随机容量认证：在天气不确定性下激励资源充足性

中文摘要 AI 辅助

高比例可变可再生能源带来了重大的资源充足性挑战，尤其是当天气驱动的不确定性影响可再生能源可用性、电力需求和热力发电机的有效容量时。现有容量信用认证方法往往忽视这些相关的天气影响，这可能会高估固定容量、扭曲长期投资决策并削弱有价格上限的电力市场中的可靠性结果。本文提出了一个用于容量认证的两阶段随机优化框架，该框架明确捕捉了风能、太阳能和温度相关的热力降额中的不确定性。利用五年的ERCOT需求和可再生能源可用性数据，我们将所提出的随机容量信用方法与确定性和平均认证方法进行了比较，并量化了替代认证方法对可靠性、投资激励和天气信息价值的影响。结果表明，纳入天气不确定性会产生更具信息性的容量信用和更可靠的投资信号。相比之下，确定性和平均方法会扭曲资源扩张决策并产生实质上更差的可靠性结果。这些发现证明了在容量认证和长期资源充足性规划中明确考虑天气不确定性的重要性。

英文摘要

High penetrations of variable renewable energy introduce significant resource adequacy challenges, particularly when weather-driven uncertainty affects renewable availability, electricity demand, and the effective capacity of thermal generators. Existing capacity credit accreditation methods often neglect these correlated weather effects, which may overstate firm capacity, distort long-term investment decisions, and weaken reliability outcomes in electricity market with price caps. This paper proposes a two-stage stochastic optimization framework for capacity accreditation that explicitly captures uncertainty in wind, solar, and temperature-dependent thermal derating. Using five years of ERCOT demand and renewable availability data, we compare the proposed stochastic capacity credit method with deterministic and average accreditation approaches, and quantify the impact of alternative accreditation methods on reliability, investment incentives, and the value of weather information. The results show that incorporating weather uncertainty yields more informative capacity credits and more reliable investment signals. In contrast, deterministic and averaged approaches can distort resource expansion decisions and produce materially worse reliability outcomes. These findings demonstrate the importance of explicitly accounting for weather uncertainty in capacity accreditation and long-term resource adequacy planning.

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10 pages, 4 figures