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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Modeling distributed energy resource aggregations in security constrained unit commitment and economic dispatch

The Federal Energy Regulatory Commission (FERC) recently issued Order 2222, which requires all wholesale electricity markets in the US to allow distributed energy resources (DERs) to participate in the market as aggregated resources. These DER aggregations may be composed of many individual resources that are offered and dispatched by the market as a single entity. We present here a model of a distributed energy resource aggregator (DERA) that is scheduled by a market operator’s security constrained unit commitment (SCUC) and security constrained economic dispatch (SCED). The DERA model includes constraints for battery energy storage systems (BESSs), demand response resources (DRRs), and a simple distributed energy resource (DER). This paper describes a model for each resource type and presents two methods for the DERA to generate market offer curves: a profit-maximizing optimization to compute cost curves and a direct cost algorithm to determine dispatch costs for each resource and combine into cost curves. Once all participating DERAs are scheduled in SCUC/SCED, the model is then modified to dispatch individual DERs to maximize profit or minimize schedule deviation of the DERAs. A simulation of a representative day illustrates the DERA offers, the scheduled generation, and the DERA dispatch. Findings show the potential for unavoidable schedule deviations due to internal DER constraints and due to economic incentives to deviate from the SCUC/SCED schedules. This highlights the importance of DERA offer construction on market efficiency and system reliability. Novel aspects of our approach include: (1) We consider the asymmetry of price incentives impacting DERAs from the wholesale market compared to those impacting consumers from the retail market, as imposed by current regulations and laws. (2) We model aggregate consumer response through statistically parameterizable utility functions rather than a potentially impractical approach of modeling each individual consumer. (3) We show how to use the DERA operational dispatch model to create offers into the wholesale electricity market. (4) We show how DERAs may fail to meet their scheduled dispatch because the market offer format may not permit them to fully express their operational features such as intertemporal costs and constraints to the market.

aggregations↗
DOE OSTI2023

Modeling of High Solids Biomass Liquefaction, Techno-economic Assessment and Life Cycle Assessment for Industrial Scale UP

Biomass liquefaction improves corn-stover slurry transport within a biorefinery, and reduces challenges associated with solids handling. Slurries, formed with yield stresses (i.e., viscosities) that are low enough to facilitate pumping from one unit operation to the next, and that enable mixing at reasonable power inputs in large fermentation vessels, enable scaling of cellulose fermentations. Three liquefaction methods: enzyme, enzyme mimetic, and a hybrid approach were analyzed by a combination of techno-economic and life-cycle analyses, resulting in an assessment of cost-effectiveness and environmental impact. Of the three scenarios, enzyme-based processes emerged as the most efficient, both in cost and emissions reduction (gCO2-equivalent/kg slurry).

Ramirez, Jorge↗
DOE OSTI2023

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Modeling the U.S. Western Electric Interconnection to Understand the Consequences of Hydrometeorological Extremes and Options for Risk Mitigation

Electricity grid operators around the world face a dual challenge; withstanding increasingly severe weather and the longer term impacts of climate change, while simultaneously decarbonizing. Extreme weather events such as heat waves and droughts are rising in both severity and frequency, which is threatening the reliability of electricity grids through increased demand, generation capacity losses, and equipment failures. Consequently, incorporating hydrometeorological stressors into computational power systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable grid simulation software capable of exhaustively stress testing the grid under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for power system modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time). In this study, we are proposing a solution to this problem with open-source software that allows users to seamlessly customize the scale and track the accuracy of grid operations models. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. Further, we use this approach to demonstrate the importance of including extreme weather events in model validation and model selection. Focusing on the occurrence of heatwaves and droughts in the U.S. Western Interconnection, we examine role of extreme events in balancing tradeoffs between model fidelity and run-time at the model design stage.

Economics↗
DOE OSTI2021

Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity

Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2026

Pumped Storage Hydropower Resource and Deployment Potential

There is growing interest in deploying new pumped storage hydropower (PSH) to meet grid needs for flexibility, reliability, and resiliency. This presentation describes how NLR resource assessment, cost modeling, and capacity expansion modeling are used to identify technical and economic PSH deployment potential and support industry decision-making on PSH investments. NLR's open data and tools demonstrate the vast technical potential of PSH on the order of 80 TW, and modeling shows economic PSH deployment under an attractive cost-value proposition.

