结果表明，互连容量的减少在很大程度上取决于规划环境：在1亿美元预算下，高负荷因子基准场景下减少约6%，在月度平均太阳能可用性下超过10%，对于更具日变化性的负荷则达到13.3%。在1000万美元预算下，5%的灵活负荷搭配1小时的工作负载延迟窗口，可将BESS容量从15.30 MWh降至4.87 MWh，同时将容量减少率从4.43%提升至4.84%。为测试对时间压缩的敏感性，模型还在完整的8760小时时间序列上求解，其保留了主要的容量和灵活性趋势。总体而言，ICP-AI量化了工作负载灵活性的互连容量和基础设施替代价值，提供了投资-互连前沿，以支持电网受限环境中的资本配置和项目早期规划。
英文摘要
Securing grid interconnection capacity has become a bottleneck for AI data center projects and can take longer than constructing the facilities themselves. This mismatch can delay deployment for years, making early interconnection planning essential. This paper develops ICP-AI, an interconnection capacity planning framework from a data center developer's perspective. The framework minimizes grid import capacity under a prescribed onsite investment budget while jointly sizing photovoltaic (PV) and battery energy storage system (BESS) resources and scheduling deadline constrained workload flexibility. A secondary refinement fixes the minimum grid capacity and selects the minimum-investment PV-BESS portfolio among solutions that achieve that capacity. The framework is evaluated using monthly composite stress profiles across varying temporal assumptions, load shapes, flexible load fractions, and deferral windows. Results show that interconnection capacity reduction depends strongly on the planning environment: at a $100M budget, it is about 6% for the high load factor baseline, exceeds 10% under monthly average solar availability, and reaches 13.3% for a more diurnal load. At a $10M budget, 5% flexible load with a 1 h workload deferral window reduces BESS capacity from 15.30 to 4.87 MWh while increasing capacity reduction from 4.43% to 4.84%. To test sensitivity to temporal compression, the model is also solved over the full 8,760 h chronology, which preserves the main capacity and flexibility trends. Overall, ICP-AI quantifies the interconnection capacity and infrastructure substitution value of workload flexibility, providing an investment-interconnection frontier to support capital allocation and early project planning in constrained grid environments.
