Uncertainty characterization for generation adequacy assessments – Including an application to the recent European energy crisis
利用丰富数据库量化可再生能源发电和电力需求的随机波动，通过动态多区域蒙特卡洛模拟评估欧洲核心国家在2022/2023年冬季的发电充裕度，发现多国面临风险但互联电网显著降低了风险。
The transition of the energy system amplifies the volatility and inherent uncertainties of electricity generation and consumption due to stochastic variations. Consequently, policymakers and system planners must model these uncertainties to assess whether future generation capacity is sufficient to meet electricity demand and anticipate potential security risks. In this paper, we use a rich empirical database to quantify stochastic variations of power generation by renewable energy sources (RES) and electricity demand for assessing generation adequacy in a dynamic multi-regional Monte Carlo simulation while considering spatio-temporal dependencies. In a case study , we explore the role of thermal generation availability and cross-regional exchanges in core European countries during the winter of 2022/2023. We assess generation adequacy under both typical stochastic variations and specific stress scenarios, such as reduced nuclear availability in France and restricted Russian gas supplies. Thereby, we derive assessments of expected energy not served (EENS) and loss of load expectation (LOLE) based on high-dimensional Monte Carlo simulations. The results show that generation adequacy was at risk in several core European countries based on the data available at the end of September 2022. Yet, the European interconnected power grid contributed to a substantial risk reduction.