Existing Hydropower Assets Plant Dataset FY20
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Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.
Marine renewable energy (MRE) harnesses ocean-based resources such as waves, tides, currents, and thermal or salinity gradients for sustainable power generation. It has the potential to complement existing renewable resources, support remote communities, and contribute to decarbonization efforts. However, understanding the hydrodynamic forces created by MRE devices and their impacts on marine life is critical for responsible deployment. Here, to address these concerns, advanced sensor devices, including the Marine Sensor Fish (MSF), Sensor Fish Mini (SF Mini), and Flexible Sensor Fish (FSF), were developed to measure interactions between aquatic organisms and MRE systems. This paper details the design, manufacturing, calibration, and field deployment of these sensor suites, highlighting their ability to capture key physical stressors such as shear forces, pressure changes, and collision impacts. The MSF successfully evaluated turbine interactions at a tidal turbine in the Salish Sea, capturing data on turbulence, collision impact, and pressure gradients. The SF Mini validated hydrodynamic conditions in scaled hydraulic models, supporting computational fluid dynamics simulations. The FSF, with its flexible silicone body, measured species-specific impacts in turbulent environments. This research demonstrates the potential of Sensor Fish technology to advance sustainable marine energy systems by reducing biological impacts and informing environmentally sustainable designs.
The National Renewable Energy Laboratory initiated a Prize with support from Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL), and sponsored by the U.S. Department of Energy Water Power Technologies Office (DOE WPTO) to encourage ideas to reduce the time to commissioning for PSH projects. As a result, nine finalists have been chosen to develop their concepts in advance of the FAST Prize Pitch Contest to be held on October 7, 2019. The National Labs will provide technical and business advisement to the noted FAST Prize finalists in preparation for this Pitch Contest.
These slides summarize and explain empirical data on interconnection timelines and costs for distributed energy projects connected to U.S. utility distribution systems. The study explores how interconnection metrics vary across states, project sizes, technology types, and over time to indicate progress toward simple, fast, and affordable interconnection. Data encompasses 188 utilities in 24 states. Project types include biomass/biofuels, coal, hydropower, natural gas/oil, solar, storage, wind, hybrid, and
Pumped storage hydropower is often overlooked in future grid planning efforts, in part because the cost to build a new facility is not always clear. That's why the National Renewable Energy Laboratory (NREL), along with the U.S. Department of Energy's Water Power Technologies Office, is developing improved cost estimates and cost models to reduce that uncertainty and give developers the data they need to decide where and when to construct new PSH facilities to support the country's evolving energy grid.
The U.S. electric sector is rapidly evolving, with widespread renewable energy deployment, building and transport electrification, and nuanced decarbonization policies. Climate change influences the supply and demand for electricity by altering water resources for cooling and hydropower, thermal plant efficiencies, and heating and cooling demand. This research combines global climate model data, asset-level water and streamflow modeling, and electric sector capacity expansion modeling to study climate-water-electricity interactions across myriad future climate and electricity scenarios that consider electrification and decarbonization using renewable, hydrogen, carbon capture, and nuclear technologies. Multi-model integration leads to insights into power-water sector interactions and their impacts on grid economics and environmental outcomes.
In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.
The National Renewable Energy Laboratory (NREL) initiated a Prize with support from Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL), and sponsored by the U.S. Department of Energy Water Power Technologies Office (DOE WPTO) to encourage ideas to reduce the time to commissioning for PSH projects. As a result, nine finalists have been chosen to develop their concepts in advance of the FAST Prize Pitch Contest to be held on October 7, 2019. The national labs will provide technical and business advisement to the noted FAST Prize finalists in preparation for this Pitch Contest.
