Empowering New York City: Leveraging Surplus Renewable Energy for Community Benefits
Christopher Ann *
Computer Science Divisions, STEM Science Center, 111 Charlotte Place Ste#100, Englewood Cliffs, NJ 07632, United States.
*Author to whom correspondence should be addressed.
Abstract
The rapid advancement of artificial intelligence (AI) has significantly increased the energy demands of data centers, placing growing pressure on urban power grids. In parallel, New York City has experienced a rise in renewable energy production, yet mismatches between generation and consumption have resulted in surplus electricity being frequently underutilized. Simultaneously, the phased retirement of fossil fuel power plants has contributed to power supply instability, disproportionately impacting low-income communities and public services. Addressing these converging challenges, this study proposes a data-driven credit refund system that redistributes surplus renewable energy based on borough-level electricity consumption and population metrics.
To develop and validate this system, three years of borough-level electricity consumption, renewable energy production, and weather data (2021–2023) were collected and analyzed. Preprocessing steps included machine learning–based imputation of missing values and outlier detection using techniques such as K-Nearest Neighbors and the Isolation Forest algorithm. Random Forest regression was employed to model surplus electricity as a function of environmental variables, capturing non-linear seasonal relationships.
Hydroelectric power was found to be the most stable energy source year-round, while solar and wind exhibited strong seasonal patterns—solar peaking in summer and wind in winter—demonstrating a significant inverse correlation between temperature and wind speed. These dynamics underscore the importance of precise weather forecasting and the role of environmental variability in optimizing grid efficiency.
Simulation results indicate that the proposed credit refund system could equitably allocate economic benefits to residents across boroughs, with the Bronx and Manhattan receiving the highest credits due to their population density and energy use. This model offers a scalable, sustainable solution for utilizing surplus electricity, improving grid efficiency, and supporting energy equity. The findings present a replicable framework for other cities seeking to integrate renewable energy surpluses into community-focused energy policy.
Keywords: Renewable energy, hydroelectric power, optimizing grid efficiency, environmental variability