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Deploying Intra-hour Uncertainty Analysis Tools to ABB’s GridView - CRADA 445
The quickly-changing generation resource mix in the US grid, with large additions of variable resources, retirements of traditional thermal generation, distributed generation and demand response, are creating new challenges on the traditional operation of both the generation and transmission systems. The ability to perform intra-hour high fidelity production cost modeling (PCM) is needed to allow grid operators and grid planners to integrate high penetration (more than 50%) of variable energy resources, and to make prompt well informed decisions in market operations and planning. In the last decade, PNNL has developed several stand-alone tools to enable grid operators and planners to understand the impact of high variable generation on their systems. These tools have been used is studies such as: (1) Evaluation the benefits of WECC balancing authorities coordination under high variable generation penetration , (2) Benefits of Energy Imbalance Market in the North West Power Pool and (3) Duke Energy and NV Energy solar integration studies. ABB’s GridView is a widely used commercial PCM tool. It is the tool used by WECC and their stakeholders to develop the WECC PCM planning model on bi-annual basis. This proposal is focused on the integrating of PNNL intra-hour uncertainty analysis tools to GridView.
Design and performance of a variable gap system for thermal conductivity measurements of high temperature, corrosive, and reactive fluids
We report high-temperature fluids such as molten salts, liquid metals, and gasses are being proposed for many advanced energy systems including thermal energy storage devices, concentrating solar plants, and advanced nuclear reactor designs. However, the chemical behavior and thermophysical properties of many of these fluids have not been well characterized, which hinders the design, modeling, safety analysis, and deployment of these systems. Thermal conductivity is a property that is especially limited by existing measurement capabilities, which are subject to errors caused by convection, material interaction, radiative heat transfer, and instrument degradation. Therefore, there is a lack of standard, systematic measurement techniques for high-temperature, reactive, and corrosive fluids. In this work, the development of a variable gap thermal conductivity measurement system is detailed. The system is designed to measure the thermal conductivity of highly corrosive and reactive fluids, and survive operation between 100 °C and 800 °C. The effects of convection are minimized by limiting the thickness of the specimen to thin sizes (<0.3 mm). Corrections for radiative heat transfer were included in the working equations to consider specimens with varying optical properties. The design, construction, instrumentation, operating principles, and data analysis techniques are discussed in detail. The system was tested up to 500 °C using helium gas and molten KNO 3 -NaNO 3 to verify the measurement technique and determine the sources of error. At 300 and 400 °C KNO 3 -NaNO 3 , results showed maximum relative error of 6% when compared to results in the literature. The helium results were within 13% of those in the literature at 300 and 400 °C. Higher errors were observed at 500 °C for both fluids, and the sources of these errors are discussed.
Why Wideband Gap Needs Techno-Economics
The wideband gap community when discussing projects antidotally refers to the cost of the technology, frequently noting the higher wideband gap upfront cost in comparison to the incumbent state of art. Other technologies such as photovoltaics have embraced techno-economics analysis (TEA) as a method of analytically articulating the impacts of research advancements on system performance and lifetime costs. Our TEA shows the potential impact of research advancements on Gallium Oxide (Ga2O3) and Silicon Carbide (SiC) wafer manufacturing cost, the impact on the levelized cost of electricity (LCOE) when using a SiC based solar inverter, and lastly how the high cost of devices (SiC in this case) can be offset by other system cuts, such as the footprint and cooling reductions in a 1MW variable frequency motor drive. The specific goal is to show analytically the lifetime cost and performance benefits of adopting wideband gap technology whether it is at the device level or it is cumulative benefit though a simplified balance of system.
