Results for “cost model” — Page 66

DOE OSTI2020

Economic Analysis of Integrated Solar Power, Hydrogen Production, and Electricity Markets

Hydrogen is a versatile energy carrier that is used in a wide variety of chemical and industrial processes. Producing hydrogen using electrolysis can enable integration of multiple sectors including electricity, heating, and industrial sectors; however, the cost of producing hydrogen from electrolysis remains a challenge for encouraging greater adoption. To help improve the economics for both solar PV and hydrogen production using electrolyzers, we explore the benefit of combining PV and electrolysis systems. Using the Revenue Operation and Device Optimization Model (RODeO) model, the optimal breakeven hydrogen production cost for six unique market participation configurations is calculated at six candidate locations in California where PV is already installed. Costs include production, storage, and compression in preparation for gaseous delivery trucks. Revenue streams included in the optimization are the sale of hydrogen, Low Carbon Fuel Standard (LCFS) credits, renewable electricity sold to the grid, and Renewable Energy Credits (REC). The costs included are the electricity costs, capital and fixed operation and maintenance cost (FOM) for the electrolyzer, PV, and storage and compression systems as well as taxes and financing costs. In addition, cost reductions are achieved through retail and wholesale electricity use optimization, by which electricity is purchased at the lowest price and sold, if possible, at the highest price. For all locations analyzed, the breakeven hydrogen production cost results show that, in order of decreasing cost, the system configurations are islanded (highest), separated, NEM, retail, hybrid retail/wholesale, and wholesale (lowest). This report also explores other aspects of hydrogen systems including optimal renewable sizing and resulting energy mixture to the electrolyzer, value of renewable premiums, competition with incumbent technologies, cost sensitivity to a variety of parameters.

decarbonized economy↗

DOE OSTI2023

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

DOE OSTI2024

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

DOE OSTI2024

Sensorium: commissioning abundant sensors with augmented reality and QR codes

In the future, it will be possible to build high-quality models of building interiors based on data from a dense fleet of sensors reporting on air volumes much smaller than a room or zone. To enable such models, we are creating technologies that allow a fleet of sensors to be commissioned quickly at low cost. Our sensor commissioning process builds a 3D model of each building interior that includes sensor positions and sensor networking information such as sensor MAC addresses. It employs multiple technologies, including augmented reality, LiDAR, QR codes, and computer vision. Sensors can be commissioned at more than 10x the speed and at less than one tenth the cost of traditional approaches.

Bier, Eric A.↗

DOE OSTI2025

Spokane Eco-District Campus Performance Under Alternative Electricity Rates: Benefits for virtual power plant participants and suppliers

Here, the respective benefits for virtual power plant participants and suppliers are revealed and compared under alternative electricity rate structures, including conventional large commercial electricity rates, large commercial electricity rates with special rates for demand-side generation, and dynamic hourly transactive prices. These three scenarios were explored using the capabilities of the Eco-District campus, a virtual power plant in Spokane, Washington, that is supplied electricity by Avista Utilities. The Eco-District Campus was modeled to host solar power generation, battery energy storage, and thermal energy resources that must be coordinated with building heating and cooling needs. First, the electricity supplier’s costs for energy, infrastructure, and energy losses were modeled. Then, the virtual power plant’s performance was modeled while presuming that its manager would minimize its costs under its electricity rate structure. The demand charges of conventional commercial electricity rates managed monthly peak, as would be expected, but hourly dynamic transactive pricing resulted in a striking alignment between the costs incurred by the supplier and the virtual power plant’s energy costs.

Electricity rates↗

DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Developing a Reduced 240-Bus WECC Dynamic Model for Frequency Response Study of High Renewable Integration

The ongoing changes in the generation resource mix, driven by the rapid adoption of inverter-based resources (IBR) as well as the early retirement of synchronous generators, are bringing new challenges to the planning and operation of bulk electric power systems. Consequently, there is an increasing need to understand, design, and quantify the reliability service provision from IBRs by performing integrated scheduling and dynamic simulations. However, test systems that have consistent scheduling and dynamic models rarely exist largely because of the decoupled nature of the two simulations on a synchronous generator-dominated system. This paper develops the dynamic model of a reduced Western Electricity Coordinating Council (WECC) system. In conjunction with the existing scheduling model, it is suitable for integrating scheduling and dynamic simulations. The reduced 240-bus WECC model reflects the generation resource mix of the Western Interconnection as of 2018. Moreover, the developed dynamic model is validated against field frequency events measured by FNET/GridEye and preserves the dominant inter-area oscillation mode in WECC.

