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DOE OSTI2020

Feasibility of Ocean-Based Renewable Energy in the Gulf of Mexico

This paper summarizes a comprehensive feasibility assessment of six offshore renewable energy sources in the Gulf of Mexico (GOM) to potentially provide utility-scale electricity from the Outer Continental Shelf (federal waters) and state waters to land-based grids. Overall, the primary objective is to inform future energy planning. The authors evaluated offshore wind, wave energy, tidal energy, ocean-based solar photovoltaics (PV), ocean current, and ocean thermal energy conversion. Of these energy resources, offshore wind was the most viable option and was the focus of the second part of the study. The technical challenges of developing offshore wind in the GOM are discussed including hurricane design for turbines and substructures, as well as turbine solutions to overcome lower wind regimes. In addition, advantages to offshore wind development in the GOM are described including proximity to oil and gas supply chains. Economic analysis using established cost models at the National Renewable Energy Laboratory identified hypothetical project locations where net value of offshore wind was the highest, and the levelized cost of energy (LCOE) was calculated for three sites: Port Isabel, Port Arthur, and Pensacola. Offshore wind LCOE in the GOM was found to be higher than those in sites along the north and mid-Atlantic coasts, but decreasing cost trajectories indicate the possibility of economic viability for locations in Texas and west Louisiana after 2030. The extrapolated 2030 LCOE values range from 73 dollars per MWh (Site 1, Port Isabel) and 79 dollars per MWh (Site 3, Port Arthur) to 91 dollars per MWh (Site 5, Pensacola).

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Iterative and Self-Consistent Estimation of Degradation and Soiling Loss in PV Systems - A Case Study

In many parts of the world, soiling is a significant loss mechanism for PV energy production. Methods for accurately quantifying historical and current soiling losses are needed. Furthermore, state-of-the-art methods to estimate degradation rates based on production data do not handle soiling in a satisfactory manner. In order to accurately estimate either of these quantities, it is in many cases helpful to consider their combined effect on PV system performance. In this work we make use of the Combined Degradation and Soiling (CODS) algorithm, which iteratively decomposes time series of daily normalized energy yield into four components: a soiling ratio, a degradation trend, seasonal variations, and noise. We demonstrate the use of the model on field performance data from a utility-scale power plant in a high-soiling environment in the Middle East. We apply the algorithm to the temperature-corrected performance index of the DC power on inverter level, quantifying how soiling varies over time and through the plant. The irradiation-weighted average soiling loss in this plant is 2.3 %, with a median soiling rate of -0.086 %/day. Between the inverters in the site in study, estimated degradation rates range between -0.86 %/year and -0.15 %/year, with a mean rate of -0.48 %/year.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2022

Conflicts of Greens’ in Renewable Energy Landscapes: Case Studies and a Planning Framework

Reducing greenhouse gas emissions (GHG) through renewable energy deployment is a goal for many cities and nations to mitigate the effects of a rapidly changing climate while securing energy needs. Given national and state level policies on green energy to achieve GHG reduction targets, localities are considering utility-scale renewable energy (USRE) facilities on remote lands, rural areas, oceans, coastal waters and large rivers to meet the energy needs for urban residents and industries. Although these USRE facilities generate much “greener” electricity than fossil fuel power plants, locating them can pose conflicts with wildlife, including endangered, threatened and/or special status species, and the habitats that support wildlife populations (Brunette et al., 2013; Gasparatos et al. 2016; Mulvaney 2017), which lead to a “Conflict of Greens” (Ko et al., 2011). In addition to ecological impacts, conflicts over social and cultural resources in local communities lead to concerns over environmental justice, in this context “energy justice,” where negative environmental, social, and economic impacts of energy projects fall more heavily upon marginalized, vulnerable, or indigenous communities. The Pacific Rim region is a center of major and growing economies, and it has become a battlefield for competing “green” objectives. In addition to progressive renewable energy goals, such as in California, Hawaii, and Taiwan, countries around the Pacific Rim have the majority of the growth in manufacturing of wind and solar energy devices and also some of the highest levels of renewables deployment with China, India, and the US leading the way in wind and solar installations. Developing renewable energy source in the ocean such as offshore wind, tidal, and wave energy, are beginning to add to this mix. Given this challenge, the APRU SCL energy working group provides six case studies that address questions about the “Conflict of Greens” across the Pacific Rim including South Korea, Taiwan, and the United States (California, Hawaii, and Massachusetts). The literature review highlights the emerging ecological, social, and economic aspects of conflicts over siting renewables on the landscape. Using case studies, we examine the trial and errors in least-conflict spatial planning, data collection and analysis, public participation in decision-making, mitigation, social planning for energy transitions, and multi-scalar approaches. Lastly, we recommend interdisciplinary policy, planning, and design actions for sustainable energy landscapes across the Pacific Rim and beyond.

