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

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Utility-Scale Solar, 2022 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2022 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2021, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

2022↗
DOE OSTI2023

Utility-Scale Solar, 2023 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2023 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2022, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

analysis↗
DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗
DOE OSTI2023

AI-Based EMT Dynamic Model of PV Systems

Several electromagnetic transient (EMT) dynamic modeling methods are available to model systems like photovoltaic (PV) plants, wind power plants, variable-speed drives, among others. The methods include: (a) physics-based models and (b) data-driven models. The physics-based dynamic models may include high-fidelity switched system model and average-value model that both require the control algorithms included in the models. However, manufacturers typically prefer to provide black-box models to avoid disclosing proprietary. One of the solutions to prevent disclosing control algorithms is the use of data-driven dynamic EMT models of PV systems. In this paper, data-driven dynamic EMT model based on artificial intelligence (AI) algorithms are presented. The AI algorithms evaluated include convolutional neural networks, recurrent neural networks, and nonlinear auto-regressive exogenous model. Automation in generating data and training these models is also discussed in this paper. The results generated by the best AI algorithms have been observed to be greater than 95 % accurate.

Debnath, Suman↗
DOE OSTI2021

High-Fidelity Models and Fast EMT Simulation Algorithms for Isolated Multi-port Autonomous Reconfigurable Solar power plant (MARS)

The integration of hybrid photovoltaic (PV) and energy storage system (ESS) based plants has become a promising way of solving the intermittency of PV plants and providing frequency support to the power grid. The multi-port autonomous reconfigurable solar power plant (MARS) can integrate the PV systems and ESSs to an ac grid and dc lines. The proposed isolated MARS incorporates an isolated converter that connects to the PV arrays and is based on the dual active bridge (DAB) converter. The high frequency switching in the DAB and the means to control the DAB converter using delays between switching signals lead to the need for a small timestep in simulations. Moreover, several hundreds of modules that include a DAB converter are present in the MARS. The small timestep and the presence of several hundreds of modules lead to a significant rise in the overall simulation time. To address this issue, simulation algorithms like numerical stiffness-based hybrid discretization and the hysteresis relaxation technique are applied to the switched system model of isolated MARS. Additionally, an event-driven interpolating method is introduced to help increase the minimum timestep to simulate the conventional DAB converter model while maintaining high accuracy in the simulation results. The developed model is validated by comparison with its reference model built in the PSCAD/EMTDC and MATLAB software environments using library components.

Xia, Qian↗
DOE OSTI2024

Techno-Economic Analysis of Greenfield Geothermal Hybrid Power Plants using a Solar or Natural Gas Steam Topping Cycle

The relatively low generation costs associated with wind, solar photovoltaic (PV), and natural-gas power plants make it challenging for geothermal power plants to produce and sell the power that has the reliability and sustainability characteristics that are greatly needed in U.S. power markets. This is especially true for geothermal resources with low-to-medium temperatures, which results in relatively low-thermal efficiency and generation costs that are higher than those for wind, solar PV, and natural gas. This analysis evaluates solar thermal- and natural-gas combustion waste heat recovery-based topping cycle hybridization of geothermal binary power plants. This approach provides several benefits that may allow geothermal power plants to generate power at more competitive costs. First, the addition of solar thermal energy or natural-gas combustion waste heat input to a geothermal power plant provides additional heat input that can be converted to electrical power. Second, the temperature level of the heat obtained from concentrating solar collectors or natural-gas combustion exhaust is higher than that of geothermal heat, which provides opportunities for improving the efficiency of the conversion of thermal energy to electrical power. Third, the ease with which solar thermal systems integrate with energy storage and the flexibility of natural gas means power generation can occur during peak demand periods. The hybrid cycles are compared to equivalently sized, co-located, independent geothermal, concentrating solar, and/or natural-gas power plants. The hybrid cycle tends to produce slightly more power than the standalone plants combined. However, the hybrid plant Levelized Cost of Energy (LCOE) is slightly higher than the LCOE of the combined standalone power plants for each of the case study locations investigated. Using the steam-topping cycle, organic Rankine cycle (ORC)-bottoming cycle hybrid plant design to combine a solar thermal resource and low- temperature geothermal resource (<120 degrees C) leads to a hybrid plant with a lower LCOE than a standalone geothermal-only system. Thus, hybrid plants may enable the economic development of geothermal resources in locations with low geothermal resource temperatures. However, in areas with higher geothermal resource temperatures (>120 degrees C), the geothermal-only plant has a lower LCOE than the hybrid cycle and thus could be developed without the need for solar heat addition. iv A geothermal-natural-gas reciprocating engine hybrid plant was evaluated for an Elk Hills, California case study location. The Elk Hills case study analysis indicates that when the natural-gas engine operates for more than 12 hours per day the hybrid plant can produce power at an LCOE lower than a standalone geothermal plant, and comparable to that of the standalone natural-gas reciprocating engine, while also reducing the carbon intensity of the power generated relative to the standalone natural-gas engine. This may represent a scenario in which the hybrid plant provides an opportunity for the deployment of a low-temperature geothermal resource that otherwise may have an LCOE too high to develop and operate as a standalone resource, while also reducing the carbon intensity of natural-gas generation sources. A "triple-hybrid" plant that combines natural gas, solar thermal, thermal energy storage, and geothermal was also investigated. A natural-gas combustion turbine (NGCT) is added to the geothermal-solar hybrid such that the hot exhaust gas from the gas turbine provides an alternative source of heat to the steam turbine of the hybrid cycle. Analysis results suggest that the triple-hybrid plant has a significantly higher energy generation and revenue than a standalone NGCT or the original geothermal-solar hybrid. The triple-hybrid design benefits most from using a smaller solar field so that the solar energy can be dispatched at the most valuable times available. The triple-hybrid plant also has a lower LCOE than the standalone NGCT. The triple-hybrid plant was evaluated making simple assumptions about the dispatch profile of the gas cycle, and more nuanced and realistic dispatching schedules should be analyzed in future work.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
DOE OSTI2024