13 HYDRO ENERGY↗
DOE OSTI2020

Probabilistic impact of electricity tariffs on distribution grids considering adoption of solar and storage technologies

This study models the role of electricity tariffs on the long-term adoption of photovoltaic and storage technologies as well as the consequent impact on the distribution grid. An adoption model that captures the economic rationality of tariff-driven investments and considers the stochastic nature of individual consumers’ decisions is proposed. This model is then combined with a probabilistic load flow to evaluate the long-term impacts of the adoption on the voltage profiles of the distribution grid. To illustrate the methodology, different components of the electricity tariffs, including solar compensation mechanisms and time differentiation of Time-of-Use (ToU) rates, are evaluated, using a case study involving a section of a medium-voltage network with 118 nodes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Energy-effective and low-cost carbon capture from point-sources enabled by water-lean solvents

Aqueous amines, as the most mature carbon capture technology, are subject to high energy and cost penalties due to the large water content in their formulations. Emerging technologies are in demand to enable a transition to a low-carbon global economy. However, rigorous process modeling and techno-economic analyses are limited for emerging carbon capture technologies. Here, four CO 2 -Binding Organic Liquids (CO 2 BOLs), all water-lean solvents were presented as promising options towards energy-effective and low-cost carbon capture from point sources. Rigorous solvent property and process models were developed in Aspen Plus for a coal-fired power plant with CO 2 BOL-based carbon capture unit. Techno-economic analyses were conducted in 2018 US pricing basis. The results suggest that water-lean formulations can minimize water condensation and vaporization, leading to a 36% energy saving compared with aqueous amines. Indeed, these CO 2 BOLs can capture up to 97–99% CO 2 from coal fired plant. The estimated carbon capture cost is about $40/tonne CO 2 at 90–97% carbon capture rate, about 12–23% less expensive than the conventional aqueous amine technology. The comparison between these CO 2 BOLs showed that in addition to vapor liquid equilibrium and kinetics (key properties for aqueous solvents), viscosity, volatility, and hydrophobicity, also have strong impacts on the performance of water-lean solvents. The methods presented in this work can be used to evaluate other emerging carbon capture technologies, while the results linking costs and performance of carbon capture solvents with their properties. Additionally, this work identifies research directions and targets for further reductions in total costs of capture from either cost or energy perspectives for these leading water-lean solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Environmental design of low-head run-of-river hydropower in the United States: A review of facility design models

We state that the goal of run-of-river hydropower is to produce cost-competitive renewable electricity with minimal disruption of the natural riverine ecosystem. Modeling and feasibility analysis of alternative design options are crucial for developing new run-of-river hydropower projects. Our review shows that existing run-of-river hydropower design models focus on maximizing economic potential at high-head diversion schemes with limited consideration of environmental outcomes. Since nearly three-quarters of new hydropower potential in the United States is found at low-head sites and environmental performance standards are imperative to project success, new models are needed to address the multi-dimensional design challenges at these sites. To aid in formulating holistic models, we synthesize the performance objectives and design variables related to early-stage run-of-river facility design. The objectives span six potential impact areas, including hydrologic alteration, sediment continuity, water quality, aquatic species passage, social, and economic. Based on these reviews, we identify three key areas to enhance the capabilities of run-of-river hydropower design models. These are 1) expanded model formulations, 2) assessment of barrier effects, and 3) explicit environmental objectives. The resulting modeling improvements would accelerate the identification of run-of-river hydropower designs that minimize environmental impacts, promote economic competitiveness, and incorporate the value of non-power benefits.

13 HYDRO ENERGY↗
DOE OSTI2022

Techno-economic and life cycle assessment of aluminum electrorefining from mixed scraps using ionic liquid

