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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Review of Value of Solar Studies In Theory and In Practice

This brief summarizes a collection of state- and utility-commissioned value-of-solar (VoS) studies and related literature, with a focus on who commissioned the study, which value and cost categories were discussed and/or quantified, and the methods used. Our objective is to compile information on prior VoS studies to inform state regulators and other stakeholders that may pursue related studies or integrate findings into rate design. The brief is organized into three parts: 1) an introduction to distributed solar photovoltaic (DPV) compensation; 2) a review of theoretical research on VoS; and 3) a review of VoS studies. The vast majority of VoS studies have served an informational role of quantifying the net benefits of PV. Three studies were commissioned in states or utility service territories that subsequently implemented VoS tariffs in California, New York, and Austin, Texas. When applied as a tariff, VoS aims to compensate PV output as efficiently as possible by doing so at rates that reflect the marginal benefits and costs of PV through value and cost categories that may vary temporally and/or geographically. This could lead to higher compensation in locations and times where more PV output is more valuable and consequently drive adoption in those locations to provide more societal benefits. Value and cost factors can be broadly grouped into five categories: generation, transmission, distribution, other utility, and other social categories. Those conducting VoS studies must weigh various tradeoffs when deciding which categories to include and quantify. Tradeoffs include prioritizing values based on their magnitude of value or cost impact, as well as taking into account the feasibility of data collection and accurate quantification. Values of higher magnitude and estimation feasibility are quantified in the majority of studies, including the earliest of studies conducted in the 2000s and 2010s. Additionally, some values of higher magnitude but low feasibility in the earliest of studies have become quantifiable in recent years. There are some values with low average system-wide levels but very high magnitude in specific locations or hours. The value magnitude in some cases can be tied to DPV penetration with low value in areas with little congestion and/or low penetration and vice versa. In these cases, values that are easier to quantify are often incorporated, while those that are more difficult are often addressed via a placeholder value. The placeholder value is paired with a discussion around data needs and methods to improve future estimates, as well as a conversation about when these value categories may increase in magnitude and necessitate more rigorous quantification. This brief summarizes findings from two meta-analyses of VoS studies that took place between 2005 and 2018, as well as findings from four additional studies published from 2018 to 2023. Table ES-1 summarizes the various value and cost categories included in each respective study and whether they were quantified, discussed, or omitted. Values such as avoided energy, capacity, transmission capacity, line losses, and avoided environmental costs are quantified in every study. Some categories were deemed harder to quantify and less impactful at the time of the study, so they were discussed but not quantified (e.g., ancillary services). Other categories, including many at the distribution level, were very locationally and/or temporally specific and dependent on high DPV penetration. These were sometimes quantified and at other times discussed. Notably, when it came to utility costs, integration costs were discussed in all cases, though they were deemed to have a small impact. Other utility costs were omitted for the most part; however, the utility-commissioned study (by NorthWestern Energy in Montana) included both lost utility revenue and programmatic/administrative cost categories. While there are some similarities across studies, each had fairly unique methods that are detailed in the body of this brief.

14 SOLAR ENERGY↗
DOE OSTI2023

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Corrosion analysis of Al-Ce-Ni and Al-Cu-Ce cast alloys in dilute boric acid at room and elevated temperatures

Two Al-Ce-Ni cast alloys, and an Al-Cu-Ce cast alloy, as candidate wet storage materials for spent nuclear fuels, were tested in 0.23 wt% H 3 BO 3 solution to assess the alloy corrosion resistance. Electrochemical and gravimetric corrosion data suggest that the three Al alloys are unlikely to undergo any severe corrosion in dilute H 3 BO 3 at or below 50 °C. Post-exposure characterization of the three Al alloys, including scanning and transmission electron microscopy and electron dispersive spectroscopy, revealed a corrosion product layer, mostly Al 2 O 3 , on the exposed surface and local penetration of oxygen into the alloy matrix. The degree of oxide layer growth and oxygen penetration is greater at 80 °C than the temperature at/below 50 °C. The Al-Cu-Ce alloy is considered less corrosion resistant than the other two alloys studied.