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

14 SOLAR ENERGY↗
DOE OSTI2023

Design and Techno-Economic Analysis of a 150-MW Hybrid CSP-PV Plant

The interest in concentrated solar power (CSP) has increased significantly over the years since it is dispatchable and requires thermal storage instead of electric storage. When compared to photovoltaics (PV), CSP has a higher Levelized Cost of Electricity (LCOE). In this paper, we present the design of a hybrid power plant using CSP and PV technologies. The hybrid system offers a lower LCOE than CSP but will still have dispatchability. The 150-MW hybrid system consists of 100-MW CSP and 50-MW PV capacity. Furthermore, the CSP system (central receiver system) has a molten salt-based thermal storage of 12 hours. The geographical focus of this study is Pakistan, which is a developing country struggling with energy crises, but which has high solar potential. The hybrid system is modeled using the System Advisor Model (SAM). The results show that by hybridizing CSP with PV, the LCOE can be reduced by 18.5%.

concentrated solar power↗
DOE OSTI2023

AI & Physics-Based Bad Command/Data Detection in Large Power Electronics Systems: Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS)

Detection of bad data from measurement sensors and bad commands from control centers need to be carried out to avoid instabilities within large power electronics systems. Towards the same, in this paper, model and data driven methods are proposed to identify anomalies in measured data and commands received by large power electronics systems. The large power electronics system considered in this paper is a multi-port autonomous reconfigurable solar power plant (MARS), which consists of photovoltaic (PV) and energy storage systems (ESSs) that connect to high-voltage direct current (HVdc) system and transmission ac power grid. The proposed algorithms in the MARS power plant to detect bad data from measurements and bad commands from control centers are evaluated in simulations and hardware-in-the-loop (HIL) tests. Furthermore, it has been observed that the proposed algorithms are able to detect bad measurements and commands in all the use cases evaluated.

14 SOLAR ENERGY↗
DOE OSTI2024

Barriers and variable spacing enhance convective cooling and increase power output in solar PV plants

When the temperature of solar photovoltaic (PV) modules rises, efficiency drops and module degradation accelerates. Thus, it is beneficial to reduce module operating temperatures. Previous studies of solar power plants have illustrated that incoming flow characteristics, turbulent mixing, and array geometry can strongly impact convective cooling, as measured by the convective heat transfer coefficient h. In the fields of heat transfer and plant canopy flow, previous work has shown that system-scale arrangement modifications—e.g., variable spacing, barriers, or windbreaks—can passively alter the flow, enhance turbulent mixing, and influence convection. However, researchers have not yet explored how variable spacing or barriers might enhance convective cooling in solar power plants. Here, high-resolution large-eddy simulations model the air flow and heat transfer through solar power plant arrangements modified with missing modules and barrier walls. We then perform a control volume analysis to evaluate the net heat flux and compute h, which quantifies the influence of these spatial modifications on convective cooling and, thus, module temperature and power output. Installing barrier walls yields the greatest improvements, increasing h by 3.4%, reducing module temperature by an estimated 2.5 °C, and boosting power output by an estimated 1.4% on average. These findings indicate that incorporating variable spacing or barrier-type elements into PV plant designs can reduce module temperature and, thus, improve PV performance and service life.

