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Undergraduate Research Showcase

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Electricity Price Models in the Context of Increasing Renewable Energy Generation<br />

Felipe dos Santos Couto, f.couto@columbia.edu<br />

SEAS ’22, Mechanical Engineering, Columbia University<br />

Supervising Faculty, Sponsor, and Location of <strong>Research</strong><br />

Dr. Bolun Xu, <strong>Undergraduate</strong> <strong>Research</strong> Involvement Program, The Earth Institute,<br />

Columbia University<br />

Abstract<br />

Renewable energy is no longer an interesting possibility for the future but rather an<br />

urgent demand in the present to fight the climate crisis. Nevertheless, academia, industry,<br />

and public entities still face many challenges to increase penetration of renewable sources<br />

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

numerous storage solutions have been developed. The purpose of this study is to better<br />

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

developed interpretable machine learning models and analyzed how supply and demand<br />

attributes correlate with price fluctuations – especially, price spikes, which are a major<br />

sign of market inefficiency. Optimal Regression Trees and Multiple Linear Regressions<br />

were applied to the Southwest Power Pool market data, shedding some light upon the<br />

attributes’ sensitivities. We then utilized the sensitivities to estimate price reductions due<br />

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

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

By doing so, we hope to corroborate with the expansion of renewable energy generation<br />

and ESS on the grid.<br />

Keywords<br />

renewable energy, energy storage, interpretable machine learning, electricity price<br />

models<br />

6

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