Al-Ce alloy↗
DOE OSTI2025

Potential Impacts of Dynamic Electricity Pricing in California: Load Shape and Customer Bill Impacts Under Elastic Customer Response

The increasing penetration of renewable energy in California has intensified grid management challenges, exemplified by the “duck curve” and the resulting need for steep ramping and curtailment of renewables. To address these issues, dynamic electricity tariffs that vary in near-real time are being considered to incentivize customers to shift demand and support the grid. This study extends previous work on the bill impacts of such tariffs in the absence of load response by quantifying the system-level and customer impacts of load response based on customer price elasticity. Customer-level load response modeling was conducted using meter data from 411,000 customers across residential, commercial, and industrial sectors. Customer demand elasticity was estimated using literature-based values, with scenarios ranging from low to high elasticity, including an automation-enhanced scenario. Results indicate that universal adoption of, and response to, dynamic tariffs can significantly reduce peak net load (by 15%) and maximum ramping requirements (by 20%) with moderate elasticity, delivering demand response resources comparable to or exceeding current programs at all elasticity levels. Bill analysis shows that, when responding elastically to dynamic prices, most non-PV customers experience modest savings, while PV customers may see higher effective rates due to lower compensation for exports during low-price periods. Emissions analysis reveals a reduction in per-kWh emissions system-wide, with a total absolute load increase of 2% accompanied by a negligible absolute emissions increase. The study concludes that while dynamic tariffs offer substantial grid benefits, customer bill savings under modeled response behaviors may be too modest to drive widespread adoption without additional incentives or enabling technologies. Future research should model flexible loads and advanced control technologies with greater fidelity to better represent the potential opportunities of dynamic tariffs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Impacts of High Variable Renewable Energy Futures on Electric-Sector Decision Making: Demand-Side Effects

Previous work by the Berkeley Lab describes how high shares of variable renewable energy (VRE) such as wind and solar power could change wholesale electricity price dynamics. These include the timing of when electricity is cheap or expensive, locational differences in the cost of electricity, and the degree of regularity or predictability in those costs. Many decentralized decision-makers on the demand-side may not yet have considered the implications of these possible future changes. In this report, we evaluate the sensitivity of a set of demand-side decisions to different levels of VRE penetration ranging from a low of 5-20% to a high of 40-50%. The analysis builds on hourly wholesale energy and capacity prices in different VRE scenarios for four wholesale markets in the United States for the year 2030 (CAISO, ERCOT, NYISO, and SPP). The principal question for this exploration is whether private and public electric-sector decisions that are made based on assumptions reflecting low VRE levels still achieve their intended objective in a high VRE scenario with 40-50% wind and solar? This scoping report evaluates the impacts of changing patterns of peak system needs on the benefits of demand reductions by examining the altered value of different energy efficiency (EE) measures. Similarly, we investigate new opportunities for large energy consumers that may arise from periods with very low wholesale electricity prices. We calculate the value of new process investments (e.g., hydrogen production and other generalized electro-commodities), showcase the varying value of new product storage investments (such as reservoir extensions at a desalination plant), and estimate the benefits of increased process flexibility that uses electricity as a process-input in addition to traditional fossil fuels (e.g., district energy systems). Finally, many decentralized decision-makers and end-use customers are not directly exposed to wholesale electricity prices but instead receive price signals from their retail electricity rates. As wind and solar shares increase, we compare the economic efficiency of flat retail rates relative to more dynamic time-of-use tariffs with and without critical peak-pricing events.

14 SOLAR ENERGY↗
DOE OSTI2025

Experimental study of multiple-orientation muon tomography with image optimization in sparse data environments

Due to the high penetrating power of cosmic-ray muons, they can be used to probe very thick and dense objects. As muons are charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object to measure particle direction change, scattering information within the object can be found. This can be used to produce a scattering-intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies were published with multiorientation tomography, which can form a three-dimensional representation faster than a single-orientation view. In this study, experimental muon-scatter-based tomography was performed using a concrete filled steel drum with several different metal wedges inside, with the drum between detector planes. Data were collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were combined using two different tomographic methods. A traditional inverse Radon transform approach used for computed tomography and a combination of multiple depth-of-field reconstructions were applied to the data. As cosmic-ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedges.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2024