14 SOLAR ENERGY↗
DOE OSTI2022

Solar Radiometer Instrumentation Evaluation: Cooperative Research and Development (Final Report, CRADA Number CRD-16-00619)

The purpose and intent of this agreement is to evaluate newly manufactured thermopile pyranometers and spectroradiometers. The purpose also extends to provide a framework for Participant to conduct research to improve and develop new radiometric devices and application in the future. The overall objective is to provide more accurate, site-specific, long-term, continuous measurements of the solar resources needed by industry to increase the deployment and improve the operations of photovoltaic and concentrating solar power plants. This CRADA addresses the needs for proven solar irradiance measurement to validate resource assessment models and generate high quality data used for site selection, energy system design, deployment, maintenance, and operation. This work will be conducted at NREL and Participant facilities. This project will place instrumentation at the NREL Solar Radiation Research Laboratory (SRRL) in cooperation with EKO Instruments, USA. Participant instruments will be deployed for the purpose of evaluation under controlled conditions. The scope of the project will be a 3 years-long segmented comparison of the instruments vs. other NREL baseline instruments with a well-characterized history. These evaluations will include planned improvements to instruments as well as extensions of future instrument applications. The final evaluation will be a written report similar to the work done by Wilcox and Myers (see http://www.nrel.gov/docs/fy09osti/44627.pdf).

14 SOLAR ENERGY↗
DOE OSTI2024

Utility-Scale Solar, 2024 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Berkeley Labs "Utility-Scale Solar", 2024 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2023, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, net value, and interconnection queue data.

analysis↗
DOE OSTI2021

Degradation mechanisms and partial shading of glass-backsheet and double-glass photovoltaic modules in three climate zones determined by remote monitoring of time-series current–voltage and power datastreams

Degradation and partial shading impact the long-term reliability and power production of photovoltaic (PV) modules and power plants. Time-series power (P mp ) and current–voltage (I-V) curve datastreams from PV modules enable a remote diagnostic approach to quantify active degradation mechanisms and identify partial shading. We study three to nine years of these datastreams, including 3.6 million P mp I-V curves and 36 million values, from eight PV modules, four each of double-glass and glass-backsheet module architectures, located in three distinctly different Köppen-Geiger climate zones, to determine the module’s performance loss rates (PLR), identify active degradation mechanisms and power loss modes, along with partial shading by local objects. Considering both module architectures, PLR results indicate that the BSh climate zone is the most aggressive for module degradation, while the Alpine ET zone is the mildest climate. PLR of double-glass modules located in BWh and BSh climate zones are different due to the significantly greater uniform current loss (ΔP Isc ) for double-glass modules in BSh, at a 5% significance level. Power loss for four out of five modules located in the BWh and BSh climates are dominated by uniform current degradation. Statistical analysis of multistep I-V curves detects partial shading experienced by three studied modules with details of the shading profile, the shading Poynting vector diagram for the obstacle’s relative position, shading scenarios, and duration. Lastly, this work demonstrates how remote monitoring and diagnosis of P mp & I-V time-series of modules can provide quantitative operations and maintenance insights into system performance, degradation mechanisms, and shading.

14 SOLAR ENERGY↗
DOE OSTI2022

Benchmarking of Solar Irradiance Nowcast Performance Derived from All-Sky Imagers

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.

all-sky imagers↗
DOE OSTI2023

Analysis of Nuclear Fuel Cycle Data

Electricity generated using nuclear power accounted for 18.9% of all electricity consumed in the United States in 2021, putting it in third place behind natural gas (38%) and coal (22%) power plants. Nuclear power plants boast a significantly higher uptime or capacity factor—90% and above—compared to 49.1% for coal fired power plants and 56.6% for natural gas power plants. Renewable energy sources, such as solar photovoltaic (PV) and wind electricity, have lower capacity factors: 24.9% and 36.3%, respectively. In addition, nuclear power is cleaner than both coal and natural gas fired power plants. With the passing of the 2022 Inflation Reduction Act, significant tax credits will be claimed by producers of hydrogen with well-to-gate greenhouse gas (GHG) emissions below 0.45 kg CO 2e /kg H 2 . This has sparked interest in using clean sources of electricity, including nuclear power, to generate H 2 via water electrolysis. As uranium is a primary fuel for modern nuclear power plants, the upstream emissions from nuclear fuel production greatly impact the GHG emissions related to all nuclear power end use. Therefore, it is important to accurately determine the upstream emissions associated with the nuclear fuel cycle of nuclear power production in the United States. In this analysis, the nuclear fuel cycle was separated into distinct steps to allow better understanding of the chemical and energy inputs at each step of the fuel cycle. This also provides details of the GHG emissions at each step in the nuclear fuel cycle. The transportation distance for each step of the fuel cycle was updated to account for the locations of uranium processing facilities along the supply chain of the current U.S. nuclear power plants. Finally, all the updated values were incorporated into Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