Christopher P. Cameron

Solar Advisor Model User Guide for Version 2.0

The Solar Advisor Model (SAM) provides a consistent framework for analyzing and comparing power system costs and performance across the range of solar technologies and markets, from photovoltaic systems for residential and commercial markets to concentrating solar power and large photovoltaic systems for utility markets. This manual describes Version 2.0 of the software, which can model photovoltaic and concentrating solar power technologies for electric applications for several markets. The current version of the Solar Advisor Model does not model solar heating and lighting technologies.

Comparison of PV system performance-model predictions with measured PV system performance

The U.S. Department of Energy has supported development of the Solar Advisor Model (SAM) to provide a common platform for evaluation of the solar energy technologies being developed with the support of the Department. This report describes a detailed comparison of performance-model calculations within SAM to actual measured PV system performance in order to evaluate the ability of the models to accurately predict PV system energy production. This was accomplished by using measured meteorological and irradiance data as an input to the models, and then comparing model predictions of solar and PV system parameters to measured values from co-located PV arrays. The submodels within SAM which were examined include four radiation models, three module performance models, and an inverter model. The PVWATTS and PVMod models were also evaluated.

The levelized cost of energy for distributed PV: A parametric study

The maturation of distributed solar PV as an energy source requires that the technology no longer compete on module efficiency and manufacturing cost (

A standardized approach to PV system performance model validation

/Wp) alone. Solar PV must yield sufficient energy (kWh) at a competitive cost (¢/kWh) to justify its system investment and ongoing maintenance costs. These metrics vary as a function of system design and interactions between parameters, such as efficiency and area-related installation costs. The calculation of levelized cost of energy includes energy production and costs throughout the life of the system. The life of the system and its components, the rate at which performance degrades, and operation and maintenance requirements all affect the cost of energy. Cost of energy is also affected by project financing and incentives. In this paper, the impact of changes in parameters such as efficiency and in assumptions about operating and maintenance costs, degradation rate and system life, system design, and financing will be examined in the context of levelized cost of energy.

Performance Model Assessment for Multi-Junction Concentrating Photovoltaic Systems

PV performance models are used to predict how much energy a PV system will produce at a given location and subject to prescribed weather conditions. These models are commonly used by project developers to choose between module technologies and array designs (e.g., fixed tilt vs. tracking) for a given site or to choose between different geographic locations, and are used by the financial community to establish project viability. Available models can differ significantly in their underlying mathematical formulations and assumptions and in the options available to the analyst for setting up a simulation. Some models lack complete documentation and transparency, which can result in confusion on how to properly set up, run, and document a simulation. Furthermore, the quality and associated uncertainty of the available data upon which these models rely (e.g., irradiance, module parameters, etc.) is often quite variable and frequently undefined. For these reasons, many project developers and other industry users of these simulation tools have expressed concerns related to the confidence they place in PV performance model results. To address this problem, we propose a standardized method for the validation of PV system-level performance models and a set of guidelines for setting up these models and reporting results. This paper describes the basic elements for a standardized model validation process adapted especially for PV performance models, suggests a framework to implement the process, and presents an example of its application to a number of available PV performance models.

Quantifying the effects of averaging and sampling rates on PV system and weather data

Four approaches to modeling multi‐junction concentrating photovoltaic system performance are assessed by comparing modeled performance to measured performance. Measured weather, irradiance, and system performance data were collected on two systems over a one month period. Residual analysis is used to assess the models and to identify opportunities for model improvement.

High heat flux engineering in solar energy applications

When modeling photovoltaic (PV) system performance data, modelers typically reduce the amount of data analyzed by reducing the sampling frequency below the maximum sampling frequency of their instruments (under-sampling), averaging a number of samples together, or a combination of these two methods. A sampling frequency which is too low may not provide enough fidelity to accurately model system performance, while a sampling frequency which is too high may provide unnecessarily high data fidelity and increase file size and processing complexity. This paper strives to quantify the errors caused by reduced sampling and averaging frequencies through the comparison of modeled high temporal resolution weather data and low resolution weather data.

Development of a Technology Roadmap for the Energy and Water Nexus

Solar thermal energy systems can produce heat fluxes in excess of 10,000 kW/m2. This paper provides an introduction to the solar concentrators that produce high heat flux, the receivers that convert the flux into usable thermal energy, and the instrumentation systems used to measure flux in the solar environment. References are incorporated to direct the reader to detailed technical information.

PV Performance Modeling Workshop Summary Report

Energy and water are critical resources that are inextricably and reciprocally linked. The production of energy requires large volumes of water, and the treatment and distribution of water depends upon readily available, low-cost energy. For example, electricity production from thermoelectric power plants can use ∼140,000 million gallons of water per day for cooling—accounting for 39% of all freshwater withdrawals in the nation, second only to agriculture in the United States (Figure 1). Significant amounts of water are also needed for hydropower, extraction/refining of minerals for energy, and bio-fuel production. Electrical energy, on the other hand, is needed for water treatment (e.g., desalination, wastewater), pumping, and distribution. The amount of electricity used in water and wastewater industries is equivalent to the amount used in chemical, petroleum refining, and paper industries. These interdependencies, coupled with increasing demands for energy and diminishing availability of freshwater supplies, pose significant challenges to ensure the sustainability of these two critical resources. Examples of the interrelationships between energy and water use are shown in Figure 2.