Analysis of Renewable Energy Adoption Efficiencies Under Uncertainty Across Electricity Markets in the U.S.

Abstract

This study evaluates the efficiencies of renewable energy adoption in different electricity markets in the U.S. Particularly, the study investigates how uncertainties in Renewable Portfolio Standards (RPS) mandates influence the adoption of renewable energy technologies in the markets. A stochastic Data Envelopment Analysis (DEA) method was employed by combing the traditional DEA approach with a Monte-Carlo simulation technique. The study found the regions covered by markets including SPP, MISO and NE-ISO to be DEA efficient. Compared to a deterministic model, the findings reveal further insights on the performance of Decision-Making Units (DMUs) across different scenarios. In addition, the stochastic model is also better able to discriminate among DMUs and more accurately capture the performance of these units over time. For each unit, the model provides a distribution of efficiencies and for those units operating below the efficient frontier, it also provides the average renewable energy capacity addition targets to be attained.