green versus green, renewable energy↗

DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗

DOE OSTI2025

Performance of wind assessment datasets in United States coastal areas

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

17 WIND ENERGY↗

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2020

California Time-of-Use (TOU) Transition: Effects on Distributed Wind and Solar Economic Potential

Time-of-use (TOU) retail energy rates price electricity differently by the time of day, communicating to consumers the costs of supplying electricity diurnally. This study evaluates the impact of Decision D.15.07-001, which mandates a transition in 2020 to default TOU rates for residential ratepayers in California, on the economic viability of behind-the-meter distributed wind and solar systems. Previous studies have demonstrated that increasing wind and solar generation will materially affect the diurnal cost of wholesale electricity. As retail and wholesale prices become more aligned, this could affect the inter-technology economic viability of distributed generation. We assess economic viability through 'economic potential,' or the amount of generation capacity that exceeds a rate of return of 5.4%. In addition, we identify sensitivities of economic potential to capital costs and other techno-economic factors as well as the specific counties and sectors with substantial economic potential. We find that TOU implementation is projected to moderately increase economic potential of distributed wind and marginally decrease that of distributed solar. Over the long-term as variable renewable energy penetration increases in California, particularly utility-scale solar, net peak load and thus peak prices will shift later in the evening. To the extent that retail rates continue to evolve with wholesale prices, we find the changes could positively affect the value of wind generation more than that of solar generation. Over the scenarios considered, model results indicate realistic pathways to reaching a robust distributed wind market in California though solar economic potential was found to be substantially larger than that of wind.

17 WIND ENERGY↗

DOE OSTI2020

Provision of Grid Services by PV Plants with Integrated Battery Energy Storage System: Preprint

Battery energy storage systems (BESS), due to their tremendous range of uses and configurations, may assist PV integration in any number of ways by increasing power system flexibility. In this paper we describe results of a research project conducted by NREL and First Solar for developing controls and demonstrating many use cases for PV-BESS systems including (1) matching generation to loads through time shifting, reducing PV curtailment,; (2) promoting higher levels of PV penetrations by balancing the grid through ancillary services; (3) use of PV-BESS systems to provide wide-area stability services in the form of oscillations damping controls; and (r) use of gird forming BESS for enabling black start and islanded applications for PV-BESS systems. In the paper we will also describe the unique multi-MW test and validation platform developed at NREL for conducting research on fielded utility-scale PV-BESS systems. The platform consists of First Solar 0.43 MW PV power plant, NREL’s 1MW/1MW BESS, 2MW PV power supplies and 7MVA power electronic grid simulator interfaced with industrial plant controllers and power-hardware-in-the-loop (PHIL) capability.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Cadmium Telluride Photovoltaics Accelerator Consortium Solicitation: Request for Proposals Information

Cadmium telluride photovoltaics (CdTe PV) is a leading PV technology built on U.S. innovations that currently holds significant market share within domestic utility scale PV systems. In order to keep pace with a highly competitive and innovative global PV module market, CdTe manufacturing needs readily adaptable advances at the cell and module levels that simultaneously enable higher conversion efficiencies and manufacturing cost reductions without risking long-term module durability. In pursuit of this goal, the Department of Energy (DOE) Solar Energy Technologies Office (SETO) has asked the National Renewable Energy Laboratory (NREL) to launch and support a Consortium to accelerate domestic CdTe technology development through a competitive solicitation process. NREL will competitively select a CdTe Consortium with the goals of enhancing U.S. competitiveness and expanding domestic CdTe PV material and module production through 2030. NREL will provide coordination and oversight through development of technical scopes, review of technical proposals, subcontract support, and review of deliverables along with research support to the Consortium. The new Consortium is expected to include major U.S. companies and universities that possess strong technology development, transfer, and validation capabilities. The research and analysis output of the CdTe Consortium will inform purchasing, design, deployment, and operations decisions of companies in the domestic CdTe PV supply chain, leading to increased material and module production in the U.S.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

A Circularity Assessment for Silicon Solar Panels Based on Dynamic Material Flow Analysis

