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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multilevel Cybersecurity for Photovoltaic Systems

The motivation behind this project is to protect critical infrastructure in electric power generation pertaining to solar photovoltaic (PV) systems. This growing renewable energy resource is becoming a more vital part of the nation’s energy portfolio, particularly since it has achieved grid-parity to existing generation methods in terms of cost. It is thus vital that steps be taken to ensure the cybersecurity of these assets. The project goal was to devise a multilevel cybersecurity solution to address PV security gaps at the inverter and system levels, and field test the solution under the supervision and review of a US-based solar inverter manufacturer and PV installer/operator. A two-level cyberattack defense approach was formulated whereby the first level, the solar inverter level, hardens individual devices and achieves a deeply cyber-secure inverter. The inverter level security involves a multi-layer defense-in-depth approach for securing the inverter while also providing data for the system level algorithms. The second level, the system level, addresses intrusion detection and restoration involving an ensemble of inverters and relevant systems.

14 SOLAR ENERGY↗
DOE OSTI2020

Selection of optimal wavelengths for optical soiling modelling and detection in photovoltaic modules

Soiling impacts the photovoltaic (PV) module performance by decreasing the amount of light reaching the photovoltaic cells and by changing their external spectral response. Currently, the soiling monitoring market is moving toward optical sensors that measure transmittance or reflectance, rather than directly measuring the impact of soiling on the performance of photovoltaic modules. These sensors, which use a single optical measurement, are not able to correct the soiling losses that depend on the solar irradiance spectra and on the spectral response of the monitored PV material. This work investigates methods that can improve the optical detection of soiling by extracting the full soiling spectrum profiles using only two or three monochromatic measurements. Spectral transmittance data, measured with a spectrophotometer and collected during a 46-week experimental soiling study carried out in Jaén, Spain, was analysed in this work. The use of a spectral profile for the hemispherical transmittance of soiled PV glass is found to significantly improve the soiling detection, returning the lowest errors independently of the PV materials and irradiance conditions. In addition, this work shows that it is also possible to select the measurement wavelengths to minimize the soiling loss detection error depending on the monitored PV semiconductor material (silicon, CdTe, a-Si, CIGS and a representative perovskite). The approaches discussed in this work are also found to be more robust to potential measurement errors compared to single wavelength measurement techniques.

14 SOLAR ENERGY↗
DOE OSTI2024

Convolution Neural Network for Fault Identification in Distribution Feeder with High Penetration Solar PV

Identification and zonal classification of the faults is a decisive factor in the relay’s decision to trip or not. Different types of fault like three-phase, line-to-line-to-ground and single-line-to-ground can occur at various locations in the feeder. These faults are seen as the variation in the instantaneous values of three-phase voltages and currents, i.e., waveforms, that are measured at the relay location. The objective of this work is to develop a machine learning model that can identify a fault and classify it to various protection zones based on measured waveforms. In this work, a data-driven relay based on Convolutional Neural Network (CNN) is proposed for fault identification in distribution feeders with high penetration solar PV. The proposed CNN model takes local current and voltage waveforms as input and classify it into fault, no-fault or a capacitor switching. Further, the CNN also attempts to identify fault zones based on the images of waveforms. The overall testing accuracy of the trained model exceeds 95%.

