DOE modeling and analysis activities focus on reducing uncertainties and improving transparency in photovoltaics (PV) and concentrating solar power (CSP) performance modeling. The overall goal of this effort is to develop improved modeling data and algorithms to accurately predict module or system performance and energy yield for a given location. Energy production estimates generated by developers and independent engineering firms are a critical part of the package reviewed by investors. The estimates help investors evaluate the risk and economic metrics, such as levelized cost of energy (LCOE) and return on investment (ROI), associated with investing in large solar energy generation projects. Improving system modeling accuracy and risk assessments will improve bankability across all markets.

Through the SunShot Systems Integration efforts, DOE funds research and development (R&D) activities at national laboratories that increase accuracy and confidence in system performance predictions. These activities include:

  • Model algorithm development
  • Tool development and deployment
  • Model validation
  • Analyses of the effects of model inputs such as measured performance, reliability, and solar resource data on model output
  • Industry outreach and technical leadership.

National Laboratory R&D

National laboratory modeling and analysis R&D is being performed in the following areas:

PV System Modeling Algorithms and Tools for Reducing Uncertainty and Risk

The National Renewable Energy Laboratory (NREL), with funding from DOE, is developing PV system modeling algorithms and tools for reducing uncertainty and risk.

NREL will make robust models available to various audiences, thereby improving the industry characterization of risk and improving bankability across all markets (residential, commercial, and utility). The current work is divided into three parts:

  • Transferring R&D results to the solar analysis community via development of the successful DOE-sponsored System Advisor Model (SAM) tool
  • Validation of SAM, the PVWatts online system model, and related Web services to improve acceptance by the solar industry, gather valuable information about issues, and interact with a technical review committee of users and potential users
  • Improving simple system algorithms to better inform the risk and improve the bankability of residential systems, and providing technical updates to PVWatts.

Even with a cost-competitive LCOE, there are still barriers to acceptance and deployment that can be perceived, whether technical or market-based. To overcome these barriers, the deployment community needs to accurately understand risk and uncertainty. The communication of this risk via a trusted third-party mechanism allows utility-scale developers to make informed decisions.

Additionally, the SAM tool is particularly adept at the actual analysis that determines whether the SunShot goals are being met. With the ability to model costs, financing, and the various anticipated performance improvements, this tool can actually determine progress towards the multi-variant SunShot cost reduction goals, which are tied to system reliability, energy yield, and footprint.

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Increasing Prediction Accuracy and Confidence in PV System Performance

Sandia National Laboratories, with funding from DOE, is working to increase prediction accuracy and confidence in PV system performance.

Sandia's technical approach involves four parallel efforts to increase confidence in performance models and estimates, quantify sources and magnitude of uncertainties, increase transparency by clearly documenting the process and algorithms, and reduce uncertainties by improving models, methods, and data. These efforts are:

  • Collection and analysis of high-quality field system performance data to benchmark model accuracy and help identify modeling areas where improvements will lead to reduced uncertainties
  • Targeting PV system model enhancements, including development of model algorithms to address emerging system modeling gaps (e.g., array-scale temperature modeling and array modeling including mismatch)
  • Leading stakeholder engagement in PV performance modeling, focusing on improving the transparency and communication of best practices related to performance modeling processes; this includes leading the PV Performance Modeling Collaborative
  • Developing an integrated framework for analysis of technical risk (uncertainties) to develop a formal analysis framework that can be used to rigorously assess and rank all potential sources of technical risk for PV projects.

A critical part of ensuring that PV technologies become cost-competitive with fossil-fuel-based energy sources is the ability to accurately predict the amount of energy a PV system will produce in a given location. Sandia's work will reduce uncertainty (both real and perceived) and increase transparency in the PV performance modeling estimates that are used to demonstrate the value and feasibility of proposed PV projects.

Additional information on Sandia's PV modeling and analysis activities is available from Sandia.

Additional information on modeling and analysis related to grid integration of solar energy is available in the Modeling and Analysis section of the High Penetration Solar Portal.

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