Forecasting distributed energy resources adoption for power systems

Nicholas Willems; Ashok Sekar; Benjamin Sigrin; Varun Rai

doi:10.1016/j.isci.2022.104381

Forecasting distributed energy resources adoption for power systems

iScience. 2022 May 10;25(6):104381. doi: 10.1016/j.isci.2022.104381. eCollection 2022 Jun 17.

Authors

Nicholas Willems¹, Ashok Sekar², Benjamin Sigrin², Varun Rai^{1

3}

Affiliations

¹ Walker Department of Mechanical Engineering, The University of Texas at Austin, 204 E. Dean Keeton St., Austin, TX 78712, USA.
² National Renewable Energy Laboratory, 15013 Denver W Pkwy., Golden, CO 80401, USA.
³ LBJ School of Public Affairs, The University of Texas at Austin, 2300 Red River St E-2700, Austin, TX 78712, USA.

Abstract

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity-the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

Keywords: Energy Modeling; Energy Systems; Energy management; Energy policy; Energy resources.