Aluminum production from bauxite ore uses significantly high amount of energy and capital expenditure. Recycle and reuse of aluminum can be economical and minimize the environmental impacts. Smelter based recycle and reuse of aluminum is used in recent days, however, it also uses high amount of energy with high cost of production and yields high life cycle impacts. The University of Alabama has developed aluminum electrorefining technology from mixed scraps using ionic liquids as an alternative to traditional smelter based recycle and reuse. This study has explored the techno-economical, and life cycle viability of that technology. An excel-based techno-economic and life cycle assessment model was developed at Idaho National Laboratory for techno-economic and life cycle assessment. SimaPro was used to get the necessary database for the life cycle assessment. This study determined that a 20,000 kg/day ionic liquid-based electrorefining system can be profitable with a net yearly profit of $2.00 million. Further, in terms of net global warming potential, it emits 0.92 kg CO 2 equivalent per kg of aluminum recycled, whereas the traditional smelter-based recycle technology emits 1.57 kg CO 2 equivalent per kg of aluminum recycled, and the aluminum production from bauxite ore emits 17.8 kg CO 2 equivalent per kg of aluminum produced. In other life cycle assessment categories, electrorefining of aluminum emits >88 % less than aluminum production from bauxite ore and it is also better than traditional aluminum recycling in six out of ten categories studied. This makes ionic liquid-based electrorefining technology a very promising technology in terms of process economics and environmental sustainability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Herbaceous Feedstock 2019 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the State of Technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and elsewhere (such as the High-Tonnage Feedstock Logistics projects (Webb et al. 2013a, Webb et al. 2013b, Webb et al. 2013c, Webb and Sokhansanj 2014, Sokhansanj et al. 2014)) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D improvements to individual supply system unit operations and present the status of feedstock logistics technology development for converting biomass into biofuels. These analyses are supported by experimental data where possible and help to align the SOT relative to the cost goals defined in the Multi-Year Program Plan. The 2019 Herbaceous SOT incorporates several technology changes in feedstock preprocessing and introduces opportunities from the integrated landscape management (ILM) strategy and increased grower participation to reduce biomass access costs, while maintaining or improving grower profitability. During FY18 uneven flow from the horizontal bale grinder was identified as a significant issue limiting preprocessing system throughput. Based on FSL-funded research at INL, the 2019 Herbaceous SOT replaces the horizontal bale grinder used in the first stage size reduction with a bale processor. The improved uniformity of biomass flow entering the PDU eliminated slugging flow from the first stage size reduction and improved the throughput of downstream operations. In order to achieve moisture reduction through frictional heating during grinding (which allowed elimination of the costly rotary drum dryer in previous SOTs), the second stage grinder was changed from a rotary shear, which does not remove moisture, back to a hammer mill. Finally, the 2019 Herbaceous SOT introduces modified three-pass and two-pass corn stover supply curves derived from the BT16 resource assessment, based on FY19 modeling results (WBS 4.2.1.20) quantifying economic benefits of ILM in the supply area, together with modeling results (WBS 1.2.1.5) identifying ILM strategies to increase grower participation. The 2019 Herbaceous SOT report documents the current modeled cost of an herbaceous feedstock supply system from harvest to the pretreatment reactor throat for hydrocarbon fuel production via biochemical conversion, based on equipment and processes now available or potentially available in the near term. The modeled cost also considers both the required quality and the availability of the biomass resources. The 2019 Herbaceous SOT predicts a modeled delivered feedstock cost of $81.37 /dry ton (2016$); this is a $2.30/dry ton (2016$) decrease from the 2018 Herbaceous SOT. Technology improvements that contributed to this modeled cost reduction include reduced cost for the new preprocessing design and quantification of the opportunities of the integrated landscape management (ILM) strategy and an increased grower participation rate to reduce the grower payment portion of biomass access costs, while maintaining or improving grower profitability. A greenhouse gas emissions (GHG) assessment was completed by Argonne National Laboratory using the 2019 Greenhouse Gases, Regulated Emissions, and Energy use in Transportation model, estimating an increase of 14.89 kg CO2e/ton from the 2018 SOT (69.27 kg CO2e/ton in 2018 to 84.16 kg CO2e/ton in 2019). The increase of energy consumption during preprocessing along with higher transportation distance to access low cost biomass from further distance contributed to the increase of GHG emissions in the 2019 Herbaceous SOT. The reason for the increased transportation distances was the cost tradeoff of going farther from the biorefinery to access the cheaper ILM-derived counties (the cheaper price outweighed the cost of increased supply radius).

09 BIOMASS FUELS↗
DOE OSTI2024

Assessment of Economic Viability of Direct Current Fast Charging Infrastructure Investments for Electric Vehicles in the United States

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Code and Data for "Quantifying Uncertainties in Greenhouse Gas Savings and Mitigation Costs with Cellulosic Biofuels"

Use of cellulosic biofuels from non-feedstocks are modeled using the BEPAM (Biofuel and Environmental Policy Analysis Model) model to quantifying the uncertainties about induced land use change effects, net greenhouse gas saving potential, and economic costs. The code is in GAMS, general algebraic modeling language. NOTE: Column 3 is titled "BAU" in "merged_BAU.gdx", "merged_RFS.gdx", and "merged_CEM.gdx", but contains "RFS" data in "merged_RFS.gdx" and "CEM" data in "merged_CEM.gdx".

BEPAM↗
DOE OSTI2021

Techno-Economic Analysis of a Concentrating Solar Power Plant Using Redox-Active Metal Oxides as Heat Transfer Fluid and Storage Media