Solar photovoltaics (PV) are the fastest growing renewable energy technology for clean, inexpensive, and sustainable electricity generation. Along with numerous technical roadmaps to improve system cost, performance and reliability, the PV industry should also plan to handle large volumes of silicon panel waste, which is initially estimated to be ~13 million metric tons (MT) by 2050 in the U.S. alone. Understanding the magnitude of material needs and how material flows throughout the PV panel life cycle could respond to design, operational and different end-of-life (EOL) circular pathways will help transition into a circular, resource-conserving economy. Herein, we introduce a dynamic material flow analysis (DMFA) framework based on electricity generation to quantify time-series stocks and flows of bulk PV materials (e.g., solar glass and aluminum frames) throughout the life cycles of utility-scale silicon PV systems in the U.S. in the period 2000-2100. We apply the model to a range of scenarios to understand how material demands depend on selected PV-related parameters, different material circularity strategies, and recent module design trends (e.g., bifacial, frameless). We found that float glass and aluminum in PV installations would likely reach 100 million MT and 12 million MT by 2100, respectively, in the baseline scenario. The most influential parameters for PV installation and subsequent waste reduction are found to be module lifetime, module efficiency, annual degradation, and material reduction. Module recycling and component remanufacturing were found to be the most effective material circularity strategies for waste minimization. Panel reuse has negligible savings on waste under current module efficiencies compared to replacements with newer generations with higher efficiency. Ongoing trends to produce larger power frameless modules could save 10 million MT of glass and ~9 million MT of aluminum. Our results enable advanced planning for future materials needs and provide insight into potential opportunities to minimize waste.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Storage Futures Study - Distributed Solar and Storage Outlook: Methodology and Scenarios

This presentation discusses the fourth report in NREL’s Storage Futures Study (SFS) publications. The SFS is a multiyear research project that explores the role and impact of energy storage in the evolution and operation of the U.S. power sector. The SFS is designed to examine the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. This report describes the expanded capabilities of the Distributed Generation Market Demand (dGen) model to analyze the economics of distributed (behind-the-meter) PV paired with battery storage systems and presents projections of adoption for the contiguous United States out to 2050 under a range of scenarios. These scenarios use technology cost and performance assumptions consistent with the National Renewable Energy Laboratory’s 2020 Standard Scenarios paired with updated battery cost projections and existing policies. Additional scenarios evaluate sensitivities to the value of backup power and DER compensation mechanisms, collectively characterizing the future potential for behind-the-meter storage and identifying key drivers of adoption. Adoption projections of DER and battery storage at high spatial and temporal resolution, as presented in this report, can enable informed planning of technical infrastructure that can help planners capture the benefits and mitigate challenges to support the ongoing trend toward distributed electricity generation.

backup power↗

DOE OSTI2021

Wind Turbine Blade Finishing Automation: Robotic Toolpath Generation

Wind turbine blades are now over 100 meters long and can reach heights of several meters while in the finishing area of the factory. Because of this, automation should be used to lower the manufacturing cost and make the process safer for blade manufacturing technicians. This session will present a novel method that generates a six degree of freedom robotic toolpath with 3D cameras for the finishing of wind turbine blades to drive down the levelized cost and make wind energy even more competitive in the utility scale energy generation industry.

3d imaging↗

DOE OSTI2021

A Circularity Assessment for Silicon Solar Panels Based on Dynamic Material Flow Analysis: Preprint

