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Electricity Price Models in the Context of Increasing Renewable Energy Generation

Felipe dos Santos Couto, f.couto@columbia.edu

SEAS ’22, Mechanical Engineering, Columbia University

Supervising Faculty, Sponsor, and Location of Research

Dr. Bolun Xu, Undergraduate Research Involvement Program, The Earth Institute,

Columbia University

Abstract

Renewable energy is no longer an interesting possibility for the future but rather an

urgent demand in the present to fight the climate crisis. Nevertheless, academia, industry,

and public entities still face many challenges to increase penetration of renewable sources

on the world energy mix. In this context, storing energy has risen as a key alternative and

numerous storage solutions have been developed. The purpose of this study is to better

understand how large-scale energy storage systems (ESS) impact electricity prices. We

developed interpretable machine learning models and analyzed how supply and demand

attributes correlate with price fluctuations – especially, price spikes, which are a major

sign of market inefficiency. Optimal Regression Trees and Multiple Linear Regressions

were applied to the Southwest Power Pool market data, shedding some light upon the

attributes’ sensitivities. We then utilized the sensitivities to estimate price reductions due

to energy storage. Results showed a potential reduction, on average, of 19.0% on price

spikes. Further studies shall continue to investigate the effects grid-scale ESS on prices.

By doing so, we hope to corroborate with the expansion of renewable energy generation

and ESS on the grid.

Keywords

renewable energy, energy storage, interpretable machine learning, electricity price

models

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