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Results for “Solar Energy Modeling”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Defects in RbF - Treated Cu(InxGal-x)Se2 Solar Cells and Their Impact on Voc

Cu(In,Ga)Se2 solar cell efficiency is limited by VOC due in large part to bulk defects limiting lifetime, but alkali treatments such as RbF recover some of the VOC loss. In this work, defects in RbF-treated and untreated CIGS were quantitatively characterized using DLTS and DLOS, and three main defects were identified in each sample. The RbF-PDT resulted in a large decrease in the mid-gap trap concentration, which was accompanied by a large improvement in minority carrier lifetime. This lifetime improvement combined with a change in doping accounted for a significant portion of the VOC improvement in the RbF CIGS.

charge carrier lifetime↗
DOE OSTI2023

A New Method for the Evaluation of Majority and Minority Carrier Contact Resistivity of Polysilicon on Oxide Contacts

In this contribution we present a novel technique for characterizing the majority and minority carrier contact resistivity in polysilicon on oxide passivating contacts. Traditionally, contact resistivity is measured through the Transfer Length Method (TLM). This method presents difficulties when measuring the minority carrier contact due to the presence of a blocking diode which prevents current flow between the pads. Our novel method is performed by creating a unipolar device which has the same contact on both sides (either symmetric n-type or symmetric p-type) and metalizing the device with a grid on one side, and a full metal contact on the back (the same pattern that will be used in a final cell). We then measure the resistance across the device at increasing illumination. Since the resistivity of the wafer is a function of carrier density, when increasing illumination, the wafer resistivity decreases. We can then fit modeling equations to the measured resistivity and extrapolate the fit to infinite illumination where only the contact resistivity is measured to accurately measure both the minority and majority carrier contact resistivity.

current measurement↗
DOE OSTI2020

Using Behavioral Science to Target LMI and High-Value Solar Installations (Final Technical Report)

This project is a data-driven analysis of approaches to facilitate solar adoption in LMI communities and areas with high potential benefits to the electric grid. The cornerstone of the project was two waves of randomized field experiments. The first wave involved novel approaches to reaching LMI communities based on insights from literature. The second wave involved areas of high potential of solar to the electricity grid, as determined in collaboration with the local utilities in Connecticut. Both waves were followed by surveys to develop insight into the effectiveness of different approaches. A third key element of the project is the development of new agent-based models of solar energy diffusion that explicitly incorporate the real spatial and temporal constraints that influence solar diffusion, based on real-world data on solar adoption. The project also involved outreach and dissemination of key findings to relevant stakeholders, including a guidebook on strategies to accelerate solar deployment to low and moderate income communities, multiple academic publications, and numerous presentations of the research findings.

14 SOLAR ENERGY↗
DOE OSTI2020

Combined-Accelerated Stress Testing for Advanced Reliability Assessment of Photovoltaic Modules

New and previously undiscovered degradation mechanisms continue to be identified after field exposure in new photovoltaic module designs or materials oftentimes results in significant losses in investment. This is despite passing certification tests. Testing is insufficient largely due to their single-or double-stress nature. A Combined-Accelerated Stress Test (C-AST) has recently been developed. The test combines multiple stress factors of the natural environment to better replicate conditions experienced by modules in the field and detect mechanisms not a-priori known in new materials and designs.

accelerator factor modeling↗
DOE OSTI2020

Analysis and Validation of Spectral Irradiance Simulations for 75 kW Bifacial Solar Test Field

This work investigates how the spectrum of irradiance incident on the rear of bifacial modules impacts their performance. Light incident on the rear of bifacial modules can be reflected from the ground, mounting structures, and other nearby objects, causing both the intensity and spectrum to be modified. As a result, PV cells and sensors with different spectral responses report different values for the rear irradiance, leading to increased uncertainty when modeling performance. Here, we calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single-axis-tracked bifacial test site. Spectral responses for different sensors and PV cells are applied and compared with field measurements for four days. Based on these results, a method for improving non-spectral simulations is proposed. In addition, a linear regression is applied to the modeled spectral results as a method to gain insight into the contributions of ground reflected light to total rear irradiance.

bifacial photovoltaics↗
DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models: Preprint

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like treelines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj:R2 of 0:95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗
DOE OSTI2021

Use of Physics to Improve Solar Forecast: Physics-Informed Persistence Models for Simultaneously Forecasting GHI, DNI, and DHI

Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (< 1 hour). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM)'s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. Overall, the results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 hours up to 6 hours. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the nexus between groundwater and solar energy plants in a desert basin with a dual-model approach under uncertainty