First experimental study of multiple orientation muon tomography, with image optimization in sparse data environments

Due to the high penetrating power of cosmic ray muons, they can be used to probe very thick and dense objects. As charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object, particle direction changes can be used to extract scattering information within an object. This can be used to produce a scattering intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies have been published with multi-orientation tomography, which can form a three-dimensional representation faster than a single orientation view. In this work we present the first experimental multiple orientation muon tomography study. Experimental muon-scatter based tomography was performed using a concrete filled steel drum with several different metal wedges inside, between detector planes. Data was collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were then combined using two different tomographic methods. Results showed that using a combination of multiple depth-of-field reconstructions, rather than a traditional inverse Radon transform approach used for CT, resulted in more useful images for sparser data. As cosmic ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedge compositions.

Applied Physics (physics.app-ph)↗
DOE OSTI2024

Newton-Raphson AC Power Flow Convergence Based on Deep Learning Initialization and Homotopy Continuation

Power flow forms the basis of many power system studies. With the increased penetration of renewable energy, grid planners tend to perform multiple power flow simulations under various operating conditions and not just selected snapshots at peak or light load conditions. Getting a converged AC power flow (ACPF) case remains a significant challenge for grid planners especially in large power grid networks. This paper proposes a two-stage approach to improve Newton-Raphson ACPF convergence and was applied to a 6102 bus Electric Reliability Council of Texas (ERCOT) system. The first stage utilizes a deep learning-based initializer with data re-training. Here a deep neural network (DNN) initializer is developed to provide better initial voltage magnitude and angle guesses to aid in power flow convergence. This is because Newton-Raphson ACPF is quite sensitive to the initial conditions and bad initialization could lead to divergence. The DNN initializer includes a data re-training framework that improves the initializer's performance when faced with limited training data. The DNN initializer successfully solved 3,285 cases out of 3,899 non-converging dispatch and performed better than random forest and DC power flow initialization methods. ACPF cases not solved in this first stage are then passed through a hot-starting algorithm based on homotopy continuation with switched shunt control. The hot-starting algorithm successfully converged 416 cases out of the remaining 614 non-converging ACPF dispatch. In conclusion, the combined two-stage approach achieved a 94.9% success rate, by converging a total of 3,701 cases out of the initial 3,899 unsolved cases.

Deep learning↗
DOE OSTI2022

Design, Deployment, and Characterization of the World’s First Flexible Large Power Transformer