Ramesh, Meghana↗
DOE OSTI2025

Q1-2024 Solar Cost Benchmarks

Each year, the U.S. Department of Energy’s (DOE) Solar Energy Technologies Office (SETO) and its national laboratory partners develop cost benchmarks for U.S. solar photovoltaic (PV) systems. These benchmarks track progress toward reducing solar costs and guide R&D priorities. Unlike typical studies that report only $/W, SETO uses intrinsic units (e.g., $/m² for mounting structures) to better capture how technology improvements such as module efficiency would impact system costs. This allows flexible modeling where inputs can vary significantly to assess cost sensitivity. Costs are reported in two ways: Minimum Sustainable Price (MSP): Long term, financially viable price under stable market conditions. Modeled Market Price (MMP): Actual market price, influenced by short term distortions such as tariffs or subsidies. Three national labs collect cost data from industry stakeholders, ensuring no duplication in outreach to stakeholders. Data reflects real transactions (primarily from Q1) and is weighted based on the number of sources per cost element. The PV System Cost Model (PVSCM) divides total installed system cost into eight categories: 1. Module (PV) 2. Inverter 3. Energy Storage System (ESS) 4. Structural BOS (SBOS) 5. Electrical BOS (EBOS) 6. Fieldwork 7. Office work 8. Other (developer/EPC costs) The first five are hardware costs, while the last three are soft costs. Each category includes fixed and variable cost components, where “size” depends on context (e.g., manufacturing capacity for modules vs. system capacity for installation costs). Variable costs are expressed using appropriate intrinsic units. The model reflects the owner’s upfront overnight capital cost, excluding tax credits. Tariffs and subsidies are treated as temporary market distortions affecting MMP but not MSP. PVSCM is implemented in Excel, where cost elements are aggregated into total system cost. Additional sheets handle unit conversions and operation & maintenance (O&M), with O&M costs levelized over the system’s lifetime.

14 SOLAR ENERGY↗
DOE OSTI2022

PVAnalytics: A Python Package for Automated Processing of Solar Time Series Data

Multiple publicly available software packages exist that analyze solar time series data, including RdTools and Solar Data Tools, among others. Several of these packages contain their own unique quality assurance (QA) and feature recognition algorithms. The python PVAnalytics package was developed to offer an internally consistent source for these analysis tools, making it easier for the end user to deploy these routines on his or her solar data. The PVAnalytics package currently contains routines for outlier detection, inverter clipping detection, irradiance and temperature checks, orientation checks, and data shift detection, among other functions. These functions have been aggregated from various sources including Solar Forecast Arbiter, RdTools, and the QA process developed by NREL's PV Fleets Initiative. We are continuously adding new functionality to the package, including documentation, examples and algorithms. By bundling QA functionality into a single software package, we hope to make PVAnalytics a comprehensive software library to support analysis of solar metadata and time series data.

data cleaning↗
DOE OSTI2023

Annual Technology Baseline: The 2023 Electricity Update

Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' renewable energy analysts. The ATB has been reviewed by experts and it includes the following electricity generation and storage technologies: land-based wind, offshore wind, distributed wind, utility-scale solar photovoltaics (PV), commercial-scale solar PV, residential-scale solar PV, concentrating solar power, geothermal power, hydropower, utility-scale battery storage, commercial battery storage, residential battery storage, pumped storage hydropower, coal, and natural gas. EIA data for nuclear and conventional biopower are included for reference. This webinar presentation introduces the 2023 update to the ATB Electricity data and documentation.

capacity factor↗
DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗
DOE OSTI2022

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Abstract Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Distributed Wind Energy Futures Study

To better understand distributed wind opportunities in the United States, researchers at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory explored cost, performance, and valuation benchmarks necessary for distributed wind to achieve widespread commercial viability in the United States by 2035. Building on DOE's prior benchmark report "Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects", the new Distributed Wind Futures Study employs higher-resolution data and new modeling techniques to highlight geographic trends in technical and economic potential and to compare the cost and performance of distribution-connected, megawatt-scale wind applications (referred to as "front-of-the-meter" applications) and behind-the-meter systems, thereby informing the case for investment. The report identifies the best locations and sectors for behind-the-meter and front-of-the-meter applications and provides data to inform the trade-offs consumers face when deciding between distributed wind and solar photovoltaic (PV).

17 WIND ENERGY↗
DOE OSTI2022

Distributed Wind Energy Futures Study [Slides]

To better understand distributed wind opportunities in the United States, researchers at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory explored cost, performance, and valuation benchmarks necessary for distributed wind to achieve widespread commercial viability in the United States by 2035. Building on DOE's prior benchmark report "Assessing the Future of Distributed Wind: Opportunities for Behind-the-Meter Projects", the new Distributed Wind Futures Study employs higher-resolution data and new modeling techniques to highlight geographic trends in technical and economic potential and to compare the cost and performance of distribution-connected, megawatt-scale wind applications (referred to as "front-of-the-meter" applications) and behind-the-meter systems, thereby informing the case for investment. The report identifies the best locations and sectors for behind-the-meter and front-of-the-meter applications and provides data to inform the trade-offs consumers face when deciding between distributed wind and solar photovoltaic (PV).

