How does stochasticity affect the efficiency of solar power plants? The applications of stochastic directional distance function approach

Increased solar power generation leads to greater complexity of power systems and significantly impacts energy security. It requires new theoretical models for efficiency assessment to characterize stochasticity of renewable resource inputs. These efficiency assessments provide analytical tools for policymakers in improving energy efficiency and combating climate change. Extending the axiomatic system of stochastic efficiency evaluation, this study proposes a new chance-constrained data envelopment analysis based on a directional distance function, to capture the statistical noise of volatile solar radiation. This method provides a practical measure of efficiency, and therefore models the performance of intermittent solar power generation within a joint production framework by means of a benchmarking analysis. This provides a robust analytical tool for energy economists and policymakers to assess the operational efficiency and cost-effectiveness of solar plants under high resource volatility. Its advantages and practicality are demonstrated by an empirical study of the power plants in Qinghai, China. Further analysis of geographical and seasonal variations indicates that environmental heterogeneity distorts the operational performance of solar power plants, and differences in the ability to adjust supply to demand are at the root of inefficiency gaps among plants.