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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗
DOE OSTI2026

A Public Data Set of Auto-Generated Geotagged PV Site Equipment, Generated via Deep Learning

In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.

14 SOLAR ENERGY↗
DOE OSTI2021

Optimal Transactive Energy Trading of Electric Vehicle Charging Stations With On-Site PV Generation in Constrained Power Distribution Networks

This paper presents a two-level transactive energy market framework, that enables energy trading among electric vehicle charging stations (EVCSs). At the lower level, the discharging capability of EVs and on-site PV generation are leveraged by individual EVCS for participating in the transactive trading with their peers. Once the lower-level trading is completed, EVCSs trade energy at the upper level through the power grid network managed by the distribution system operator (DSO). The upper-level market is cleared while satisfying the power distribution network constraints. A cooperative game-based model is proposed to model the energy trading among EVCSs. To this end, the asymmetric Nash bargaining method is applied to allocate the grand coalition's payoff to each EVCS at the upper-level market, while a weighted proportional allocation method is used to allocate individual EVCS's payoff to its respective EVs at the lower-level market. In this work, the upper-level market formulation is further decomposed into two subproblems representing an energy scheduling and trading subproblem which maximizes EVCS payoffs, and a bargaining subproblem which allocates EVCS payoffs. The effectiveness of the proposed framework for incentivizing transactive trades among EVs and EVCSs is validated in case studies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Making Better Use of On-Site PV Generation: Direct Distribution of DC Power in Buildings

With the increasing availability of Direct Current (DC) power from on-site solar systems, building designers now have the option of direct distribution of DC power to energy storage systems and end-use loads such as lighting, electronics, and motors. Research shows that direct-DC can reduce building distribution losses by 5-15%, while improving power reliability. A readily available way to implement direct-DC distribution is Power over Ethernet (PoE), which typically has provided low-voltage DC to power office equipment such as WiFi access points. With recent updates to the PoE standard, equipment such as network-controlled LED lighting, thin client and computer workstations, HVAC controllers, fans, motors, and actuators can now be powered by PoE and are commercially available. This paper presents results from a DOE-funded project that is demonstrating PoE technologies and describes a DC modeling and design tool that is intended to help building designers properly evaluate and design DC distribution systems in buildings. The tool provides designers of DC power systems with estimates of both power system losses and indirect effects on building heating and cooling. The results of this research demonstrate that the design methods and products are available to implement DC power in buildings today.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. Finally, this research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Planning, Procuring, & Constructing on-site PV [Slides]

A developer will install a renewable energy system on DOE property. In exchange, DOE agrees to purchase the power generated by the system. These power purchase payments repay the developer over the contract term. The developer owns, operates, and maintains the system for the life of the contract.

14 SOLAR ENERGY↗
DOE OSTI2019

Foresee the Future: Using Machine Learning, Climate, and Site Characteristics to Predict PV Solar Plant Generation

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and increasing the amount of revenue generated. Machine learning techniques can help us in this regard by providing more accurate predictions of PV power production, such that the forecasts take into account not only a site's system design characteristics, but also important weather and climate information. This type of research is important because we can leverage the vast amounts of SCADA data we collect to build more effective, accurate models that can help improve our performance management.