17 WIND ENERGY↗
DOE OSTI2021

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Triple Effect Economics: How Do Electric Vehicles, Solar Photovoltaics, and Work-from-Home Transform Household Electricity Cost?

Solutions for deep decarbonization need to be affordable as well as sustainable to garner widespread adoption. The past two decades have seen the introduction of new technologies and paradigms such as electric vehicles (EVs), solar photovoltaics (PVs), and increased work-from-home (WFH), which impact a household's overall energy consumption and cost in different ways. However, much attention has not been paid to explore the joint impact these technologies have on a household's electricity cost burden. Leveraging the 2020 Residential Energy Consumption Survey (RECS), this study presents an integrated model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. National level RECS data revealed that, compared to the baseline (i.e., households that did not own any of these technologies), households that own EVs, PVs, and engage in WFH observed a 13.5% decline in their electricity bills, despite a 25% increase in electricity consumption. The model results indicated that bundled adoption of EVs-PVs reduces electricity costs more significantly for a household, compared to the increased costs imposed by more household members staying at home. This holistic assessment presents an opportunity for decision makers to contextualize the changing energy costs and design effective strategies to foster a sustainable and cost saving mechanism. Policies such as bundled incentivization to motivate co-adoption, education about energy-management strategies, and add-on subsidies to adopt energy-efficient appliances, will not only accelerate decarbonization but increase household financial savings.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Annual Technology Baseline: The 2024 Electricity Update

Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' energy analysts. The ATB has been reviewed by experts and it includes the following electricity generation and storage technologies: land-based wind, offshore wind, distributed wind, utility-scale solar photovoltaics (PV), commercial-scale solar PV, residential-scale solar PV, concentrating solar power, geothermal power, hydropower, utility-scale battery storage, commercial battery storage, residential battery storage, pumped storage hydropower, nuclear, coal, and natural gas. EIA data for conventional biopower are included for reference. This webinar presentation introduces the 2024 update to the ATB Electricity data and documentation.

battery storage↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2025

Validation of Photovoltaic Modeling Tool Solargraf Against Measured Data

Solargraf is a cloud-based 3D design tool by Enphase Energy that allows users to design solar and storage systems with a variety of elements. Through a Cooperative Research and Development Agreement (CRADA), Enphase Energy collaborated with the National Renewable Energy Laboratory (NREL) to validate Solargraf's 3D design simulation against measured PV system performance. This study follows the same methodology of similar validation studies completed at NREL. The predicted performance results from simulations in both Solargraf and NREL's System Advisor Model (SAM) tool were compared with measured data to evaluate performance predictions.

14 SOLAR ENERGY↗
DOE OSTI2025

Novel Approach to PV Inverter Modeling and Simulation Leveraging Experiments, Learning Based Modeling and Co-Simulation

Photovoltaic (PV) inverter manufacturers use custom, proprietary control approaches and topologies in their inverter design. The proprietary nature of these approaches makes it challenging to share electromagnetic transients (EMT) domain models for system studies. This research work presents an approach to develop EMT models from experimental data. We use novel approach in experimental design, high fidelity data collection, use of learning-based modeling, and co-simulation to reduce the time taken to develop an EMT model for an inverter under test (IUT). We used a 20 kW off-the-shelf grid following PV inverter and subjected the inverter to controlled tests. The tests include voltage and frequency step changes, as well as solar irradiance variations. The recorded high frequency data were used to train a neural network model representing the dynamic behavior of the IUT. The model was subsequently imported into an EMT tool using co-simulation techniques, and thus completing the modeling effort.

black box inverter modeling↗
DOE OSTI2024

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Enabling Interoperability on PV Inverters Using IEC 61850

Inverter based distributed energy resources are integrated into distribution system at a fast pace. Distribution management systems, microgrid controllers, and other distributed assets need to communicate with each other for sensing and control of the different assets. Interoperability is critical to the ease of data exchange between different assets. This presentation will provide a background on interoperability and a laboratory implementation using IEC 61850. Specifically, this presentation will discuss the use of IEC 61850 7-420 to enable interoperability in PV inverter.

41 EE - Solar Energy Technologies Office (EE-4S)↗