Earth as an Exoplanet. I. Time Variable Thermal Emission Using Spatially Resolved Moderate Imaging Spectroradiometer Data
Among the more than 4000 exoplanets known today, some terrestrial planets have been detected in the so-called habitable zone of their host stars and their number is expected to increase in the near future, energizing a drive to understand and interpret the eagerly awaited wealth of data to identify signs of life beyond our solar system. So far, Earth remains the best and only example of a habitable (and inhabited) world. Although, it seems extremely unlikely that any other exoplanets will be true Earth twins, it is important to explore and understand the full range of spectral signatures and variability of Earth in order to inform the design of future instruments and missions, and understand their diagnostic power as well as potential limitations. In this work we use Earth observation data collected by the MODIS instrument aboard the Aqua satellite. The complete data set comprises 15 years of thermal emission observations in the 3.66–14.40 μm range for five different locations on Earth (Amazon Rainforest, Antarctica, Arctic, Indian Ocean, and the Sahara Desert). We then determine flux levels and variations as a function of wavelength and surface type (i.e., climate zone and surface thermal properties) and investigate whether periodic signals indicating Earth’s tilted rotation axis can be detected. Our findings suggest that (1) viewing geometry plays an important role when thermal emission data is analyzed as Earth’s spectrum varies by a factor of three and more depending on the dominant surface type underneath; (2) typically strong absorption bands from CO 2 (15 μm) and O 3 (9.65 μm) are significantly less pronounced and partially absent in data from the polar regions implying that estimating correct abundance levels for these molecules might be challenging in these cases; and (3) the time-resolved thermal emission spectrum encodes information about seasons/planetary obliquity, but the significance depends on the viewing geometry and spectral band considered.
Solar Power + Electric Vehicle Charging: Capturing Synergies in Minnesota
Despite the state policy support, significant market barriers to decarbonization of transportation remain. Analysis conducted by the National Renewable Energy Laboratory (NREL) and others confirm that without routine access to charging infrastructure, both at home and in non-residential locations, bullish market forecasts are unlikely to materialize. Similarly, the expansion of EV charging and variable renewable energy deployment each have potentially disruptive and costly effects on the distribution grid. Finally, electrification of passenger vehicles is not enough to decarbonize transportation without pathways to carbon-free electrification, linking EV charging and clean energy generation. This document examines the opportunity to jointly deploy solar energy and EV charging infrastructure to capture synergistic value, mitigate for risks associated with integrating and scaling both solar and EV charging, and accelerate their mutual deployment.
Comparing different generations of idealized solar geoengineering simulations in the Geoengineering Model Intercomparison Project (GeoMIP)
Abstract. Solar geoengineering has been receiving increased attention in recent years as a potential temporary solution to offset global warming. One method of approximating global-scale solar geoengineering in climate models is via solar reduction experiments. Two generations of models in the Geoengineering Model Intercomparison Project (GeoMIP) have now simulated offsetting a quadrupling of the CO2 concentration with solar reduction. This simulation is idealized and designed to elicit large responses in the models. Here, we show that energetics, temperature, and hydrological cycle changes in this experiment are statistically indistinguishable between the two ensembles. Of the variables analyzed here, the only major differences involve highly parameterized and uncertain processes, such as cloud forcing or terrestrial net primary productivity. We conclude that despite numerous structural differences and uncertainties in models over the past two generations of models, including an increase in climate sensitivity in the latest generation of models, the models are consistent in their aggregate climate response to global solar dimming.
Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic
In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.
Investigations of the stability of etched or platinized p-InP(100) photocathodes for solar-driven hydrogen evolution in acidic or alkaline aqueous electrolytes
Here, the stability of p-InP photocathodes performing the hydrogen-evolution reaction (HER) has been evaluated in contact with either 1.0 M H 2 SO 4 (aq) or 1.0 M KOH(aq), with a focus on identifying corrosion mechanisms. Stability for the solar-driven HER was evaluated using p-InP electrodes that were either etched or coated with an electrodeposited Pt catalyst (p-InP/Pt). Variables such as trace O 2 were systematically controlled during the measurements. Changes in surface characteristics after exposure to electrochemical conditions as well as electrode dissolution processes were monitored using X-ray photoelectron spectroscopy (XPS) and inductively coupled plasma mass spectrometry (ICP-MS). In either H 2 SO 4 or KOH, etched p-InP photoelectrodes corroded cathodically under illumination, forming metallic In 0 at the electrode surface. In contrast, electrodeposition of Pt kinetically stabilized illuminated p-InP photocathodes in both H 2 SO 4 and KOH by inhibiting the cathodic corrosion pathway. Notably, when held at 0 V vs. the reversible hydrogen electrode (RHE) in 1.0 M H 2 SO 4 (aq), p-InP/Pt exhibited a stable current density (J) of ~–18 mA cm –2 for >285 h under simulated 1 Sun illumination. The long-term current density vs. potential (J–E) behavior at pH 0 and pH 14 of p-InP/Pt photocathodes correlated with changes in the surface chemistry as well as the dissolution of p-InP. In acidic media, the J–E behavior of p-InP/Pt photocathodes remained nearly constant with time, but the surface of a p-InP/Pt electrodes gradually turned P-rich via a slow and continuous leaching of In ions. In alkaline electrolyte, the surface of p-InP/Pt electrodes was passivated by formation of an InO x layer that exhibited negligible dissolution but led to a substantial degradation in the J–E characteristics. Consequently, changes in the catalytic kinetics and surface stoichiometry are both important considerations for determining the corrosion chemistry and the long-term operational stability of InP photoelectrodes.
