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This report is part of a series of annual papers from the LIFT Community of Practice which convenes cross-sector stakeholders and organizations to expand public participation in renewable energy development.
The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for the city of Orlando in Florida. The rooftop solar technical potential information is based on data from two different datasets. The first dataset is LiDAR data for the city of Orlando obtained from the Orlando Utilities Commission (OUC) (Koebrich et al. 2021). The second dataset is a national parcel dataset (HIFLD 2020) which contains descriptive data and geometries for parcels in the U.S. Parcel scale data from both these datasets have been processed and aggregated to block scale to produce these html maps. The first html map (block scale developable roof area for Orlando) contains the developable roof area for solar. The second html map (block scale rooftop solar technical potential for Orlando) contains the rooftop solar technical potential in units of kilowatts as well as additional information on the most common building use type and the most common building occupancy type for the block. These html maps are provided to demonstrate the proof-of-concept analysis conducted for Orlando.
Organic waste streams generated by wastewater treatment plants, agricultural operations, and food processing industries represent an important yet underutilized opportunity for renewable energy production in the United States. Through anaerobic digestion, these waste streams can produce biogas, a mixture primarily composed of methane (CH4) and carbon dioxide (CO2), that can be upgraded to pipeline-quality natural gas. However, most existing upgrading technologies remove CO2 from biogas rather than utilizing it, leaving a significant portion of the potential energy unused. This project investigates a novel biological upgrading approach that converts CO2 into additional CH4 by supplying hydrogen (H2) to specialized microorganisms capable of performing hydrogenotrophic methanation. The main challenges associated with biological biogas upgrading are related to hydrogen supply, gas-liquid mass transfer, and process stability. First, due to the high cost of hydrogen gas, it is preferable that H2 be produced on-site using renewable energy sources such as wind or solar power. Second, hydrogen has low solubility in liquids, which limits its availability to microorganisms and requires strategies to improve gas dissolution and transfer within the reactor. Third, process inhibition may occur as a result of increased pH caused by CO2 consumption or elevated H2 partial pressure, both of which can negatively affect methanogenic activity. Although research in these areas has advanced during the course of this project, these challenges have not yet been fully resolved. To date, the biological systems that have achieved the highest methane concentrations are typically ex-situ reactors, where operational conditions can be more easily controlled. For this reason, the findings of the present project remain highly relevant. The project goal was to develop an innovative system that can accomplish biogas upgrading via biological conversion of CO2 to CH4, in a novel hybrid approach that combines the advantages of both in-situ and ex-situ systems. The proposed system employs a three-phase upflow anaerobic bioreactor with H2 delivery through a gas-permeable membrane, enabling efficient hydrogen transfer and microbial conversion. Under optimized operating conditions, the system achieved 99% H2 consumption and 90% CO2 conversion. A subsequent gas cleaning stage was implemented to further improve gas quality and meet target purity standards. The upgraded gas composition reached 97.7% CH4, 2.2% CO2, and 0.97% O2, while H2S concentrations remained below detection limits. In addition, a flue gas-driven inorganic thermoelectric generator (TEG) system was designed and experimentally validated as a potential source of electricity for H2 production. The system consisted of six TEG modules connected in series and achieved an open-circuit voltage of 4.5 V and a maximum power output of 224 mW at a temperature difference of approximately 53.5 °C, demonstrating effective conversion of waste heat into electrical power under simulated flue gas conditions. Finally, a comprehensive techno-economic analysis was completed to evaluate the capital and operating costs associated with the proposed system. The results provide important insights to guide future scale-up, optimization, and potential deployment of integrated biological biogas upgrading technologies.
Energy system models underpin decisions by energy system planners and operators. Energy system modeling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modeling has started to form that aims to better integrate energy system models with weather and climate models. In this work, we evaluate the disconnects between the energy system and climate modeling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modeling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision-making.
Using Japan as a proxy for a developed nation, we investigated the role of existing and nascent technologies in curbing carbon emissions. We simulated possible pathways to meeting 2030 and 2050 emission targets within the Japanese electricity supply sector using a single-region model in The Integrated MARKAL-EFOM System (TIMES). Critically, our simulations incorporate novel technologies like hydrogen electrolysers, carbon capture, photochemical water splitting, and emerging photovoltaic cells, assess long-term impacts up to the year 2100, and include life-cycle emissions and learning curves for parameters such as investment cost, efficiency, and emission coefficients. Results indicate that a hybrid approach, using nuclear power and hydrogen from renewable energy-based electrolysis, is cost-effective and provides long-term emission reduction along with energy security. Finally, nuclear, wind, solar, and hydrogen from renewables emerge as key emission reduction technologies, while natural gas with carbon capture plays a minor role in achieving emission reduction targets.