circular economy↗

DOE OSTI2021

Energy Arbitrage: Comparison of Options for use with LWR Nuclear Power Plants

Arbitrage is the opportunistic buying and selling of a commodity during local pricing valleys and peaks respectively to maximize economic value. This report evaluates options for energy arbitrage integrated with existing light water reactor (LWR) nuclear power plants (NPPs) where nuclear energy could be stored in a variety of forms and later recovered to generate electrical power during periods when grid electricity demand and pricing are high. The forms of energy storage examined in this report include the potential value of batteries, hydrogen, and thermal energy storage for coupling with nuclear power. Various large demand response options are also analyzed, including the production of liquid nitrogen via air separation and liquefaction, liquefaction of hydrogen, compressed hydrogen, and the cryogenic capture of CO2. Demand response refers to dispatchable loads that can cycle up or down depending on-grid electricity demand to aid in balancing the grid. Large demand response options could dispatch to aid nuclear power stations in avoiding power turndowns by providing an alternate disposition for electrical energy by producing marketable products (e.g., liquid nitrogen, hydrogen, or captured CO2). Static conditions were chosen and analyzed in this report for each option. Dynamic operation or optimization of energy arbitrage or demand response are out of scope for this report. The analysis is based on storage systems with discharge capacities of 500 MW for which various durations of storage and costs of charging (electricity cost) are examined. While the value of thermal energy to an industrial user for flexible plant operations has been previously proven as a business case, this report evaluates costs of hydrogen energy storage and leading thermal energy storage options, and large demand response loads that could be integrated with LWRs in comparison to utility-scale battery storage for use of off-peak nuclear energy. Compilation of this information will be used by the Idaho National Laboratory (INL) RAVEN/HERON systems integration and economics tool to evaluate thermal energy dispatch to industrial users. Relative ranking of energy storage options was done using a levelized cost of storage (LCOS) metric which calculates a rough breakeven cost for the system, taking into account the capital and operating costs as well as the revenue from arbitrage. Table ES1 below shows the LCOS for each of the energy storage options considered. First, in the table, lithium iron (Fe) phosphate batteries are listed as the base case for comparison against the other options. Next is hydrogen storage where most of the hydrogen analyses assumed the hydrogen to be produced using solid oxide electrolytic cell (SOEC) high temperature steam electrolysis (HTSE). The others used existing models of polymer electrolyte membrane (PEM) low temperature electrolysis to produce hydrogen. HTSE performance parameters and costs were taken from existing INL models. Various means were assumed to convert the hydrogen to electricity, including PEM fuel cells (FCs) and a gas turbine mixed in a 30 vol% mixture with natural gas. Physical storage (pressure vessels) and geological storage (natural underground features) were used to store the hydrogen as noted. Geological storage is more economical, but the locations are limited because of the requirement for pre-existing geological formations that will support storage. Thermal energy storage (TES) options were also analyzed including electro-thermal energy storage (ETES) and four different liquid sensible heat TES storage media as noted (Hitec, Hitec XL, Therminol-66, and Dowtherm A). The ETES process considered was modified using existing public documentation on an Echogen process and uses a separate supercritical CO2 charge and discharge cycle with sand as the heat storage media.

25 ENERGY STORAGE↗

DOE OSTI2021

Geothermal Energy and Resilience in Arctic Countries

The eight Arctic countries have diverse energy systems but can be split into two distinct groups based on energy characteristics. The first group includes countries which are heavily gridconnected (Iceland, Norway, Sweden, and Finland); the second group includes countries with some grids as well as an abundance of remote microgrids, particularly in their more northern regions (Canada, Russia, the United States [Alaska], and Greenland). The primary energy source for both heat and power in remote communities is almost exclusively diesel. Geothermal energy is currently used in all eight Arctic countries, providing heat and sometimes electricity at utility scales and at the microgrid scale. However, the availability of geothermal resources is poorly defined in Arctic countries. We reframe geothermal heat and power as integrated energy systems, asking the question: are integrated geothermal energy systems - where available and economic - resilient solutions for communities in Arctic countries? Resilience attributes of integrated geothermal energy systems are identified, with a focus on microgrids and small-scale applications.

arctic energy↗

DOE OSTI2022

Storage Futures Study: Key Learnings for the Coming Decades

The Storage Futures Study (SFS) is a multiyear research project that explored the role and impact of energy storage in the evolution and operation of the U.S. power sector. The SFS examined the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. The research findings and supporting data were published across a series of six reports, culminating in the final, seventh publication that draws upon findings from across the study, previous work, and additional analysis to identify eight key learnings about the coming decades. This presentation is from an NREL webinar to discuss the eight key learnings.

decarbonization↗

DOE OSTI2022

Variable Resource Resilience: How Systems Experience Increased Resilience from Variable and Hybrid Resources

Variable resources like wind and solar are often seen as detriments to system resilience rather than benefits because they may not be available with the capacities or services required during a high-impact low-frequency (HILF) event, whether that is a physical threat, natural disaster, or cyber attack. However, resilience goals and metrics are inadequate for electric energy delivery systems with inverter-based resources. Examination of this topic reveals that renewable resources are well suited to combat many resilience hazards due to local resource availability. Metrics that demonstrate the resilience value of variable resources are presented and categorized for resource (wind, solar, storage, hybrid) and installation type (bulk utility scale, behind-the-meter, front-of-the-meter, isolated). Distributed and hybrid systems can further enhance resilience benefits my maximizing resource potential for a locality. A case study demonstrating quantitative resilience benefits from wind alone is provided for St. Mary's, AK, which concludes that hundreds of thousands of dollars are saved by the addition of a wind turbine in the face of realistic fuel shortage and extreme winter weather scenarios.

17 WIND ENERGY↗

DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