Reliability Analysis of Renewable Grid Transition in New York Using Data-Driven Models

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Under the New York State Climate Leadership and Community Protection Act (CLCPA) , the State of New York has set ambitious targets to eliminate 100% of the climate pollution caused by humans. This includes a plan to source 70% of its electricity from renewable energy by 2030 and to achieve a 100% zero-emission electricity sector by 2040. However, an increase in reliability on renewable energy can lead to energy reliability issues and price hikes. Price spikes hurt the vulnerable in society, who had little contribution to the carbon emission problem to begin with. This project explores the drivers behind electricity price spikes using NYISO data and predictive modeling techniques—regression, decision trees, and ensemble methods. The findings reveal the complexity of forecasting price spikes and outline steps for building more robust models in future energy systems.