This paper presents an innovative approach to Extreme Value Analysis (EVA) by introducing the Extreme Value Dynamic Benchmarking Method (EVDBM). EVDBM integrates extreme value theory to detect extreme events and is coupled with the novel Dynamic Identification of Significant Correlation (DISC)-Thresholding algorithm, which enhances the analysis of key variables under extreme conditions. By integrating return values predicted through EVA into the benchmarking scores, we are able to transform these scores to reflect anticipated conditions more accurately. This provides a more precise picture of how each case is projected to unfold under extreme conditions. As a result, the adjusted scores offer a forward-looking perspective, highlighting potential vulnerabilities and resilience factors for each case in a way that static historical data alone cannot capture. By incorporating both historical and probabilistic elements, the EVDBM algorithm provides a comprehensive benchmarking framework that is adaptable to a range of scenarios and contexts. The methodology is applied to real PV data, revealing critical low - production scenarios and significant correlations between variables, which aid in risk management, infrastructure design, and long-term planning, while also allowing for the comparison of different production plants. The flexibility of EVDBM suggests its potential for broader applications in other sectors where decision-making sensitivity is crucial, offering valuable insights to improve outcomes.

翻译：本文提出了一种创新的极值分析（EVA）方法，引入了极值动态基准测试方法（EVDBM）。EVDBM整合了极值理论以检测极端事件，并结合了新颖的动态显著相关性识别（DISC）-阈值算法，该算法增强了对极端条件下关键变量的分析。通过将EVA预测的回报值整合到基准评分中，我们能够调整这些评分以更准确地反映预期条件。这为每个案例在极端条件下的预期发展提供了更精确的描绘。因此，调整后的评分提供了前瞻性的视角，以静态历史数据无法捕捉的方式，突出了每个案例的潜在脆弱性和韧性因素。通过结合历史和概率要素，EVDBM算法提供了一个全面的基准测试框架，能够适应各种场景和背景。该方法应用于实际光伏数据，揭示了关键的低产量场景以及变量之间的显著相关性，有助于风险管理、基础设施设计和长期规划，同时也允许对不同生产工厂进行比较。EVDBM的灵活性表明其在其他决策敏感性至关重要的领域具有更广泛的应用潜力，为改善结果提供了宝贵的见解。