Improvement and validation of a model for photovoltaic array performance

Abstract

The use of photovoltaic cells has increased in the last few decades as their manufacturing cost has decreased and as people have become more concerned about energy use. Designers need a reliable tool to predict energy production resulting from building integrated photovoltaic panels in order to make a sound decision on whether or not to incorporate this technology into a building. A few models that predict energy production have been developed, but they require a large amount of input data that are normally not available during the design phase. The 5-Parameter model investigated in this research uses the data provided by the manufacturers and semi-empirical correlation equations to predict the energy production for specified cell parameters and operating conditions. Data were obtained from a building integrated photovoltaic facility at the National Institute of Standards and Technology (NIST), where four different cell technologies were tested. These data were used to verify the accuracy of the energy production predictions, therefore validating the model suggested in this study. The model was analyzed for these four different cell technologies and compared with different existing models, showing acceptable and sometimes even better results than the existing models that require more input information. Because the model only requires a iv small amount of input data available from the manufacturer, it provides a valuable tool for energy prediction.v

Three-Phase Optimal Power Flow for Study of PV Plant Distributed Impact on Distribution Systems

“…However, a more realistic view of the operating conditions of the system is obtained if the PV generation is calculated from measurements of solar irradiation and temperature. For this, the PV module can be represented by the five-parameter model [12,13]. This representation was used in the power flow problem [14] and, more recently, in the single-phase optimal power flow problem [15].…”

An accurate method for the PV model identification based on a genetic algorithm and the interior-point method

“…In the first group, the authors in [17,18,19,20,21] presented analytical methods to calculate the parameters. The identification method proposed in [17] attempts to approximate and solve analytically the equation of the PV as a polynomial of the first order.…”

“…Even though a simple parameter estimation method is demonstrated, the method does not provide enough accuracy. The works of [18,19,20,21], however, introduced a system of five equations to resolve the five model parameters. Three of these equations are extracted from three remarkable points [13], i.e., the short-and the open-circuit points coupled with the MPP.…”

“…While the forth equation is constructed based on the fact that the derivative of the power at the MPP is zero, the fifth equation is more challenging. The authors in [18] employed an approximation for the temperature coefficient as the fifth equation, however, the others adopt the equation introduced in [19] as a simplified approximation of the shunt resistor. Although these analytical methods are straightforward and only need the nominal values from datasheets, they still need sophisticated numerical or even optimization methods to solve the resulting nonlinear and transcendental system of equations.…”

“…The equations rely on several empirical parameters, which need to be extracted for each type of the PV module. The national institute of standards and technology (NIST), USA [27] provides these empirical parameters for different PV module technologies [18]. The procedure needs to be repeated for any new PV module type.…”