Methane (CH 4 ), an abundant greenhouse gas, is the second largest contributor to global warming after carbon dioxide (CO 2 ). In comparison to CO 2 , CH 4 has a larger warming effect over a much shorter lifetime. While technologies to radically reduce global carbon dioxide emissions are materializing, rapid reductions in methane emissions are needed to limit near-term warming. Methane is primarily emitted as a byproduct from agricultural activities and energy extraction/utilization and is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected, and emission rates are challenging to characterize with current monitoring frameworks. In this report, we study methane leaks from oil and gas infrastructure using a tiered monitoring approach that combines bottom-up and top-down approaches in an integrated framework. We describe the individual advantages of bottom-up and top-down sensors in both stationary and mobile settings before characterizing how a fully integrated framework can improve predictions and uncertainties of potential leak locations and their emission rates. Further, we study the impact of different atmospheric (wind) conditions on integrated methane monitoring and develop a probabilistic approach to optimal sensor placement, thereby shortening detection times and improving monitoring capabilities. Last, we discuss how biogenic flux modeling can be used to improve assessment of background methane concentrations needed to fully assess the sensitivity of a tiered monitoring system.
Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.
The purpose of this CRADA is to address key technical challenges specific to the transport of ammonia, a promising carbon-free fuel and hydrogen-carrier, through crude oil pipelines. Specifically, the project seeks to develop novel technologies for preparing liquid ammonia/ hydrocarbon mixtures for the dual purpose of (i) pipeline transport, and (ii) developing advanced marine fuel blends. By demonstrating compatibility with the Trans-Alaskan Pipeline System (TAPS) ammonia/oil blends may improve access to stranded natural gas and help overcome low-flow issues associated with declining oil productivity. In Alaska, this technology enhances the capabilities of TAPS allowing it to function as a statewide “Hydrogen Highway” for exporting green or blue ammonia. This is strategically important for Alaska which lacks statewide electrical transmission infrastructure but contains vast renewable and fossil resources located in remote regions with few local markets. Nationally, Alaska-sourced green (hydropower) or blue ammonia has the potential to improve reliability of a Pacific Northwest hydrogen storage hub as a hydrogen-carrier by helping to overcome seasonality of green hydrogen produced from solar or wind energy. Globally, this technology project has significant potential to improve the safety and efficacy of ammonia-rich fuel compositions for use in maritime propulsion and other mid-sized engines.
The purpose of this CRADA is to address key technical challenges specific to the transport of ammonia, a promising carbon-free fuel and hydrogen-carrier, through crude oil pipelines. Specifically, the project seeks to develop novel technologies for preparing liquid ammonia/ hydrocarbon mixtures for the dual purpose of (i) pipeline transport, and (ii) developing advanced marine fuel blends. By demonstrating compatibility with the Trans-Alaskan Pipeline System (TAPS) ammonia/oil blends may improve access to stranded natural gas and help overcome low-flow issues associated with declining oil productivity. In Alaska, this technology enhances the capabilities of TAPS allowing it to function as a statewide “Hydrogen Highway” for exporting green or blue ammonia. This is strategically important for Alaska which lacks statewide electrical transmission infrastructure but contains vast renewable and fossil resources located in remote regions with few local markets. Nationally, Alaska-sourced green (hydropower) or blue ammonia has the potential to improve reliability of a Pacific Northwest hydrogen storage hub as a hydrogen-carrier by helping to overcome seasonality of green hydrogen produced from solar or wind energy. Globally, this technology project has significant potential to improve the safety and efficacy of ammonia-rich fuel compositions for use in maritime propulsion and other mid-sized engines.
Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.
This project develops an innovative, integrated, and transformative approach to mitigate the impact of wind ramping by providing flexible ramping products from wind power. The project will significantly contribute to the reduction of wind integration costs by making wind power dispatchable and allowing the efficient management of wind ramping characteristics. In addition, we collaborated with industry to test and validate the methodology, considering economic and reliability goals, by integrating the proposed methodology into the simulated market frameworks of two independent system operators: California Independent System Operator and the Electric Reliability Council of Texas.
The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.9, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.
This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Further, bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved.
Numerical models have been widely used for the resource characterization and assessment of tidal instream energy. The accurate assessment of tidal stream energy resources at a feasibility or project-design scale requires detailed hydrodynamic model simulations or high-quality field measurements. This study applied a three-dimensional finite-volume community ocean model (FVCOM) to simulate the tidal hydrodynamics in the Passamaquoddy–Cobscook Bay archipelago, with a focus on the Western Passage, to assist tidal energy resource assessment. IEC Technical specifications were considered in the model configurations and simulations. The model was calibrated and validated with field measurements. Energy fluxes and power densities along selected cross sections were calculated to evaluate the feasibility of the tidal energy development at several hotspots that feature strong currents. When taking both the high current speed and water depth into account, the model results showed that the Western Passage has great potential for the deployment of tidal energy farms. The maximum extractable power in the Western Passage was estimated using the Garrett and Cummins method. Different criteria and methods recommended by the IEC for resource characterization were evaluated and discussed using a sensitivity analysis of energy extraction for a hypothetical tidal turbine farm in the Western Passage.
This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.