GE Research and its partner Prolec GE have designed, built and deployed in the field the world’s first flexible power transformer. The flexible power transformer is a transmission class 3-phase autotransformer configurable in impedance and in voltage which allows it to serve as a universal spare for multiple units in a given fleet. However, the key innovation in this new concept is the online adjustable leakage impedance which allows the transformer to change its impedance without interrupting the transmission line operation. The flexible power transformer can be designed with up to three low voltage transmission class ratings and up to 12 impedance values changeable both online and offline. This report provides an overview of the design, manufacturing, testing, and commissioning of the 165kV, 60MVA prototype built including the results of the field performance validation tests. The prototype was specified in collaboration with Cooperative Energy, the utility host. It was designed and tested in the factory according to IEEE standard C57.12.00 and followed all protocols for transportation, installation, and commissioning of a power transformer. In addition to the prototype, a flexible protection system capable of automatically adjusting its settings upon the transformer impedance was also developed and deployed in the for testing and validation. On September 3, 2021 the prototype was energized in Cooperative Energy’s substation in Columbia, Mississippi to become the world’s first flexible power transformer in operation. Its performances and impact on the grid operation were demonstrated through different field tests. Results obtained confirm that the impedance of the flexible transformer can be varied under load through its full range, from 4.3% to 9.3%, without adverse impacts on the line operation, the protection system, the transformer stability and health condition. Results also proved that the flexible transformer is very effective in controlling power transfer through the line or load sharing between units operating in parallel. Indeed, it was proven that higher impedances decrease the thruput power of the transformer while lower impedances increase it. Up to 26MW was controllable on a line loading of 45MVA. It was also possible to demonstrate that the variation of the transformer impedance has no effect on the circulating current between units in parallel, except a minor transient during the impedance change. It was also proven that the flexible protection relay can update its protection settings automatically when the impedance change was detected. The prototype has operated continuously for more than 12 months now with a peak load exceeding 50MVA corresponding to >80% of its ONAN power rating. No alarm, trip or sign of failure has been reported by the utility. In addition to the development and deployment of the flexible transformer prototype, investigations were carried out on new nanodielectric fluids to replace the mineral oil used in power transformers with the goal of reducing their footprint and weight. The key parameters that were targeted for improvement included the breakdown voltage to reduce clearances between windings and tank hence the footprint; viscosity and thermal conductivity to increase the cooling efficiency and therefore to reduce the winding material. Several nanodielectric mixtures with mineral oil including with alumina (Al2O3), titania (TiO2) and Borum Nitrate (BN) with different surfactants have been analyzed and tested. Unfortunately, despite encouraging results no nanofluid candidate has been found viable to replace mineral oil. With the formulations tested, breakdown voltages are generally similar to mineral oil at lower particle contents and worse at higher particle contents. Viscosity appreciably increased at particles concertation over 2 wt% and thermal conductivity increased slightly at 5wt% and appears to be 10-15% higher at 10 wt% particle content. It is recommended to continue investigations to find solutions that can help increase the power density of future flexible power transformers. Flexible power transformers can significantly help the future power grid by providing more flexibility and resiliency. Indeed, by providing voltage and impedance flexibility, flexible power transformers reduce the need for multiple spares, hence inventory costs for utilities. With their online controllable impedance, they can provide support to the grid and help manage short-circuit currents, power flow, line congestion, and grid stability which will become more important with higher penetrations of intermittent renewable resources. During the field validation tests, it was demonstrated that up to 26MW was controllable on a line loading of 45MVA when the transformer impedance was varied from its minimum to its maximum range. Also, with the impedance range, the short-circuit currents could be reduced by up to 38% at the load side of the transformer. With its controllable impedance, flexible transformers can be used in future strategies of grid resilience to help better prepare the grid to face forecasted severe events including storms, heat waves and contingencies. The flexible power transformers can also find role in other applications including high voltage transmission cables such as offshore wind farms where solutions for energizing the cables and managing the reactive power are of critical importance. The designed flexible power transformer is now fully validated and ready for commercialization. Further analysis on the benefits of flexible power transformers for grid stability and short-circuit management including current limiting capability, reclosure and line restoration, control of inrush current, sizing of flexible AC components (FACTS) would help its rapid adoption by the industry.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Towards Construction of a Novel Nanometer-Resolution MeV-STEM for Imaging Thick Frozen Biological Samples

Driven by life-science applications, a mega-electron-volt Scanning Transmission Electron Microscope (MeV-STEM) has been proposed here to image thick frozen biological samples as a conventional Transmission Electron Microscope (TEM) may not be suitable to image samples thicker than 300–500 nm and various volume electron microscopy (EM) techniques either suffering from low resolution, or low speed. The high penetration of inelastic scattering signals of MeV electrons could make the MeV-STEM an appropriate microscope for biological samples as thick as 10 μm or more with a nanoscale resolution, considering the effect of electron energy, beam broadening, and low-dose limit on resolution. The best resolution is inversely related to the sample thickness and changes from 6 nm to 24 nm when the sample thickness increases from 1 μm to 10 μm. To achieve such a resolution in STEM, the imaging electrons must be focused on the specimen with a nm size and an mrad semi-convergence angle. This requires an electron beam emittance of a few picometers, which is ~1000 times smaller than the presently achieved nm emittance, in conjunction with less than 10 -4 energy spread and 1 nA current. We numerically simulated two different approaches that are potentially applicable to build a compact MeV-STEM instrument: (1) DC (Direct Current) gun, aperture, superconducting radio-frequency (SRF) cavities, and STEM column; (2) SRF gun, aperture, SRF cavities, and STEM column. Beam dynamic simulations show promising results, which meet the needs of an MeV-STEM, a few-picometer emittance, less than 10 -4 energy spread, and 0.1–1 nA current from both options. Also, we designed a compact STEM column based on permanent quadrupole quintuplet, not only to demagnify the beam size from 1 μm at the source point to 2 nm at the specimen but also to provide the freedom of changing the magnifications at the specimen and a scanning system to raster the electron beam across the sample with a step size of 2 nm and the repetition rate of 1 MHz. This makes it possible to build a compact MeV-STEM and use it to study thick, large-volume samples in cell biology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a Modular, Crushed-Rock Thermal Energy Storage Pilot Design