Globally, many solar power plants and other types of renewable energy are being located in water-scarce regions. Many projects rely on groundwater resources whose sustainability is uncertain. In the Chuckwalla Basin in California, quantification of recharge and trans-valley underflow is needed to estimate the impacts of solar project withdrawals on the water table. However, such estimates are highly challenging due to data scarcity, heterogeneous soils and long residence times. Conventional assessment employs isolated groundwater models configured with crude and uniform estimates of recharge. Here, we employ a data-constrained surface subsurface processes model, PAWS+CLM, to provide an ensemble of recharges and underflows with perturbed parameters. Then, the Parameter Estimation (PEST) package is used to calibrate MODFLOW aquifer conductivity and filter out implausible recharges. The novel dual-model approach, potentially applicable in other arid regions, can effectively assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was assumed, it resulted in under-estimated drawdown and larger uncertainty bounds. The withdrawals are approaching total inflow, suggesting the system will be nearing, if not exceeding, its sustainable groundwater production capacity, and a boom of such projects will not be sustainable. Especially, the cost/benefit of pumped-storage projects is called into question as the initial-fill phase depletes entire area’s recharge. Our study highlights the stress on groundwater resources of solar development, and that the speed of groundwater recovery does not indicate sustainability. Main point 1: A novel dual model approach, involving an integrated surface/subsurface model and a groundwater parameter-estimation model, was able to better constrain the model. Main point 2: The groundwater system may be nearing, if not exceeding, its sustainable groundwater production capacity and the speed of recovery is not indicative of sustainability. Main point 3: Results from using conventionally-assumed uniform recharge distort calibrated K fields and impacts assessment

14 SOLAR ENERGY↗
DOE OSTI2022

Probabilistic Modeling of Climate Change Impacts on Renewable Energy and Storage Requirements for NM's Energy Transition Act (SAND Report)

This report provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico’s Integrated Resource Plan for 2020 – 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~25,000 – 30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modeling, Load Profile Validation, and Assessment of Solar-Rooftop Energy Potential for Low-and-Moderate-Income Communities in the Caribbean

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community.

14 SOLAR ENERGY↗
DOE OSTI2022

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

This paper uses a two-level model predictive control-based approach for the coordinated control and energy management of an integrated system that includes photovoltaic (PV) generation, energy storage, and building loads. Novel features of the proposed local controller include (1) the ability to simultaneously manage building loads and energy storage to achieve different operational objectives such as energy efficiency, economic cost efficiency, demand response and grid optimization through the design of specific power trajectory tracking performance functionals, (2) an energy trim function that minimizes the impact of solar forecasting errors on system performance, and (3) the design of a state of charge controller that uses day-ahead forecast of solar power and building loads to intialize energy storage at the start of each day. The local controller is tested in simulation using an exemplary system with PV generation, energy storage and dispatchable building loads. Two sample days with different PV forecasts and multiple case scenarios are considered, and the performance of the algorithm in managing the real and reactive net building load trajectories and the ramp rate of PV injections into the utility network are evaluated. The simulations are based on actual forecasted and measured PV data, and the results show that the local controller meets the tracking requirements for real and reactive power within the operating constraints of the building.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling the potential effects of rooftop solar on household energy burden in the United States

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance.

14 SOLAR ENERGY↗
DOE OSTI2023

Modeling of Particle Thermal Energy Reservoir for Solar Industrial Process Heat (Final Technical Report)

Industrial process heat is a leading source of carbon emissions in the United States. To achieve decarbonization goals and reduce costs, solar industrial process heat (SIPH) systems have been investigated as a means of providing a carbon-free heat supply. Particle thermal energy storage (TES) could supplement solar resources (i.e., concentrating solar thermal and photovoltaics) to enable a high capacity factor (> 90%), carbon-free heat source. Particle TES has been considered due to its low-cost storage medium and capability to support a wide range of temperatures. This report provides technical details of developing a component and system modeling tool for a unique particle TES platform to assist the adoption of SIPH technology.

14 SOLAR ENERGY↗
DOE OSTI2020

Aluminum-doped calcium manganite particles for solar thermochemical energy storage: Reactor design, particle characterization, and heat and mass transfer modeling

A two-step cycle is discussed for solar thermochemical energy storage based on particulate aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompass 1) the storage of concentrated solar irradiation within endothermic reduction of aluminum-doped calcium manganite and 2) the delivery of heat to an Air-Brayton cycle via exothermic re-oxidation of oxygen-deficient aluminum-doped calcium magnanite. A 5 kW th scale solar thermochemical reactor operating under vacuum was designed, modeled, and optimized to thermally reduce a continuous, gravity-driven flow of aluminum-doped calcium manganite particles. The granular flows were characterized in a tilt-flow rig, and particle image velocimetry was used to determine flow properties via frictional and velocity scaling relationships. Flow properties were integrated into a detailed heat and mass transfer model of the solar thermochemical reactor. A reactor design with 31° inclination angle, 230 g/min of particles, and 5.2 kW th radiative input from the high-flux solar simulator was discovered to produce an outlet flow temperature of 1158 K, with stoichiometric deviations of 0.076 and a storage efficiency of 0.628 while avoiding particle overheating and promoting longer particle residence times.

14 SOLAR ENERGY↗
DOE OSTI2024

Droughts in Wind and Solar Power: Assessing Climate Model Simulations for a Net‐Zero Energy Future

Abstract Understanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

Liu, Xue [Department of Oceanography Texas A&amp,M↗
DOE OSTI2023

Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB

Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.

data models↗