As the United States transitions toward a clean energy economy, an opportunity exists for redeveloping the more than 17,000 mine land sites located across the nation with clean energy technologies, which have a combined potential for generating more than 85 GW of clean electricity. This report provides an overview of the potential of demonstrating and deploying clean energy projects on current and former mine land. Clean energy project refers to a project that demonstrates one or more of the following technologies: solar; microgrids; geothermal; direct air capture; fossil-fueled electricity generation with carbon capture, utilization, and sequestration; energy storage, including pumped storage hydropower and compressed air energy storage; and advanced nuclear technologies. The report discusses the following technologies and their potential for creating jobs and generating tax revenue that would result in direct and indirect benefits to the local economy: Solar photovoltaics (PV) is being developed on current and former mine land in various parts of the world, including the United States. This approach is attractive because it requires limited infrastructure investment and would utilize the bare surfaces of mines and tailing ponds. Solar resource availability may be greater in the southern regions, including the Interior and Appalachian Basins and the southwestern United States. However, since some mine land sites include areas of significant change in elevation, the deployment of PV on mine land may require sophisticated planning to account for shading and irradiance, or may require regrading of the areas. PV does not create significant environmental risks and generally does not face public resistance; Geothermal systems are often spatially and genetically associated with ore deposits, and in some cases, they have been discovered while in search for epithermal mineral resources. Numerous diverse geothermal applications have been employed at mine land around the world, including power generation, mineral extraction from geothermal brines, process heating, direct use for other mining operations, and direct use for non-mining operations and subsurface energy storage, including geothermal heat pumps. Case studies highlighting these applications provide key lessons relating to identifying drivers and barriers to geothermal resource deployment and can be used to create screening tools for identifying the types and locations of mine land most amenable to utilizing geothermal resources; Carbon capture, utilization, and sequestration technologies include direct air capture (DAC) and enhanced weathering. DAC technologies include air contactors, regeneration systems, and CO 2 compression systems. Captured CO 2 can be converted to valuable feedstocks or possibly injected into abandoned subsurface mines where it would be absorbed by alkaline rock waste and mine tailings or by the porous minerals along the walls of the mine. DAC systems can be coupled with energy sources such as wind, solar, grid, or geothermal. Many DAC systems require a source of water or steam; however, some are expected to be net producers of water. Local impacts of DAC systems are expected to be low, and are related to land footprint, material disposal, and upstream impacts of energy and material production; Compressed air energy storage is an established energy storage technology in salt caverns. It has the potential for implementation in underground mines by pressurizing and storing a large amount of air using electrical compressors when excess electricity is available. When a need for discharge emerges, the air is used to spin turbines and produce the necessary volume of electricity. Abandoned or unused mine openings, including shafts, adits, access tunnels, and mined workings of any orientation, offer potential for vast amounts of compressed air energy storage if the site characteristics meet operational requirements; Pumped hydropower storage can be implemented in surface and subsurface mines. In surface mine applications, both reservoirs may be located in a mine pit or artificial reservoirs made of excavated materials. In subsurface mines, the lower reservoir may be implemented by waterproofing and flooding mine shafts and tunnels. The water is then pumped from the lower reservoir to the upper reservoir during periods of low load and high production, and it is discharged through the turbines during periods of peak demand. The potential environmental damages associated with acidity of mine water or the presence of toxic chemicals incentivizes the development of closed-loop technologies, in which water circulates inside the pumped hydropower facility without being discharged into the external water basins; Advanced nuclear energy technologies include small modular reactors, which can be deployed locally to produce electricity and heat. Such units require seismic stability and a supply of cooling water, but population constraints may exist in some areas. Therefore, remote mine land could represent an optimal location for siting advanced nuclear energy technologies.
Achieving net zero carbon emissions likely requires future power systems to integrate new, flexible energy technologies to accommodate higher levels of capacity from variable renewable energy sources. To determine the optimal deployment of new electricity capacity and to study the likelihood of deployments of new energy technologies, an expansion planning model has been developed as part of the IDAES-Enterprise suite of grid models. The Generation Expansion Planning (GEP) model is a multi-period model in which investment decisions occur yearly, and a Unit Commitment (UC) problem is examined on an hourly timescale. To reduce computational complexity of the GEP model, the UC problem is solved for average “representative days” which leaves out extreme, but relatively common, scenarios in which low renewable generation occurs, leaving the system with inadequacy in capacity. The IDAES-Enterprise GEP model has been modified to include these extreme scenarios while keeping the model reasonably tractable. Specifically, a lazy constraint technique was implemented to check for capacity adequacy on an hourly basis over a large data set of aligned load-wind-solar profiles. As a vast majority of the capacity constraints will not be violated, the technique lowers computational expense by searching for violated capacity constraints over an “iterative manner,” adding those infeasible constraints back into the model. Results on a test case of the Southwest Power Pool shows that the lazy constraint technique significantly reduces retirements and increases installments of natural gas combined cycles and flexible natural gas units. It also reduces some retirements of coal units. These modifications provide a more reasonable estimation of required dispatchable power generation capacity to ensure feasibility during peak net load.
Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.