The goal of this project, primarily funded by the U.S. Department of Energy, is to design a next-step pilot to advance near-term energy storage integrated with a fossil plant to provide a facility capable of being viable and effective in a market with growing penetration of variable renewable energy (VRE). Thermal energy storage (TES) represents an ideal technology for this purpose. The current effort will complete a feasibility study to prepare for the Phase II pre-front end engineering design (pre-FEED) for implementing a crushed-rock TES system integrated with a natural gas combined cycle (NGCC) plant. The crushed rock storage technology, which is being developed by Brenmiller Energy, is a modular TES system termed bGen™, which can accommodate both thermal and electrical inputs and output steam, hot water, or hot air. For this application, the estimated efficiency is 80% thermal to thermal. The TES technology is being designed to operate on a slipstream from NYPA’s Eugene W. Zeltmann Power Project (Zeltmann) natural gas combined cycle (NGCC) plant. The projected size of the system will be up to 4 MWe with at least 4 hours of storage duration, or 16 MWh-e total. Final sizing is being determined during the ongoing feasibility study. The next-step pilot being designed as part of the current project will represent a 5-fold increase in scale for this TES technology, and will be used to validate the technology’s ability to provide effective and economical energy storage. This pilot would represent the next-to-last demonstration scale before the technology could be commercial ready at GWh-e scales in the 2030 timeframe. This project is expected to design the critical next-step pilot, which will be undertaken in real-world operating conditions to better determine the crushed rock TES technology’s ability to be integrated with an NGCC plant and assess degradation over transient cycling at rates befitting various marketplaces. The crushed-rock TES pilot design is a technically feasible next step in the development of the technology and is built upon years of prior testing and demonstration. The proposed TES technology was developed by Brenmiller over the last 8 years and has been tested in 3 generations of demonstration units at various sites, globally. During this field testing, different characteristics and performance of the technology were validated, including the following key features: energy storage capacity, energy storage charge/discharge rate, energy storage loses, energy storage mechanical testing. This presentation will provide an update on the results of the conceptual design study for the modular crushed-rock TES system, integrated with a NGCC power plant.

03 NATURAL GAS↗
DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗
DOE OSTI2022

Assessing the New Home Market Opportunity: Case Study and Cost Modeling for Solar and Storage in 2030

Residential solar and storage markets are growing in the United States. With approximately 1 million new homes constructed every year, this represents a significant opportunity for solar and storage installations. Some homebuilders have begun to build new homes with solar and storage included as a standard offering. It is not clear how solar and storage is incorporated into the new construction process and at what cost. Further, it is unclear what barriers or opportunities exist to scale this model nationwide. To fill this gap in the literature, this research conducts a case study of Mandalay Homes' new solar and storage community in Arizona to gather lessons learned. From this foundation, we further generate a set of pathways to reduce install costs and expand solar and storage market penetration in this sector. To model existing and 2030 solar and storage costs, we use the National Renewable Energy Laboratory's (NREL's) bottom-up cost model. This modeling is further informed by 12 interviews conducted with new home builders, solar contractors, and other subject matter expert organizations. Our case study analysis generated three key considerations for other homebuilders including: 1. Educating local permitting, inspection, and in some cases utility officials on solar and storage products, designs, and code compliant building practices may be required. The need for education may decline as more local governments and utilities review and approve solar and storage projects. 2. Incorporating solar and storage systems into the homebuilding process can add complexity and related coordination challenges. This does not need to result in home construction delays, but can result in costly contractor "dry runs" to construction sites. 3. Deploying solar and storage at the time of new construction has significant economies of scale, which can improve the value proposition of the systems. The case study, extant literature, and interviews were used to model both existing and future solar and storage installation costs at time of new construction. Here, we find three key cost reduction opportunities relating to solar and battery storage hardware, customer acquisition, and overhead. If future contractors can maximize the cost reduction opportunities outlined here, residential new construction costs could decline by 8 - 25% by 2030, depending on the modeled scenario. Though we expect costs to decline through 2030, it is unclear which of these scenarios may ultimately appear. Interviewees further identified a variety of barriers across each cost category that could temper the savings shown here. At the same time, interviewees described several pathways to scale the new construction solar and storage market, beyond installation cost savings. Interviewees confirmed that changes in finance, rate design, resilience policies, deployment mandates, and DER aggregation could all support more market adoption than seen today. These findings suggest that there are significant opportunities to expand new construction markets and this research can serve as a baseline to assess progress in this segment through 2030.

