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 />
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