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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sandia Contributions to WaterTAP

SAND2023-06591O Through National Alliance for Water Innovation funding, Sandia will be contributing to the Water treatment Technoeconomic Assessment Platform (WaterTAP) project. WaterTAP is an open-source, Python-based software package that supports the technoeconomic assessment of full water treatment trains. WaterTAP includes a modular water treatment model library spanning a broad set of water treatment processes composed of unit, property, and costing models.

Rawlings, EdnaSoraya↗
DOE OSTI2020

Cost Efficient and Highly Weather-Resistant Solar Panel Backsheet Produced through Continuous Co-Extrusion Processing

The efficient generation and delivery of ‘clean energy” through photovoltaic (PV) technology relies heavily on the cost, performance and reliability of solar panels, along with the individual components used in assembly of those panels. In order to continue towards the Department of Energy’s (DOE) goals of driving costs down, the lifetime of high-output power generation equipment (i.e. solar modules) must be extended to reduce costs associated with power output degradation and replacement of failed or degraded panels. One of the major sources of module failures in the past 30 years has been the failure of the solar panel backsheet. Failure of this component causes severe output losses due to oxidation and yellowing of the module while creating safety concerns as the major electrical insulation of the module is compromised. Tomark-Worthen LLC was formed in 2012 with the goal of designing and manufacturing novel PV backsheets and encapsulants that would be produced in the United States of America and exceed expectations of domestic and international module producers. These backsheets would improve upon known backsheet design failures (i.e. Isovoltaic’s AAA backsheet) and create domestic manufacturing jobs in the PV sector. This goal became a reality with a first-generation product in 2018 and now, with the support of the DOE, Tomark-Worthen has launched a second-generation product known as PhotoMark® Reflections™ 205-3 and 360-3. This 2+ year effort began in late 2017 with a screening of potential polymeric materials and backsheet structural designs. Performance evaluations of each material provided the necessary data needed to select the proper material combinations and move forward into the design of the manufacturing process. These decisions were made by teaming with a local university, well-respected government laboratories, and private research organizations who provided the tools necessary to predict long-term performance of these materials in much shorter periods of time. Once the go/no-go criteria were met at the mid-point of the project, the effort transitioned to predicting 30+ year performance, achieving certifications, and improving manufacturing efficiencies designed to lower costs in order to be competitive in a cost-driven market. Major project accomplishments include recognition of the new backsheet by Underwriters Laboratories (UL) and international certification for 1000V and 1500V modules by TUV Sud. Both 1000V and 1500V products have been proven on several module manufacturing lines and initial customer orders have been received by both domestic and international module producers. Accelerated exposure testing has shown that the performance of this backsheet exceeds the prior polyamide-based backsheet known to fail in the field. Cost-models and initial customer orders have shown the ability to produce this backsheet at a cost acceptable to many domestic manufacturers, while International costs for standard backsheet remains extremely low. Tomark-Worthen remains one of the only U.S. companies manufacturing backsheet domestically and this has drawn interest from many domestic module manufacturers looking to improve their material supply logistics by avoiding long lead times and custom’s headaches while avoiding current tariffs on Chinese-made components. Along with the launch of a new PV backsheet, this project has provided a critical deliverable to the PV R&D community. This deliverable is the knowledge that not all polyamide-containing backsheets are doomed to failure as the earlier versions did. There has been a fear amongst the PV community to consider these materials due to the failure of one design. This effort has brought a new level of interest to the scientific community as it has been shown that polyamides can be used successfully in a well-designed backsheet. As we move forward from this project, we are excited to see how backsheet technology and manufacturing in the U.S. will continue to progress and grow.

14 SOLAR ENERGY↗
DOE OSTI2022

Integrated Heat Pump Thermal Storage and Power Cycle for CSP (Final Technical Report)

Pumped thermal energy storage (PTES) is a storage system that stores electricity in thermal reservoirs. In this project, methods of integrating PTES with concentrating solar power (CSP) systems were investigated and their feasibility evaluated. Hybrid "solar-PTES" devices can provide both flexible renewable power generation as well as a variety of electricity storage services. Techno-economic models of PTES and solar-PTES were developed and used to assess their performance, cost, and commercial viability. Systems were optimized using multi-objective optimization techniques. The value that these storage devices provide to the grid was estimated using production cost modelling. Key advances made in this project include the development of detailed techno-economic models that improve state-of-the-art assessments of these technologies. Off-design models were developed and used in grid-analysis models. This work describes for the first time how PTES and solar-PTES behave over a range of operating points and assesses the value that these systems can provide to the electrical grid. Feedback was obtained from experts in the field, which clarified key technological and economic barriers that must be addressed to improve the feasibility of these devices.