Implementation of effective downstream passage for silver-phase American Eel (Anguilla rostrata) at hydropower dams using methods that are economically feasible has been a challenge for many project owners. Due to the potential for high mortality rates during turbine passage, resource agencies have focused on mandatory prescriptions that prevent entrainment by physically excluding eels (i.e., narrow trash rack bar spacing) or eliminating turbines as a passage route (i.e., unit shutdowns at night when eel movement is greatest). Both of these options can significantly impact the economic viability of a project. Consequently, there is a need for alternative technologies that can provide safe and effective downstream passage for silver eels without having a major impact on the ability of a project to generate power during the eel downstream migration period. This need is of particular concern now because about 300 hydropower projects within the range of American Eel freshwater habitats are scheduled to be relicensed by the Federal Energy Regulatory Commission (FERC) in the next 15 years
This technical report summarizes work that was conducted by the University of Massachusetts Amherst and the U.S. Geological Survey, along with other project partners, on the Fishway Entrance Palisade, a project funded through the Department of Energy’s (DOE) funding opportunity titled ‘Innovative Solutions for Fish Passage at Hydropower Dams’ (DE-FOA-0001662). The period of performance spanned from September 1, 2018 through September 30, 2021. The Entrance Palisade is a novel fish passage engineering technology designed to provide more favorable entry conditions for fish into fishways and to reduce costs relative to conventional fishway auxiliary water systems.
Hydroelectric plants require precise and timely estimates of rain, snow and other hydrometeors for operations. However, it is far from being a trivial task to measure and predict precipitation. This paper presents the linkages between precipitation science and hydroelectricity, and in doing so it provides insight into current research directions that are relevant for this renewable energy. Methods described include radars, disdrometers, satellites and numerical models. Two recent advances that have the potential of being highly beneficial for hydropower operations are featured: the Global Precipitation Measuring (GPM) mission, which represents an important leap forward in precipitation observations from space, and high performance computing (HPC) and grid technology, that allows building ensembles of numerical weather and climate models.
Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), the National Renewable Energy Laboratory annually provides an organized and centralized set of such cost and performance data. The ATB uses the best information from the Department of Energy national laboratories' renewable energy analysts. The ATB has been reviewed by experts and it includes the following electricity generation technologies: land-based wind, offshore wind, utility-scale solar photovoltaics (PV), commercial-scale solar PV, residential-scale solar PV, concentrating solar power, geothermal power, hydropower, utility-scale battery storage. EIA data for coal, natural gas, nuclear, and conventional biopower are included for reference. This webinar presentation introduces the 2020 update to the ATB Electricity data and documentation.
The National Renewable Energy Laboratory (NREL) initiated a Prize with support from Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL), and sponsored by the U.S. Department of Energy Water Power Technologies Office (DOE WPTO) to encourage ideas to reduce the time to commissioning for PSH projects. As a result, nine finalists have been chosen to develop their concepts in advance of the FAST Prize Pitch Contest to be held on October 7, 2019. The national labs will provide technical and business advisement to the noted FAST Prize finalists in preparation for this Pitch Contest.
The interconnection among food–energy–water (FEW) systems in meeting societal demands is broadly acknowledged. Similarly, competitive or synergistic allocations of water and energy resources for agricultural production, manufacturing, and human consumption are understood, and their economic impacts can be predicted. Far less appreciated and understood are the outcomes of the FEW nexus in response to operation changes in agricultural practices and the associated technological innovations for future generations. Also, the inter-scale and feedback effects of emerging technology-driven resource reallocation and decision-making on FEW systems are largely unknown. For example, how do the agroeconomic feedbacks of intelligent technologies influence the FEW nexus of agricultural production under environmental and demographic changes? How does the necessary water allocation for powering non-powered dams and pumped-storage hydropower generation influence agricultural production and municipal water supply maintenance? How do solar and wind energy farms influence land use for agriculture and the rural economy? In turn, how can the generated solar and wind energy help reduce the cost of groundwater extraction or water desalination?