Characterization of multiterminal tandem photovoltaic devices and their subcell coupling
Three-terminal (3T) and four-terminal (4T) tandem photovoltaic (PV) devices using various materials have been increasingly reported in the literature, but measurement standards are lacking. Here, multiterminal devices measured as functions of two load variables are characterized unambiguously as functions of three device voltages or currents on hexagonal plots. We demonstrate these measurement techniques using two GaInP/GaAs tandem solar cells, with a middle contact between the two subcells, as example 3T devices with both series-connected and reverse-connected subcells. Coupling mechanisms between the subcells are quantified within the context of a simple equivalent optoelectronic circuit. Electrical and optical coupling mechanisms are most clearly revealed using coupled dark measurements. These measurements are sensitive enough to observe very small luminescent coupling from the bottom subcell to the top subcell in the prototype 3T device. Quick simplified measurement techniques are also discussed within the context of the complete characterization.
First-Principles Study on the Role of Cu and Cl-Based Dopants in NiO
Utilization of wide band gap oxide-based materials in thin-film solar energy technologies has increased in recent years. Among the numerous candidate oxide materials, NiO has shown many desirable optoelectronic properties that are applicable to thin-film PV technologies such as cadmium telluride PV. However, one critical factor requiring further investigation is the p-type doping behavior of NiO, specifically when the cadmium telluride solar cell undergoes conventional processes such as copper doping and chlorine-based activation treatment. The previous literature has shown a large degree of variability in hole concentrations in NiO when copper is used as the primary dopant. This study uses first-principles computational modeling based on density functional theory coupled with defect equilibria calculations to quantitatively explore the role of copper and chlorine-based extrinsic dopants in the p-type doping activity of NiO. The study reveals the importance of extrinsic dopants and their binding interactions with nickel vacancies to effectively p-dope NiO. It is suggested that both the formation of V Ni + Cu Ni and V Ni + Cl O defect pairs under a supersaturated state of NiO are potential mechanisms for increasing hole densities. On the other hand, the production of 2Cu Ni severely limits the effectiveness of p-doping in NiO, even in the presence of the aforementioned defect pairs. Furthermore, the study provides a guideline for experimentalists interested in using copper or chlorine species to understand how to controllably p-dope NiO during thin-film synthesis.
Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)
Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.
EUI Benchmarks for Net-Zero Energy Buildings in India
In our study we present EUI benchmarks for NZEBs for six building types across residential and non-residential typologies and for India's five climate zones. This approach is similar to the simulation-based benchmarks used by the B3 program in Minnesota, the Cal-Arch methodology in California, and the US Solar Decathlon approach, which combine simulations with actual building data. Of the six building types we explore one in detail with a range of operation scenarios, specifically narrowing down the EUI benchmarks for mixed-mode building operation and for variable temperature set-points as prescribed in the National Building Code of India.The contribution of this work is to provide rigorous end-use level EUI benchmarks for six building types, and to describe a method for simulation-based EUI benchmarks for mixed-mode operation with variable setpoints to highlight the difference between the standard approach used for the five building types and the On-Site Construction Worker Housing which additionally has the mixed-mode variable setpoint approach.On-Site Construction Worker Housings are typically poorly constructed temporary structures, without adequate thermal comfort, It is critical to provide adequate thermal comfort to protect people from the warming effects of climate change, and to discover super efficient and cost-effective ways to do so.The methodology used for the On-Site Construction Worker Housing (CWH) results in an 80% acceptability according to the India Model for Adaptive Comfort in the National Building Code of India. EUIs of all six buildings are 60% lower than the minimum compliance with India's Energy Conservation Building Codes, providing benchmarks for efficiency levels. The end-use level EUI benchmarks are now provided to over 1800 Solar Decathlon India participants so that they can compare the performance of their NZEB designs.In particular, the CWH results provide an insight into the importance of the mixed-mode operation with variable temperature set-points. The results from the simulation study show that for an NZEB target, the EUI with standard thermal comfort model and without mixed operation is 58.26 kWh/m2*year, while that with the variable set-points of the adaptive model with mixed mode operation is 24.23 kWh/m2*year. This is a 58% reduction in EUI. Given that many building types including residential, and non-residential operate in mixed mode, it is important to take this work further to develop mixed-mode operation NZEB benchmarks so that the carbon intensity of these buildings could be lower than the standard thermal comfort model approach.