14 SOLAR ENERGY↗
DOE OSTI2023

Bifacial PV Module Energy Modeling Validation Study

The cost delta between monofacial and bifacial photovoltaic (PV) modules was decreasing in 2018 when this study was initially proposed, making bifacial modules an attractive offering. However, there were limited field studies available that could be considered utility-scale representative, and PV module manufacturers were stating large ranges for the expected energy yield improvements of bifacial technology. This generated uncertainties regarding bifacial performance gains and the actual LCOE (levelized cost of energy). There was also relatively low confidence in the industry’s energy modeling tools’ ability to accurately predict bifacial energy yield, meaning that early adopters of bifacial modules were not able to fully account for the increased energy yield in their energy and financial models. The intent of this project was to provide the solar industry with greater certainty on the energy yield gains of bifacial modules and provide higher confidence in the ability for different energy modeling software to model bifacial PV site performance. Achieving this would allow for bifacial technology to become bankable (i.e. accepted by Independent Engineers, site financiers and other PV site stakeholders), offering a step change in PV site performance that had not been seen since the widespread adoption of the single-axis tracker. Over the course of this study other economic factors including a significant tariff exemption for bifacial modules resulted in accelerated adoption of bifacial technology throughout the US utility scale solar segment. The number of early adopters increased rapidly, and with many investors accepting this module choice the question of bifacial bankability was seemingly answered faster than expected. However, PVEL’s study has still achieved significant accomplishments in demonstrating the accuracy of bifacial modeling across three different software platforms. The results of this study have shown that the mean biased error (MBE) between the field data and the predicted values from all three software platforms were aligned with a maximum MBE of 1.3% and a minimum MBE of -1.8%.

14 SOLAR ENERGY↗
DOE OSTI2021

Photovoltaic Data Acquisition (PVDAQ) Public Datasets

The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.

Array↗
DOE OSTI2023

Vegetation Management Cost and Maintenance Implications of Different Ground Covers at Utility-Scale Solar Sites

Utility-scale solar photovoltaics (PV) is the largest and fastest-growing sector of the solar energy market, and plays an important role in ensuring that state and local jurisdictions can meet renewable energy targets. Potential adverse environmental impacts of utility-scale solar PV are well-documented, and the effects of diverse mitigation and dual land use strategies under the banner of ’low-impact solar’ are justly receiving more attention; this article seeks to contribute to improving understanding of this topic. Capital costs for different PV configurations are well-documented; however, operation and maintenance (O&M) costs for vegetation management at low-impact utility-scale solar PV sites are not as well-understood, particularly as they compare to costs for sites that use more conventional ground cover practices, such as turfgrass or gravel. After a literature review of different vegetation strategies and O&M cost considerations, we collected data from utility-scale solar PV O&M stakeholders, including site owners/operators, O&M service providers, vegetation maintenance companies, and solar graziers, on costs and activities associated with vegetation management at low-impact, agrivoltaic, and conventional PV sites. In this paper, we perform data analysis to detail the per-activity and total O&M costs for vegetation management at PV sites with different ground covers and management practices, providing the most comprehensive and detailed assessment of PV vegetation O&M costs to date. For the 54 sites included in our analysis, we found that while the per-acre and per-kilowattdc (kWdc) costs for individual activities, such as mowing, trimming, and herbicide application at native or pollinator friendly ground covers, were lower than at turfgrass sites, the total combined vegetation O&M costs were slightly higher; this is presumably because more individual activities are required for the first 3–5 years of vegetation establishment. Qualitative results include recommendations from data providers for site and system design, and ongoing vegetation management operations.

14 SOLAR ENERGY↗
DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗
DOE OSTI2015

The impacts of net metering on utility profits and rates: Case studies of two prototypical utilities

Heated debates about the impact of net metering on utility shareholders and ratepayers have surfaced in some of the larger state solar markets and will only become more pronounced and widespread as solar costs decline and deployment accelerates. In order to inform these discussions, we performed a scoping analysis to quantify the magnitude of the financial impacts of distributed PV on utility shareholders and ratepayers, and that assesses the potential efficacy of various options for mitigating those impacts. We quantify the impacts of customer-sited PV for two prototypical investor-owned utilities: a vertically integrated utility located in the southwest and a wires-only utility and default service supplier located in the northeast. For each utility, we model the impacts of customer-sited PV over a 20-year period, estimating changes to utility costs, revenues, average rates, and utility shareholder earnings and return-on-equity.