dynamic simulation↗

DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗

DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Integrated Process Model Utilization and Development for Inertial Fusion Energy

This CRADA between LLNS and Longview Fusion Energy Systems utilized a modernized LLNL Integrated Process Model (IPM) to assess technoeconomic viability and power plant configurations for Inertial Fusion Energy (IFE). During the collaboration period, a streamlined IPM model was produced by LLNL, which consolidated a combination of 3 models under one file. Additionally, obsolete parameters were removed and parameters available for trade space analysis were organized in a user-friendly fashion on the front interface. Gain scaling curves were discussed and analyzed in the framework of the IPM. Additional updates were made to account for cost scaling to today's dollars using an average inflation rate. Heat transfer material costing was updated using a ground up approach with public vendor data. Further levelized cost of electricity (LCOE) methods and models were discussed and reviewed for applicability to the IPM. Prior published literature on optical scaling was also discussed. Moreover, as part of this CRADA, the Participant developed a fusion technology development roadmap and point designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Techno-Economic Implications of Electrical Machine Scaling for Wave Energy Converters: Preprint

The sizing of an electrical machine for a Wave Energy Converter (WEC) can have a substantial impact on the overall sizing, cost, and rating of the device. An electrical generator is typically part of the power take-off (PTO) system, which is the mechanism by which the energy absorbed by the prime mover is transformed into useable electrical. For practically all WECs, the rate of change of actuation is predominantly determined by the wave resource (i.e., the wave height and frequency) and devices will see a sinusoidally varying velocity according to the wave conditions. The same can then be said for both directly and indirectly coupled PTOs with electrical generators. This techno-economic study investigates electrical machine scaling and associated cost implications through core machine design theory, manufacturer data, supporting literature, and the DOE sponsored Reference Model Project (RMP). The RMP was a partnered effort to develop open-source marine energy (ME) point designs as reference models (RMs) to benchmark ME technology performance and costs, methods for design and analysis of ME technologies, estimations for capital costs, operational costs, and levelized costs of energy (LCOE). The results from this study show torque is directly related to (1) the physical size of the machine required to increase the airgap sheer stresses, (2) the amount of active material, (3) the support structure, (4) bearing size and rating, and (5) offshore cable rating, all of which have a significant effect on overall system costs in terms of both CAPEX and OPEX. This paper aims to be a critical benchmark in helping determine an “optimal” nameplate rating for wave energy devices and their associated PTO. With an optimized rating and sizing process, WEC costs can be reduced, and overall performance can be improved.

cables↗

DOE OSTI2022

Techno-Economic Implications of Electrical Machine Scaling for Wave Energy Converters

The sizing of an electrical machine for a Wave Energy Converter (WEC) can have a substantial impact on the overall sizing, cost, and rating of the device. An electrical generator is typically part of the power take-off (PTO) system, which is the mechanism by which the energy absorbed by the prime mover is transformed into useable electrical. For practically all WECs, the rate of change of actuation is predominantly determined by the wave resource (i.e., the wave height and frequency) and devices will see a sinusoidally varying velocity according to the wave conditions. The same can then be said for both directly and indirectly coupled PTOs with electrical generators. This techno-economic study investigates electrical machine scaling and associated cost implications through core machine design theory, manufacturer data, supporting literature, and the DOE sponsored Reference Model Project (RMP). The RMP was a partnered effort to develop open-source marine energy (ME) point designs as reference models (RMs) to benchmark ME technology performance and costs, methods for design and analysis of ME technologies, estimations for capital costs, operational costs, and levelized costs of energy (LCOE). The results from this study show torque is directly related to (1) the physical size of the machine required to increase the airgap sheer stresses, (2) the amount of active material, (3) the support structure, (4) bearing size and rating, and (5) offshore cable rating, all of which have a significant effect on overall system costs in terms of both CAPEX and OPEX. This paper aims to be a critical benchmark in helping determine an “optimal” nameplate rating for wave energy devices and their associated PTO. With an optimized rating and sizing process, WEC costs can be reduced, and overall performance can be improved.

cables↗

DOE OSTI2023

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero-carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

