Enhancing Photovoltaic Farm Capacity Estimation: A Comprehensive Analysis with a Novel Approach

Abstract

This research paper addresses the inaccuracies in the current methods for estimating the capacity value of photovoltaic (PV) plants, which rely heavily on large‐scale data and fail to represent the actual capacity value pattern accurately. The research conducts case studies in Belgium, Texas, and California to analyze the impact of different factors on capacity value. It proposes a new metric called the Marginal Moving‐Average Limited‐Hours (MMALH) Equivalent Load‐Carring Capability (ELCC) ‐ Based capacity value. The proposed metric reduces the dependence on hourly data and better represents capacity value. The results from real case studies validate the effectiveness of the new metric, highlighting its novelty and contribution to the assessment of capacity value in PV power systems. The study emphasizes the importance of accurately assessing the capacity value of PV compared to conventional units, considering environmental factors and system parameters. The study exposes the shortcomings in current metrics and advocates for the MMALH ELCC methodology as a more precise evaluation approach. The research suggests optimizing design, employing advanced tracking systems, enhancing maintenance practices, and ensuring effective grid integration to boost solar plant efficiency. Consistent monitoring and analysis of the utilization factor are vital for pinpointing improvement areas and augmenting productivity.