14 SOLAR ENERGY↗
DOE OSTI2021

Solar +: Clean Energy Strategies for the Sunshine State (Final Technical Report (FTR) of the Florida Alliance for Accelerating Solar and Storage Technology Readiness (FAASSTeR))

This reports on a four-year effort to provide foundational research, analysis, strategies and assistance to help Florida, and other states that might learn from this work, to grow solar energy in conjunction with other distributed energy resources by addressing and overcoming existing barriers, and in way that delivers increased value. The start of this effort coincided with an inflection point of sorts into a new dawn for solar energy in Florida, where the Sunshine state’s national ranking in total installed solar, according to the Solar Energy Industries Association (SEIA), has rose from 13th to 4th. Florida has now become the national leader in annual utility-scale solar growth as dozens of large plants have come online. Also, during this time, Florida utilities have expressed a strong and growing interest in understanding the role of energy storage and how to best plan for and deploy this unique resource as part of strategies to grow solar. The utility-scale solar growth experienced has been fueled by the economics of solar cost-parity with natural gas combined cycle plants and Florida Public Service Commission’s (PSC) approval of cost-recovery for the Investor-Owned Utilities (IOU’s), primarily through the Solar Base Rate Adjustment (SoBRA) mechanism. This has led to gigawatts (GW’s) of rate-based solar capacity additions over several years, along with fairly significant amounts of energy storage. Meanwhile, municipal electric utilities, which, collectively, are the third largest source of power in the state, have been increasing solar considerably through power purchase agreements (PPA’s) and are on track to have close to 1 GW of grid-connected solar by 2024. Florida’s municipal utilities and the Florida Municipal Electric Association (FMEA) have been key partners in the Florida Alliance for Accelerating Solar and Storage Technology Readiness (FAASSTeR), formed to carry out this effort. The six largest of these have been Core Team utilities, engaging throughout the project in weekly calls, discussions, and project direction, participating in and hosting workshops and benefiting from technical assistance in several areas.