A fast reduced model for a shell-and-tube based latent heat thermal energy storage heat exchanger and its application for cost optimal design by nonlinear programming

Numerical simulation of latent heat thermal energy storage (LHTES) systems plays a fundamental role in studying the physical process and guiding the engineering design. Discretization of the PDEs describing the nonlinear solidification/melting process of phase change materials (PCMs) leads to a large-scale complex dynamics system, where the system behavior depends on a set of parameters. In a design setting, repeated model evaluations are required over the set of parameters results in significant computational burden. In this paper, an explicit analytic solution was built for the propagation of the solidification front in a cylindrical coordinate. The analytic solution approach is further employed to develop a low computational reduced model (RM) as a module for a shell-and-tube based LHTES heat exchanger. The levelized Cost of Energy (LCOE) is used as a design metric and the RM model is used to apply system-level constraints in the nonlinear programming formulation that facilitates efficient global optimal design of the PCM properties, flow conditions and tube geometries. The use of LCOE as the design metric prevents over design of the heat transfer rate and also establishes a fair ground for evaluation of different thermal storage technologies and their integrated applications with other systems. Optimal results showed that a higher effectiveness results in a higher LCOE; the velocity of the HTF and the length of the channel are highly correlated with each other; both larger PCM latent energy and conductivity result in lower LCOE.

25 ENERGY STORAGE↗

DOE OSTI2022

Technoeconomic Cost Analysis of NREL Concentrating Solar Power Gen3 Liquid Pathway

The National Renewable Energy Laboratory is leading the liquid (molten salt) power tower pathway for the U.S. Department of Energy's concentrating solar power Gen3 initiative. The Gen3 liquid pathway required updated designs to three major components: the tower and receiver, the thermal energy storage tanks, and the power cycle. We assume a 100 MWe net system output and used the System Advisor Model (SAM) to complete a technoeconomic cost analysis of the Gen3 liquid pathway design and estimate its levelized cost of electricity. This paper summarizes the methodology and results of that analysis.

chloride salt↗

DOE OSTI2024

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

STREAM: A technology planning and capacity expansion model for the industrial sector

The Strategic Technology Roadmapping and Energy, Environmental, and Economic Analysis Model—STREAM—is an optimization-based modeling tool and analysis framework to assist with strategic planning and technology investments of the industrial sector. This open-source framework is written in Julia using the JuMP package, which enables users to model future “pathways” for incumbent and future production technologies, costs, fuels and energy carriers, and energy and non-energy environmental impacts from industries as they transform in pursuit of a robust and competitive manufacturing sector. The model starts with an initial stock of industrial production technologies and assets at a facility level and then determines pathways that minimize cost, subject to an array of possible constraints on demand, market shares, environmental flows, and other exogenously specified operational considerations such as capacity utilization rates or regional energy costs. Key features of the framework include flexibility to model a wide range of industries and industrial technologies/processes at varying levels of granularity, ability to perform parametric sensitivity analyses, and ability to visualize model results using visualization objects.

capacity expansion↗

DOE OSTI2021

Evaluation of the potential for wake steering for U.S. land-based wind power plants

The wind power plant-wide control strategy known as wake steering involves the misalignment of upstream turbines with the wind direction to deflect wakes away from downstream turbines, increasing net wind plant power production. In this paper, we evaluate the potential of wake steering for U.S. land-based wind power plants. First, we outline a method for simulating and optimizing wake steering control for existing wind plants by combining the flow redirection and induction in steady state wake steering engineering model with the U.S. Wind Turbine Database and Wind Integration National Dataset Toolkit wind resource dataset. Next, to better understand the potential benefits of wake steering beyond those for existing wind plants, we evaluate the relative impacts of turbine specific power, turbine spacing, and mean wind speed on energy gain and levelized cost of energy (LCOE) using a model land-based wind power plant. For a subset of 60 existing wind plants, assuming a constant turbulence intensity of 8%, wake steering was found to yield an average annual energy production (AEP) gain of 0.80%, equivalent to recovering 13.85% of baseline wake losses. Further, we present a linear approximation between baseline wake losses and AEP gains that can be used to estimate wake steering gains for other wind power plants. Highlighting additional benefits of wake steering, for the model wind power plant we found that energy gains from wake steering enabled an approximate 30% reduction in turbine spacing while keeping LCOE constant.

17 WIND ENERGY↗