A Data Driven String Sizing Methodology for Utility-Scale Photovoltaic Systems
Abstract
This paper presents the Data-Driven String Sizing (DDSS) method for optimizing PV module string length in utility-scale systems. Unlike conventional conservative approaches, DDSS utilizes sub-hourly, site-specific meteorological data to refine cell temperature and open-circuit voltage estimates, enabling increased string size. The method incorporates thermal inertia modeling to capture worst-case irradiance scenarios, applies statistical analysis, and integrates safety factors to address uncertainties. Two case studies in distinct Brazilian climates (Jaguaruana and Florianópolis) assessed technical and economic outcomes based on one year of data. DDSS allowed for increased string length by a single module (~3.2%), leaving energy yield essentially unchanged but yielding measurable CAPEX savings – up to 0.84% – mainly from reduced racking and construction costs. An extended string-length scenario, considering a less conservative scenario with additional modules in series, improved CAPEX savings of up to 1.96%. While benefits depend on site conditions and require high-quality on-site data, DDSS offers a replicable, data-driven approach with potential to enhance the economic performance of utility-scale PV plants.
Keywords
Photovoltaic systems, String sizing, Data-driven design, Thermal modeling, Module temperature, Utility-scale solar, Economic assessment, Levelized cost of energy, Voltage optimization