14 SOLAR ENERGY↗
DOE OSTI2022

Liquid Salt Combined-Cycle Pilot Plant Design

The work described in this report is responsive to the Office of Fossil Energy program ‘Energy Storage for Fossil Power Generation.’ This Phase I report has been prepared by Pintail Power LLC, with support from Nexant ECA, Electric Power Research Institute (EPRI) and Southern Company Services as a deliverable for the U.S. Department of Energy for NETL Award DE-FE-00320016. The Liquid Salt Combined Cycle™ (LSCC™) technology provides large-scale energy storage integrated with Fossil Electric Generating Units (FEGUs) to meet critical needs in the energy transition by providing: • the lowest cost large-scale storage for time-shifting of renewable energy, • superior fuel efficiency to reduce GHGs from dispatchable resources, • flexible capacity and ramping to balance variability of wind and solar resources, • essential grid stability services to assure reliability of a low-carbon grid. The LSCC approach: • employs equipment that has already been proven in utility service, • uses safe, non-toxic, non-degrading, perpetual-life storage medium, • leverages and repurposes existing FEGU assets, • expands the value stack of energy storage to reduce market, financing, and commodity risks. Pintail Power has developed the LSCC technology to meet the need for reliable, efficient, and cost-effective integration of Variable Renewable Energy (VRE) into a low-carbon electric grid by coupling proven thermal energy storage with proven gas turbines, steam turbines, and heat transfer equipment. This novel approach is intended to address the key issues facing the grid and operators of renewable and fossil generating units including: • Overgeneration and curtailment of renewables, • Need for fast ramping dispatchable resources, • Improved efficiency and flexibility of fossil units, • Additional peaking capacity to support electrification of transportation and heating, • Provision of reliability services to support high penetration of VRE, especially synchronous inertia and fast frequency response. A Technology Readiness assessment by EPRI confirmed that LSCC technology consists of commercially proven hardware used in industrial and utility applications. Although the novel LSCC approach has not yet been demonstrated as a complete system, interfaces between major components have been conservatively specified. A Phase III pilot is planned to demonstrate equipment integration and operation. The patented innovation is removal of the evaporator section from the exhaust heat recovery system, with the evaporation performed by stored energy in a separate steam generator. This arrangement couples renewable and fossil power generation via long-duration energy storage to deliver cost, performance, and operational synergies, including superior charging and discharging flexibility, reduced fuel consumption and lower CO 2 emissions compared to conventional Combined Cycle Power Plants, and low-cost, large-scale energy storage. The LSCC technology is composed of proven equipment integrated with gas turbine exhaust heat in a novel system. During charging, electric heaters raise the salt temperature as it flows from the Cold Salt Tank to the Hot Salt Tank. During discharging, hot salt produces steam from feedwater that is heated with gas turbine exhaust, which also superheats steam to drive a steam turbine. LSCC technology can be added to any combustion-turbine to integrate renewable energy, provide needed grid services, and increase the value of fossil electric generating units based on the technology’s following attributes: • Long-duration storage enables time-shifting of VRE to avoid curtailment and impairment of renewable assets. • Long storage duration combined with fast-charging capability increases arbitrage opportunities by storing more energy when the price is low and discharging more hours when the price is high. • Long storage duration allows resource adequacy to be supplied across multiple days to increase reliability and reduce risk. • The stored energy reduces fuel heat rate and GHG emissions, and increases merit, so the LSCC dispatches earlier and longer to increase the plant’s capacity factor and asset value. • The stored energy enables pre-heating and startup of the steam cycle, without operating the gas turbine, to enable fast startup and ramping when dispatched for discharge. • The steam turbine can operate without the gas turbine so it can provide valuable synchronous inertia during charging without consuming fuel. • Fast frequency response and regulation services can be provided during charging using solid-state heater and pump controls to vary the charge power input in response to grid signals. • The LSCC system can be configured for resilience including black start, islanded/micro-grid operation, and even self-recharging of storage using either gas turbine power or gas turbine exhaust heat. The commercialization plan is to add LSCC technology to existing simple cycle gas turbine power plants with the 50MW GE LM6000 aero-derivative gas turbine as the reference design basis. A Techno-economic assessment of the reference design evaluated the benefits (Levelized Avoided Cost of Energy) and costs (Levelized Cost of Energy). The plant definition included all major systems and budgetary vendor quotes. Pintail Power and NexantECA developed the overall cost estimate for the LSCC plant up to the total plant cost level, following the DOE-NETL cost estimate guidelines at AACE Class 3 (-20%/+30%). This includes the equipment cost, bulk material, direct and indirect labor costs to arrive at the bare erected cost. Engineering costs are factored from the BEC and added to it to arrive at the EPC cost. Process and project contingencies were then factored from the EPC cost and rolled-up to yield the total plant cost of $\$$184 million for 1746 MWh of discharge electricity. • At $\$$105/kWh, the reference plant costs less than any of the Energy Storage Systems evaluated by PNNL in 2020 for the Energy Storage Grand Challenge. Operations and Maintenance cost estimates were scaled from combined cycle practice, assuming that the LSCC unit was co-located with and sharing some labor expense with other units, to arrive at $\$$2.2 million per year. Plant economics were evaluated using prices from the ERCOT Day-Ahead Market for calendar year 2019 (excluding the market disruptions from the COVID pandemic and the February 2020 deep freeze event). Assuming economic dispatch in the ERCOT Day-Ahead market, the reference plant capacity factor would have discharged for 2777 hours at 91.9 MW, a 31.66% capacity factor, with a marginal cost of $\$$25.59/MWh, and a LACE of $\$$82.41/MWh. Fixed charges were calculated according to EIA guidelines to arrive at an LCOE of $\$$83.48. The benefit-to-cost ratio of 0.99 suggests that the reference plant would have been cost-effective and competitive in the market. EPRI interviewed selected utilities to gauge the need for, applicability of and interest in the LSCC system. Several utilities are currently managing increased load growth along with the inclusion of increasing levels of renewable generation, putting pressure on conventional generation by requiring increased turndown requirements and ultimately lower capacity factors. All of the utilities interviewed have CO 2 reduction targets in the 2030-2050 timeframe that will severely limit the participation of fossil generation and require better utilization of carbon free generation. While there is limited opportunity for storage in the current markets, the utilities interviewed stated that there will be a substantial need for long duration energy storage in the future given the expected trends. Utilizing an energy storage system will generally be preferred over new gas capacity in some cases, with the capabilities of the LSCC system being a potential option for retrofit to existing simple cycle gas turbine units, allowing them to deliver greater participation in the market with lower carbon intensity. A technology gap assessment and technology maturation plan identified a pilot-scale demonstration as the final step before commercialization. Key gaps to be addressed during the Phase II FEED (Front-End Engineering Design) are component selection and design, commissioning procedures, and operational procedures and the control system for LSCC charging and discharging. The project team has been expanded to include Wood Group PLC as EPC. The proposed Phase II work leads to a pilot-scale engineering demonstration (TRL 6) to be conducted at Southern Company’s Plant Rowan, where the prototype system will perform “all the functions that will be required of the operational system.” The proposed pilot will facilitate commercialization (TRL-9) by scale-up to utility-scale systems integrated with peaking GTs or directly to facility scale systems using industrial GTs. The conceptual design for the pilot plant focuses on the novel integration aspects of LSCC technology. A slipstream of gas turbine exhaust will feed a waste heat recovery unit coupled to a molten salt steam generator heated by stored energy. The pilot is intended to demonstrate all key operating modes of the LSCC technology during charging, discharging and standby. The pilot equipment will be approximately one-seventh scale of the LM6000 commercial target and is expected to have commercial off-ramp potential for facility-scale applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