Barbose, G↗
DOE OSTI2017

Pathways for Off-site Corporate PV Procurement

Through July 2017, corporate customers contracted for more than 2,300 MW of utility-scale solar. This paper examines the benefits, challenges, and outlooks for large-scale off-site solar purchasing through four pathways: power purchase agreements, retail choice, utility partnerships (green tariffs and bilateral contracts with utilities), and by becoming a licensed wholesale seller of electricity. Each pathway differs based on where in the United States it is available, the value provided to a corporate off-taker, and the ease of implementation. The paper concludes with a discussion of future pathway comparison, noting that to deploy more corporate off-site solar, new procurement pathways are needed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Predicting Solar Plant Generation with Machine Learning Techniques

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.

14 SOLAR ENERGY↗
DOE OSTI2025

Bat activity at ecovoltaic solar energy developments in the Midwestern United States

As global photovoltaic (PV) solar electricity generation continues to increase, some PV sites are co-prioritizing electricity generation and ecosystem function (“ecovoltaics”) to align renewable energy development with biodiversity conservation. Thus far, positive responses of plant and insect communities to native habitats at ecovoltaic sites have been observed, but there has been little research on bat responses to ecovoltaic designs in the U.S. We conducted passive ultrasonic monitoring in 2023 and 2024 at 12 solar sites and paired reference sites (agricultural fields) to investigate bat activity responses to ecovoltaic facilities in the Midwestern U.S. We found that average weekly overall bat activity was approximately 50?% higher within ecovoltaic sites than reference sites in the first half of the monitoring season. We also found species-specific differences in bat responses to ecovoltaic sites, with Hoary Bats showing higher activity on ecovoltaic sites throughout most of the monitoring season, Big Brown Bats showing higher activity on ecovoltaic sites during the first one-third of the monitoring season, and Silver-haired Bats showing no difference in activity between ecovoltaic sites and reference sites. There were no weeks in which bat activity was statistically greater on reference sites, suggesting that bats in the Midwestern U.S. do not avoid ecovoltaic solar sites. Rather, our results suggest that ecovoltaic sites in this region may provide early season (May-June) habitat at a time of year when resources may be limited in the surrounding landscape. These findings support a growing body of evidence on the positive ecological outcomes of ecovoltaic designs. Further investigations of the types of bat calls being recorded at PV sites and relationships with insect prey abundance are needed to understand the underlying drivers of species-specific responses to PV developments.

14 SOLAR ENERGY↗
DOE OSTI2018

Coherent Probabilistic Solar Power Forecasting

Solar power has been growing rapidly in recent years. Many countries have invested in solar energy technology, especially in Photovoltaic (PV) power generation. With the increased penetration level, solar power forecasting becomes more challenging. To cope with solar power uncertainties, probabilistic forecasting provides more information than traditional point forecasting. Moreover, multiple PV sites with spatial-temporal correlations need to be taken into account. To produce probabilistic forecasts, this paper applies quantile regression on top of time series models. Considering the coherency among multiple PV sites, a reconciliation is applied using a copula-based bottom-up method or proportion-based top-down method. Numerical results show that the proposed methods efficiently produce accurate and coherent probabilistic solar power forecasts.

quantile regression↗
DOE OSTI2018

A methodology to analyze photovoltaic tracker uptime

A metric is developed to analyze the daily performance of single-axis photovoltaic (PV) trackers. The metric relies on comparing correlations between the daily time series of the PV power output and an array of simulated plane-of-array irradiances for the given day. Mathematical thresholds and a logic sequence are presented, so the daily tracking metric can be applied in an automated fashion on large-scale PV systems. The results of applying the metric are visually examined against the time series of the power output data for a large number of days and for various systems. The visual inspection results suggest that overall, the algorithm is accurate in identifying stuck or functioning trackers on clear-sky days. Visual inspection also shows that there are days that are not classified by the metric where the power output data may be sufficient to identify a stuck tracker. Based on the daily tracking metric, uptime results are calculated for 83 different inverters at 34 PV sites. The mean tracker uptime is calculated at 99% based on 2 different calculation methods. The daily tracking metric clearly has limitations, but as there is no existing metrics in the literature, it provides a valuable tool for flagging stuck trackers.

14 SOLAR ENERGY↗