We present results for a one-dimensional quasi-steady-state thermodynamic model developed for a 111.7 MW e concentrating solar power (CSP) system using a redox-active metal oxide as the heat storage media and heat transfer agent integrated with a combined cycle air Brayton power block. In the energy charging and discharging processes, the metal oxide CaAl 0.2 Mn 0.8 O 2.9-δ (CAM28) undergoes a reversible, high temperature redox cycle including an endothermic oxygen-releasing reaction and exothermic oxygen-incorporation reaction. Concentrated solar radiation heats the redox-active oxide particles under partial vacuum to drive the reduction extent deeper for increased energy density at a fixed temperature, thereby increasing storage capacity while limiting the required on sun temperature. Direct counter-current contact of the reduced particles with compressed air from the Brayton compressor releases stored chemical and sensible energy, heating the air to 1,200°C at the turbine inlet while cooling and reoxidizing the particles. The cool oxidized particles recirculate through the solar receiver subsystem for another cycle of heating and reduction (oxygen release). We applied the techno-economic model to 1) size components, 2) examine intraday operation with varying solar insolation, 3) estimate annual performance characteristics over a simulated year, 4) estimate the levelized cost of electricity (LCOE), and 5) perform sensitivity analyses to evaluate factors that affect performance and cost. Simulations use hourly solar radiation data from Barstow, California to assess the performance of a 111.7 MW e system with solar multiples (SMs) varying from 1.2 to 2.4 and storage capacities of 6–14 h. The baseline system with 6 h storage and SM of 1.8 has a capacity factor of 54.2%, an increase from 32.3% capacity factor with no storage, and an average annual energy efficiency of 20.6%. Calculations show a system with an output of 710 GWh e net electricity per year, 12 h storage, and SM of 2.4 to have an installed cost of $329 million, and an LCOE of 5.98 ¢/kWh e . This value meets the U.S. Department of Energy’s SunShot 2020 target of 6.0 ¢/kWh e ( U. S Department of Energy, 2012 ), but falls just shy of the 5.0 ¢/kWh e 2030 CSP target for dispatchable electricity ( U. S Department of Energy, 2017 ). The cost and performance results are minimally sensitive to most design parameters. However, a one-point change in the weighted annual cost of capital from 8 to 7% (better understood as a 12.5% change) translates directly to an 11% decrease (0.66 ¢/kWhe) in the LCOE.

Gorman, Brandon T.↗
DOE OSTI2020

Deep Direct-Use Geothermal Feasibility Study for Hawthorne NV

The Hawthorne Nevada, deep direct-use geothermal study is a two-year effort funded by the U.S. Department of Energy to determine the techno-economic feasibility of implementing a large-scale, direct-use facility for the Hawthorne Army Depot (HAD) and the public facilities of the city of Hawthorne and Mineral County. The approach links a production side analysis (PSA) and a demand side analysis (DSA) into a whole-system analysis (WSA) to provide an integrated assessment of the resource and the probability of delivering economically viable direct-use energy to Hawthorne. Hawthorne, Nevada is in the western part of the Basin and Range province and has been the focus of geothermal investigations for over 40 years. Over the last 15 years, several studies completed by the U.S. Navy Geothermal Program Office (GPO) in conjunction with industry professionals quantified the existence of several low temperature geothermal prospects, the most promising of which is called Prospect A. The promise of Prospect A is based on drilling and flow testing that produced ~100 °C water at flow rates of up to 31 l/s (500 gallons per minute). Measured productivity indexes range from 40-85 l/s/MPa, suggesting a warm and productive heat source. Despite the promise of the resource, uncertainties in its spatial extent and long-term sustainability mean that techno-economic analyses must include probabilities of the sustainability of the resource under different operating scenarios. Here, the PSA is conducted by integrating a wide range of disparate data to estimate lognormal P90, P50, and P10 resource capacities. These capacities are used as input to a thermal-hydrologic (T-H) model to estimate thermal drawdown for each capacity estimate for several different DSA scenarios. Using a systems-based approach, the WSA links the dynamic T-H simulations of the PSA/DSA combinations with the techno-economic model GEOPHIRES to account for both the temporal dynamics and uncertainties in the system to produce probabilistic distributions of several performance metrics including the levelized cost of heat (LCOH) and the return on investment (ROI). This report is the final delivery for the project and documents the study's activities and results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Rosario Strait Tidal Energy plus Energy Storage — Preliminary Economic Assessment

The Orcas Power and Light Co-op (OPALCO) is a non-profit utility that provides energy services to approximately 11,700 customers across 20 islands in San Juan County, Washington. OPALCO is developing a diverse set of local renewable energy resources to reduce dependence on mainland Washington State for energy and to reduce the regional need for fossil-fueled power. OPALCO’s load doubles in winter but solar production is roughly 20% of levels reached in summer periods. Tidal energy is strong year-round, night and day, and is predictable, requiring much less storage to firm it. To assess the technical and economic feasibility of tidal power, working in combination with multiple other distributed energy resources (DERs), Argonne National Laboratory (Argonne) employed an optimization model to evaluate several economic benefits associated with varying scales of tidal power of between 2.4 and 9.6 megawatts (MW) and other DERs. In addition to existing photovoltaic (PV) and battery energy storage system (BESS) resources located on Decatur Island, Argonne also evaluated the addition of a BESS on southern Orcas Island with power and energy capacities ranging from 1-4 MW and 2-4 hours in storage duration.

25 ENERGY STORAGE↗