A Safe Response to Renewable Energy Hazards

The International Association of Fire Fighters (IAFF) and Underwriters Laboratories, LLC (UL) in conjunction with UL Solutions initiated a joint project in 2022 under an agreement with the United States Department of Energy-Office of Energy Efficiency and Renewable Energy (DOE-EERE). This project focused on two separate and important initiatives related to energy efficiency in residential buildings. Initiative 1: Fire Performance on Energy Efficient Exterior Walls. Initiative 2: Firefighting Tactics in Residential Properties with Building Energy Storage Systems (BESS). The project’s first initiative addresses concerns surrounding new technologies with enhanced, energy-efficient exterior walls installed on residential properties. The concerns of fire rapidly traveling vertically up the exterior of these walls were examined. This addressed a growing concern from the first responder community that many times, the fires on the exterior of residential buildings have already evolved into an attic fire by the time of arrival – making it problematic to address the fire scenario. The test plan for Initiative 1 incorporated a modified version of an American Society for Testing and Materials (ASTM) test method, ASTM E2707, Standard Test Method for Determining Fire Penetration of Exterior Wall Assemblies Using a Direct Flame Impingement Exposure, as the foundation of the research. The test method involved a wall structure intended to represent retrofit construction to evaluate how fire would spread vertically or laterally. The second aspect of the UL-IAFF Project focuses on the fire service response to Residential Battery Energy Storage System (RBESS) incidents. These simulation tests were constructed in the large-scale fire test facility at UL Solutions’ Northbrook, IL campus. A baseline test was conducted that involved a test structure with no batteries—shelving units populated with standardized commodities, representing a typical garage with cellulosic and plastic contents. Three additional tests have been conducted to generate data with the contribution of energy storage system (ESS) batteries to compare fire and explosion hazards against the baseline test. Through this work, fire service tactical considerations can be explored. From the data, the team can determine 1) the visual indicators of a residential fire that has involved an RBESS (or, potentially, other large batteries) and 2) the impact of fire service-initiated ventilation of the structure on the fire conditions and explosion risks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