A Machine Learning Framework to Deconstruct the Primary Drivers for
  Electricity Market Price Events
Milan Jain Xueqing Sun Sohom Datta Abhishek Somani
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).