Photovoltaic performance models: an evaluation with actual field data

ABSTRACT Prediction of energy production is crucial to the design and installation of the building integrated photovoltaic systems. This prediction should be attainable based on the commonly available parameters such as system size, orientation and tilt angle. Seve ral commercially available as well as free downloadable software tools exist to predict energy production. Six software models have been evaluated in this study and they are: PV Watts, PVsyst, MAUI, Clean Power Estimator, Solar Advisor Model (SAM) and RETScreen. This evaluation has been done by comparing the monthly, seasonaly and annually predicted data with the actual, field data obtained over a year period on a large number of residential PV systems ranging between 2 and 3 kW dc . All the systems are located in Arizona, within the Phoenix metropolitan area which lies at latitude 33° North, and longitude 112 West, and are all connected to the electrical grid. Keywords: BIPV, models, energy, systems, prediction 1. INTRODUCTION From consumer and system integrator perspectives, it is vital to be able to reasonably predict the energy production of building integrated photovoltaic (BIPV) systems. This prediction should be attainable based on available or desired system sizing parameters such as model of the modules, performance rating, orientation, and derate factor (AC-DC loss). Seve ral-commercially available as well as free downloadable prediction software tools exist. These software tools can be used to calculate the energy production of a desired system by simply computing the system sizing parameters. A few of the most commonly used system sizing tools are cons idered in this study. This study analyzes six of the most commonly used models for predicting the performance of grid-connected, residential PV systems. The actual performance data collected, by Salt River Project (SRP), from 23 residential PV systems in Arizona were compared with projected estimates from six software models. An annual as well as monthly and season al energy analyses have been carried out. 2. METHODOLOGY Six software models are evaluated in this study, namely; PVWATTS, PVsyst, MAUI, California Energy Commissions (CEC) Clean Power Estimator, National Renewable Energy Laboratorys (NREL) SAM, and Natural Resources Canadas (NRCan) RETScreen. A quick overview of these models is presented below. PVwatts is a web based software model that calculates electrical energy produced by a grid-connected photovoltaic (PV) system. The PVwatts model was developed by the Renewable Resource Data Center (RReDC), which is supported by the National Center for Photovoltaics (RReDC). This model uses a 30 year average daily weather data to determine the daily amount of solar energy (insolation), and uses it to calculate the amount of energy a system will produce based on the systems configuration [1]. PVsyst software is a European based PV system predictor developed by the University of Geneva for the European Center of Energy. This PC software package is suitable for studying simulating and analyzing PV systems. The software is suitabl e for both stand-alone and grid-conn ected PV systems. This software is also useful for analyzing solar-thermal systems and applications [2].