The Solar System calcium isotopic composition inferred from Ryugu samples
The Hayabusa2 spacecraft has returned samples from the Cb-type asteroid (162173) Ryugu to Earth. Previous petrological and chemical analyses support a close link between Ryugu and CI chondrites that are presumed to be chemically the most primitive meteorites with a solar-like composition. However, Ryugu samples are highly enriched in Ca compared to typical CI chondrites. To identify the cause of this discrepancy, here we report stable Ca isotopic data (expressed as δ 44/40 Ca SRM915a ) for returned Ryugu samples collected from two sites. We found that samples from both sites have similar δ 44/40 Ca SRM915a (0.58 ± 0.03 ‰ and 0.55 ± 0.08 ‰, 2 s.d.) that fall within the range defined by CIs. This isotopic similarity suggests that the Ca budget of CIs and Ryugu samples is dominated by carbonates, and the variably higher Ca contents in Ryugu samples are due to the abundant carbonates. Precipitation of carbonates on Ryugu likely coincided with a major episode of aqueous activity dated to have occurred ~5 Myr after Solar System formation. Based on the pristine Ryugu samples, the average δ 44/40 Ca SRM915a of the Solar System is defined to be 0.57 ± 0.04 ‰ (2 s.d.).
Machine learning reduces soft costs for residential solar photovoltaics
Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.
Dryness controls temperature-optimized gross primary productivity across vegetation types
Temperature response of gross primary productivity (GPP) is a well-known property of ecosystem, but GPP at the optimum temperature (GPP_T opt ) has not been fully discussed. Our understanding of how GPP_T opt responds to warming and water availability is highly limited. Here, in this study, we analyzed data at 326 globally distributed eddy covariance sites (79°N-37°S), to identify controlling factors of GPP_T opt . Although GPP_T opt was significantly influenced by soil moisture, global solar radiation, mean annual temperature, and vapor pressure deficit in a non-linear pattern (R 2 = 0.47), the direction and magnitude of these climate variables’ effects on GPP_T opt depend on the dryness index (DI), a ratio of potential evapotranspiration to precipitation. The spatial pattern showed that soil moisture did not affect GPP_T opt across energy-limited sites with DI < 1 while dominated GPP_T opt across water-limited sites with DI >1. The temporal pattern showed that GPP_T opt was lowered by warming or low precipitation in water-limited sites while energy-limited sites tended to maintain a stable GPP_T opt regardless of changes in air temperature. Vegetation types in humid climates tended to have higher GPP_T opt and were more likely to benefit from a warmer climate since it was not restricted by water conditions. This study highlights that the response of GPP_T opt to global warming depends on the dryness conditions, which explains the nonlinear control of water and temperature over GPP_T opt . Our finding is essential to realistic prediction of terrestrial carbon uptake under future climate and vegetation conditions.
Temporal complementarity and value of wind-PV hybrid systems across the United States
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Demonstrating SolarPILOT’s Python API Through Heliostat Optimal Aimpoint Strategy Use Case
SolarPILOT is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. SolarPILOT was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s1 System Advisor Model (SAM) in a simplified format. Prior means for user interaction with SolarPILOT have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This paper presents a new, full-featured, Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT’s capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instance with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layouts; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying solar field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating solar field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux distribution; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot’s functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize power tower CSP research. This enables CSP researchers to perform analysis that was previously not possible through SolarPILOT’s existing interfaces. This paper discusses the capabilities of CoPylot and presents a use case wherein we demonstrate optimal solar field aiming strategies.