The federal government has initiated an aggressive set of policies to achieve a net-zero carbon emission goal for the electricity sector by 2050. As a result, rapid growth in deployment of renewable energy technologies is expected. Most commercially mature technologies are temporally variable and do not provide grid inertia, while renewable technologies with high projected deployment have intermittent generation methods. Energy storage technologies are needed to both dispatch power on-demand and help provide the needed grid inertia. Pumped storage hydro (PSH) is a well-established technology that has gained renewed interest in recent years offering energy-balancing, grid stability, control of electrical network frequency, and large-scale storage capacity. For widespread adoption of PSH, more information is needed regarding its current life cycle environmental impacts. The objective of this study is to perform a full life cycle assessment (LCA) of new closed-loop PSH in the U.S. The functional unit for this study is 1 kWh of electrical power delivered to the grid and the base case project lifetime is 80 years. The life cycle inventory for this project accounts for all material and energy flows associated with the green-field construction, operation, maintenance, and decommissioning of a closed-loop PSH plant in the U.S. Collected data represents a range of potential PSH specifications and geographic locations coming from all prospective closed-loop PSH installations in the U.S. with data available. In addition, existing PSH installations are used to provide assumptions for inventory inputs. Results presented will include the global warming potential (GWP IPCC 100a) and Energy Return on Investment (EROI) from our base case (average PSH installation) as well as from scenario analyses and model sensitivity. These results will be compared to the impacts from existing PSH sites and alternate storage technologies. Methods align with the assumptions and guidelines put in place by previous PSH LCAs to ensure an accurate comparison with the results from this report.
The North American electric power system is undergoing significant change, with renewable resources now contributing more generation than ever before. This transformation is poised to continue given decreasing technology costs and ambitious decarbonization goals at the federal, state, local, corporate, and consumer levels. The North American Renewable Integration Study (NARIS) aims to inform grid planners, utilities, industry, policymakers, and other stakeholders about challenges and opportunities for continental system integration of large amounts of wind, solar, and hydropower to support a low-carbon future grid. The National Renewable Energy Laboratory (NREL) conducted a detailed, continent-wide analysis with planning scenarios of transmission, generation, and demand to reach 80%–92% carbon reductions (from 2005) for the Canadian electric power system, and up to 80% reductions continent-wide. We used a suite of models to study future scenarios and gain insights, including potential impacts on costs, emissions, resource adequacy, and the specific technologies that help enable the transition to a low-carbon grid. Our analysis had a particular focus on the potential role of cooperation among the three North American countries and between regions within each country, and how transmission can support sharing of supply and demand diversity. The NARIS project began in 2016. This report describes a U.S. perspective in coordination with the U.S. Department of Energy, and a companion report describes a Canadian perspective in coordination with the Natural Resources Canada. NARIS was an extension of a previous body of work, including the Western Wind and Solar Integration Study, the Eastern Renewable Generation Integration Study, Interconnections Seam Study, and the Pan Canadian Wind Integration Study. NARIS analyzed the entire continent in detail while studying higher renewable generation than previous studies. The scenarios in NARIS were informed by the goals in the Mid-Century Strategies for the Paris Agreement in each country.
The North American electric power system is undergoing significant change, with renewable resources now contributing more generation than ever before. This transformation is poised to continue given decreasing technology costs and ambitious decarbonization goals at the federal, state, local, corporate, and consumer levels. The North American Renewable Integration Study (NARIS) aims to inform grid planners, utilities, industry, policymakers, and other stakeholders about challenges and opportunities for continental system integration of large amounts of wind, solar, and hydropower to support a low-carbon future grid. The National Renewable Energy Laboratory (NREL) conducted a detailed, continent-wide analysis with planning scenarios of transmission, generation, and demand to reach 80%–92% carbon reductions (from 2005) for the Canadian electric power system, and up to 80% reductions continent-wide. We used a suite of models to study future scenarios and gain insights, including potential impacts on costs, emissions, resource adequacy, and the specific technologies that help enable the transition to a low-carbon grid. Our analysis had a particular focus on the potential role of cooperation among the three North American countries and between regions within each country, and how transmission can support sharing of supply and demand diversity. The NARIS project began in 2016. This report describes a Canadian perspective in coordination with the Natural Resources Canada, and a companion report describes a U.S. perspective in coordination with the U.S. Department of Energy. NARIS was an extension of a previous body of work, including the Western Wind and Solar Integration Study, the Eastern Renewable Generation Integration Study, Interconnections Seam Study, and the Pan Canadian Wind Integration Study. NARIS analyzed the entire continent in detail while studying higher renewable generation than previous studies. The scenarios in NARIS were informed by the goals in the Mid-Century Strategies for the Paris Agreement in each country.