基于制造商提供参数的11参数光伏模块功能模型识别

In light of the ongoing decline in photovoltaic (PV) generation costs and its growing competitiveness with retail electricity prices, accurately predicting PV performance is increasingly important. While manufacturers have typically rated PV modules at standard test conditions (STCs), their ratings are now being enhanced by reporting module data at low irradiance conditions (LICs) and nominal operating cell temperature (NOCT). Recently, an enhanced PV model was proposed Angulo et al., 2024, capable of reproducing the behavior of a PV module across a wide range of atmospheric conditions. Although the superiority of this model is thoroughly discussed in Angulo et al., 2024, the identification of its characterizing parameters from ratings provided by manufacturers is not addressed. This paper proposes a parameter identification methodology relying on STC, LIC, and NOCT ratings. The problem at hand involves solving a complex system of eleven nonlinear equations, and is approached by progressively reducing the search space and generating adjustment functions. The methodology is tested in an automated fashion over the entire California Energy Commission PV database, which currently contains 17 710 modules, achieving a convergence rate of 99.8%. The quality of the identified model is assessed by comparing energy predictions against experimental measurements, including state-of-the-art models available in the literature. Results indicate that the identified model reduces prediction errors by about 9% compared to the best competitive model.