14 SOLAR ENERGY↗
DOE OSTI2026

Hydrogen Production Cost from Proton-Conducting Solid Oxide Electrolysis

Rigorous stakeholder-vetted techno-economic analysis (TEA) was conducted to estimate the cost of hydrogen (H 2 ) production using Proton-Conducting Solid Oxide (PSO) electrolysis. The analysis evaluates Current (2025) and Future (2035) technology cases at centralized plant scales of 50 and 500 metric tonnes per day (MTD), assuming electricity, water, and air as the only system inputs. Untaxed, unsubsidized levelized cost of hydrogen (LCOH) is projected to range from 2020 $\$$1.81 to $\$$2.47/kg H 2 at an electricity price of $\$$0.03/kWh and 97% capacity factor under Nth-of-a-kind (NOAK) deployment assumptions. Total installed capital cost was developed using bottom-up Design for Manufacture and Assembly (DFMA) stack cost modeling and detailed balance-of-plant estimates, including mechanical and electrical subsystems, installation, site preparation, engineering, and contingency. Stack performance assumptions include thermoneutral operation, degradation over time, and periodic replacement. LCOH was calculated using the Hydrogen Analysis (H2A) discounted cash flow model in constant 2020 dollars. Results indicate PSO electrolysis has potential for competitive hydrogen production costs under low-cost electricity and mature manufacturing conditions.

08 HYDROGEN↗
DOE OSTI2024

Solar Photovoltaic and Storage Supply Chains and Technology and Market Opportunities

This talk will highlight the most recent efforts from the National Renewable Energy Laboratory (NREL) to track solar photovoltaic (PV) and storage supply and demand in the United States and globally, as well as bottom-up calculations of manufacturing costs for facilities across the globe. We will begin with an overview of the global solar PV supply chain and 2022 benchmark input data used for NREL's bottom-up crystalline silicon (c-Si) and thin film PV module manufacturing cost models. For the polysilicon, wafer, cell conversion, and module assembly steps of the c-Si supply chain, and for thin film modules, we will review the industry-collected input data and methods used for calculating the costs of goods sold (COGS); research and development (R&D) expenses; and sales, general, and business administration (S, G, and A) expenses. This 2022 benchmark analysis is compiled for state-of-the-art c-Si and thin film PV module manufacturing in several countries and regions; and will also include a quantified summary of the impacts of the manufacturing incentives and tax credits that are available for solar manufacturing and installations within the United States. Next generation technologies that lower PV manufacturing and installation costs, reduce operations and maintenance (O&M) expenses, and improve system energy yield will also be highlighted. We will conclude with projections of solar market penetration to 2050 from NREL's Solar Futures Study and Annual Technology Baseline (ATB) model, which includes solar coupled with lower-cost storage scenarios as well as the range of future cost scenarios for other power generation sources. We look forward to sharing NREL's extensive work in these areas and discussing ideas for future directions.

economics↗
DOE OSTI2024

RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing

Ensuring high-quality recommendations for newly onboarded users requires the continuous retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data. To serve the online DLRM retraining, existing solutions use hundreds of CPU computing nodes designated for input preprocessing, causing significant power consumption that surpasses even the power usage of GPU trainers. To this end, we propose RAP, an end-to-end DLRM training framework that supports Resource-aware Automated GPU sharing for DLRM input Preprocessing and Training. The core idea of RAP is to accurately capture the remaining GPU computing resources during DLRM training for input preprocessing, achieving superior training efficiency without requiring additional resources. Specifically, RAP utilizes a co-running cost model to efficiently assess the costs of various input preprocessing operations, and it implements a resource-aware horizontal fusion technique that adaptively merges smaller kernels according to GPU availability, circumventing any interference with DLRM training. In addition, RAP leverages a heuristic searching algorithm that jointly optimizes both the input preprocessing graph mapping and the co-running schedule to maximize the end-to-end DLRM training throughput. The comprehensive evaluation shows that RAP achieves 78.3× speedup on average over CPU-based DLRM input preprocessing frameworks. In addition, the end-to-end training throughput of RAP is only 2.04% lower than the ideal case, which has no input preprocessing overhead.

