Power grids are moving towards 100% renewable energy source bulk power grids, and the overall dynamics of power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to ensure grid reliability. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning-based analysis framework to deconstruct the primary drivers for price spike events in modern electricity markets with high renewable energy. The outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data; however, in this paper, it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England (ISO-NE).

翻译：电网正朝着100%可再生能源大规模电网方向发展，电力系统运行与电力市场的整体动态正在发生变革。电力市场不仅追求经济调度资源，还需考虑可再生能源弃电、输电拥堵缓解和储能优化等多种可调控手段，以确保电网可靠性。这使得电力市场价格形成机制变得极为复杂。传统的根因分析与统计方法已无法适用于分析高比例可变可再生能源（VRE）现代电网及市场中价格形成的主要驱动因素。本文提出一种基于机器学习的分析框架，用于解构高可再生能源渗透率现代电力市场中价格尖峰事件的主要驱动因素。该研究成果可应用于市场设计、可再生能源调度与弃电管理、运行优化及网络安全等关键领域。本框架可适用于任何独立系统运营商（ISO）或市场数据，但本文将其应用于加州独立系统运营商（CAISO）与新英格兰独立系统运营商（ISO-NE）的公开数据集。