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- Conventional Hydro
Autonomous acoustic receiver system for 3D tracking and monitoring real-time fish survival
Lead Companies
PNNL
Lead Researcher (s)
- Jayson Martinez
This project is developing two technologies related to JSATS autonomous acoustic receivers: 1) a system which can be used to estimate fish survival in near real-time for optimizing hydropower operations, hereafter referred to as the Real-time Autonomous Acoustic Detection System (RAADS); 2) an advanced machine learning based 3D acoustic-tagged fish tracking system, hereafter referred to as the Machine Learning Autonomous Tracking System (MLATS).RAADS will allow detection information from acoustic-tagged fish to be broadcast from underwater autonomous acoustic receivers to a surface-based receiver that would then transmit the data to an offsite location. This will generate timely information that can be input into models that would allow metrics of fish survival and behavior to be calculated and displayed on a dashboard.
Technology Application
Conventional Hydro
Research Category
Environmental and Sustainability
Research Sub-Category
Fish and Aquatic Resources
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Data-Driven Approach for Hydropower Plant Controller Prototyping using Remote Hardware-in-the-Loop (DR-HIL)
Lead Companies
NREL
Lead Researcher (s)
- Mayank Panwar, Mayank.panwar@nrel.gov
Real-time prototyping of hydropower plant controls is important for reducing the cost and the risk of field deployment. In this project, we propose to 1) collect design and operational data from actual hydro plants and 2) use a physics-informed machine learning approach for real-time emulation of hydropower plants, including the hydro turbine and hydrodynamics. The data-driven models will be interfaced with digital real-time simulation at NREL’s Flatirons campus for hardware-in-the-loop (HIL) testing of the governor hardware device or controller-HIL (CHIL). The proposed approach will also establish the connectivity-based remote CHIL testing capability using real-time data streams from an actual hydro plant. This integrated data-driven hydro-plant emulation with CHIL will be used to prototype hydro-governor controls and, in the future, provide an opportunity to test hydropower integrated with various technologies (e.g., conventional and renewable generation, energy conversion, etc.) as HIL.
Technology Application
Conventional Hydro
Research Category
Powerhouse Equipment
Research Sub-Category
Governor
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Determination of Optimal Operating Schemes for a Multi-Reservoir System Under Environmental Constraints
Lead Companies
Vanderbilt University
Lead Researcher (s)
- Hydropower Foundation
In stratified reservoirs, both dam tailwater discharge and thermal plant intake water quality and temperature can be highly dependent on structure depth. A twodimensional laterally-averaged model allows for better prediction of water quality over time at specific depths. Because high-fidelity models are typically too computationally expensive for direct inclusion within optimization algorithms, water quality is incorporated using one dimensional models are simple flow requirements. Water quality predictions can be incorporated within the optimization process through using surrogate modeling methods, in this application artificial neural network (ANN) models. ANNs are flexible machine learning tools for function approximation composed of a structure of neurons assembled within a multi-layer architecture. They are capable of handling large amounts of training data and modeling nonlinear dynamic systems, making ANNs a well-suited method for this application. This report illustrates the development of ANN models to emulate the hydrodynamic and water quality modeling capabilities of the high-fidelity, two-dimensional CE-QUAL-W2 (W2) model, as well as a linked riverine reservoir system optimization process which accounts for energy generation, water balance and hydraulics, and compliance point water quality. A process for hourly hydropower generation planning is demonstrated on a pair of reservoirs linked in series. The two reservoirs are U.S. Army Corps of Engineers projects with hydropower capabilities on the Cumberland River near Nashville, Tennessee, USA. The content presented here is largely a combination of technical papers previously presented at the HydroVision International conference (Shaw et al., 2015, 2016).
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2016
- Conventional Hydro
Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems [HydroWIRES]
Lead Companies
Stevens Institute of Technology
Lead Researcher (s)
- Lei Wu, lwu11@stevens.edu
One barrier to the optimal operation of hydropower plants is a lack of accurate inflow forecast information. This is true for both seasonal inflow expectations, which affects long-term planning for bulk energy production, and daily inflow expectations, which affects flexibility, and is further exacerbated in the case of cascading plants. The proposed work aims to develop: 1) accurate machine-learning based closed-loop forecast models for seasonal and day-ahead water inflows; and 2) enhanced cascading hydroelectric system (CHE) modeling and data-driven optimization approaches to explore multidimensional spatial-temporal hydropower operational flexibility potentials with proper consideration of unique characteristics of CHE systems. Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Machine Learning for Improving Sub-Seasonal Forecasting
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Ken Nowak
Reclamation concluded the prize competition "Sub-Seasonal Climate Forecast Rodeo" in June 2019 with a symposium hosted at NOAA headquarters in Silver Spring, MD. Several winning teams that were able to outperform operational forecasts from NOAA used machine learning Machine Learning for Improving Sub-Seasonal Forecastingtechniques to produce their forecasts. This funding would allow Reclamation to partner with those teams or pursue refinement of their solutions by other means. In addition to improving sub-seasonal forecast skill, Reclamation will be able to build and enhance internal machine learning capacity.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
ongoing
Completion Date
2020
- Conventional Hydro
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Lead Companies
Upstream Tech
Lead Researcher (s)
- Frederik Kratzert
- Daniel Klotz
- Mathew Herrnegger
Long short‐term memory (LSTM) networks offer unprecedented accuracy for prediction in ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS data set using k‐fold validation, so that predictions were made in basins that supplied no training data. The training and test data set included ∼30 years of daily rainfall‐runoff data from catchments in the United States ranging in size from 4 to 2,000 km2 with aridity index from 0.22 to 5.20, and including 12 of the 13 IGPB vegetated land cover classifications. This effectively “ungauged” model was benchmarked over a 15‐year validation period against the Sacramento Soil Moisture Accounting (SAC‐SMA) model and also against the NOAA National Water Model reanalysis. SAC‐SMA was calibrated separately for each basin using 15 years of daily data. The out‐of‐sample LSTM had higher median Nash‐Sutcliffe Efficiencies across the 531 basins (0.69) than either the calibrated SAC‐SMA (0.64) or the National Water Model (0.58). This indicates that there is (typically) sufficient information in available catchment attributes data about similarities and differences between catchment‐level rainfall‐runoff behaviors to provide out‐of‐sample simulations that are generally more accurate than current models under ideal (i.e., calibrated) conditions. We found evidence that adding physical constraints to the LSTM models might improve simulations, which we suggest motivates future research related to physics‐guided machine learning.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2019
- Marine Energy
TSDat: An open_source Data Standardization Framework for Marine Energy and Beyond
Lead Companies
NREL
Lead Researcher (s)
- Rebecca Fao
- Frederick Driscoll
Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems. Technology Application
Marine Energy
Research Category
Research Sub-Category
Status
complete
Completion Date
2020
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Have questions about WaRP?
Contact Marla Barnes at: marla@hydro.org