Wang, Zheng↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

On the operational characteristics and economic value of pumped thermal energy storage

Pumped thermal energy storage (PTES) systems use an electrically-driven heat pump to store electricity in the form of thermal energy, and subsequently dispatch the stored thermal energy to generate electricity using a thermodynamic heat engine. Optimal day-ahead operational scheduling and annual value of a PTES system based on Joule-Brayton thermodynamic cycles and two-tank molten salt hot thermal storage is evaluated in this work. Production cost models, which simultaneously optimize commitment and dispatch schedules for an entire set of generators to minimize the cost of satisfying electricity demand, are employed to determine system-optimal operation and day-ahead energy value of the PTES system within each of six hypothetical near-future grid scenarios intended to approximately represent the U.S. Western Interconnection or the Texas Interconnection. Sensitivity to grid scenario (including the contribution of variable renewable energy sources), thermal storage capacity, relative heat pump and heat engine capacities, and startup/shutdown cycling costs are evaluated. PTES energy value and heat engine annual capacity factor increase strongly as the contribution of variable renewable resources increases, heat pump capacity increases relative to heat engine capacity, or PTES cycling costs decrease. Grid scenarios in which the contribution of variable renewable energy is dominated by solar photovoltaics (PV) vs. wind produce inherently different PTES operational patterns. Annual PTES energy value within PV-dominated scenarios increased with storage capacity only up to approximately seven hours of full-load discharge capacity, whereas that within wind-dominated scenarios exhibited a continual increase with storage duration up to at least 16 hours.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Patterning Metal Grids for GaAs Solar Cells with Cracked Film Lithography: Quantifying the Cost/Performance Tradeoff

We introduce cracked film lithography (CFL) as a way to reduce the cost of III–V photovoltaics (PV). We spin-coat nanoparticle suspensions onto GaAs thin-film device stacks. The suspensions dry in seconds, forming crack networks that we use as templates through which to electroplate the solar cells’ front metal grids. For the first time, we show that heating the crack template allows it to flow and refill cracks, which decreases crack footprint and improves final grid transmittance. We demonstrate 24.7%-efficient single-junction GaAs solar cells using vacuum-free CFL grids. These devices are only 1.7% (absolute) less efficient than the baseline grids patterned by photolithography with the loss mostly resulting from the reduced transparency of the CFL pattern. Additional optimization could decrease this difference. Initial cost modeling suggests that CFL is more scalable than photolithography: In particular, CFL’s lower materials and equipment costs could greatly reduce the levelized cost of electricity of III–V PV at scale, a potential step toward terrestrial deployment.

14 SOLAR ENERGY↗
DOE OSTI2026

FLOW BATTERY COST AND COMMERCIALIZATION TOOL (FlowBaCC) v.1.0

The Flow Battery Cost and Commercialization Tool (FlowBaCC) is a python-based framework used to project the costs and commercialization timelines for redox flow batteries. FlowBaCC combines learning curves for component costs and performance with adoption curves for market diffusion to project future system costs. The tool uses the bottom-up, spreadsheet-based Battery Performance and Cost Model for Flow Batteries (BatPaC-Flow) as the engine to translate learning curve inputs into full system costs. FlowBaCC provides capital costs and levelized costs of energy storage. The tool enables scenario analysis, sensitivity analysis, and uncertainty quantification (via grid and Monte Carlo methods).

Fu, Xiaoxu [Argonne National Laboratory (ANL), Arg↗
DOE OSTI2022

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

14 SOLAR ENERGY↗
DOE OSTI2024

Power System Operational Impacts of Electric Vehicle Dynamic Wireless Charging

The electrification of the transportation sector poses an opportunity for reducing greenhouse gas (GHG) emissions from passenger vehicles. Electric vehicle (EV) charging through dynamic wireless power transfer (DWPT), known as roadway electrification, could shift EV demand profiles to better coincide with renewable electricity generation. However, this would be a very large new load and few studies evaluate the regional impacts of DWPT charging in a power transmission system. This paper defines methods that address dataset generation for passenger vehicle trips and models to evaluate regional impacts for this emerging technology. Household vehicle miles traveled (VMT) data form localized EV demand profiles through discrete-event simulation. This data serves as exogenous inputs for a Production Cost Model (PCM) of a synthetic transmission system based on the Electric Reliability Council of Texas's (ERCOT) network. EV charging methods are compared for both a 2018 baseline generation mixture and a high-renewable generation case incorporating 20 GW of installed solar photovoltaic (PV) capacity. The PCM employs unit commitment and economic dispatch (UC&ED) models to compare financial, environmental, and grid reliability impacts from EV charging across passenger EV adoption levels. In-transit charging could reduce grid operational costs by as much as 1.49%, with up to $13.7B saved in annual vehicle operational costs for consumers compared to gas-powered vehicles. Health impacts analysis from power plant and vehicle tailpipe emissions from this study show net health benefits increase by 40% for in-transit charging coupled with high renewable generation. Renewable resources provide an avenue for cost-effective in-transit charging with reduced emissions. The combination of dataset generation and open-source power system modeling establish a foundation for the holistic evaluation of regional DWPT impacts.

dynamic wireless power transfer↗
DOE OSTI2023

2022 Standard Scenarios Report: A U.S. Electricity Sector Outlook

This report documents the eighth edition of the annual Standard Scenarios. It summarizes 70 forward-looking scenarios of the U.S. electricity sector that have been designed to capture a wide range of possible futures. In August 2022, the United States Congress passed the Inflation Reduction Act (IRA), a law aimed at accelerating U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. This year’s scenarios include representations of the main electricity-sector provisions from IRA and the potential impact on electricity demand. The Standard Scenarios are simulated using the Regional Energy Deployment System (ReEDS) model, which projects utility-scale electricity sector evolution for the contiguous United States using a system-wide, least-cost approach subject to policy and operational constraints. A subset of the scenarios are simulated in the PLEXOS production cost model to obtain a broader suite of metrics at the hourly resolution, which are made available through the National Renewable Energy Laboratory’s (NREL’s) annual Cambium data sets. The scenarios can be viewed and downloaded from NREL’s Scenario Viewer. Annual results are available for the full suite of scenarios in the Standard Scenarios projects in the viewer, whereas the Cambium projects contain hourly data for a subset of scenarios. The Standard Scenarios includes a scenario called the Mid-case, which has central or median values for core inputs such as technology costs and fuel prices, moderately paced demand growth averaging 1.3% per year, and electricity sector policies as they existed in September 2022 (including IRA). The remaining 69 scenarios are created by varying inputs such as technology and fuel prices, resource availability, demand growth, whether nascent generation technologies are allowed, and by introducing national decarbonization constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Operating Reserves in ReEDS

This presentation provides an overview of the formulation for operating reserves in the Regional Energy Deployment System (ReEDS), a capacity expansion model. It describes the three reserve products modeled in ReEDS--regulation, spinning (contingency), and flexibility--and highlights recent updates to the modeling approach, including modifications to how storage provides reserves, the constraints on commitment for generators that provide reserves, and the costs for thermal units to provide spinning reserve. The presentation benchmarks operating reserve provision in ReEDS against comparable simulations in PLEXOS, finding that the improved formulation is different but yields results that are holistically similar to those in production cost modeling. In this presentation we also investigate the impact of different reserve options in the model (e.g., allowing wind and solar to provide operating reserves) on the solution. The results thus far indicate that most switches related to operating reserves exert little influence on the outcome on the results. Future work will continue to explore operating reserves in capacity expansion models, particularly for models with different temporal resolution.

14 SOLAR ENERGY↗
DOE OSTI2024

Nodal Capacity Expansion Modeling with ReEDS: A Case Study of the RTS-GMLC Test System

Test systems play an important role in power systems analysis and are used extensively for a wide range of purposes like reliability analysis, production cost modeling, studying the impacts of load shifting, and many more. These test systems have continued to evolve over the years to keep pace with changing system compositions due to technology and policy changes. In this work, we apply the Regional Energy Deployment System (ReEDS) model, a large-scale capacity expansion model (CEM), on the nodal RTS-GMLC system to perform capacity expansion of the bus-level test system. This work both demonstrates that a large-scale CEM can successfully be applied to nodal systems, and that the CEM can allow a test system to be evolved to meet desired criteria. The process allowed the coupling of CEM data not available in the test system (such as capital costs) with test system data to provide plausible system evolutions in line with scenario specifications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Transmission Alternatives for California North Coast Offshore Wind, Volume 4: Cost-Benefit Analysis Report

The purpose of this report is to compare the costs and benefits of installing offshore wind power plants with capacities up to 500 megawatts (MW) in the Humboldt Wind Energy Area offshore California, focusing on identifying the costs related to transmission upgrades and possible alternatives such as energy storage. We identify several options that result in lower costs than upgrading transmission to provide full deliverability. Our estimates of the levelized cost of energy (LCOE) assuming full deliverability range from $80/MWh for a 144-MW plant to $73/MWh for a 480-MW plant with a 2030 commercial operation date; however, the additional cost of transmission upgrades increases these values to between $86/MWh and $98/MWh. Based on curtailment and revenue estimates developed using a production cost model, the LCOEs for plant capacities of 144 MW and 168 MW with Energy Only deliverability are lower than the combined cost of energy and transmission upgrades for full deliverability. The addition of a 4-hour, 15-MW battery energy storage system increases revenue for both plant sizes, with the 144 MW + storage scenario producing the most revenue relative to LCOE.

17 WIND ENERGY↗