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- Pumped Storage
Pumped Storage Hydropower (PSH) FAST Commissioning Prize Technical Analysis
Lead Companies
Oak Ridge National Laboratory (ORNL)
Lead Researcher (s)
- Scott DeNeale (denealest@ornl.gov)
The US energy landscape has undergone major changes over the past 10 years and will continue to see significant changes in future decades as the power grid increases its reliance on variable renewable energy resources. Because of the inherent variability of these resources, renewable energy growth may require additional energy storage capacity to provide flexible load-following capabilities and other grid services that can quickly adjust to changes in energy demand and generation. Pumped storage hydropower (PSH)—one such energy storage technology—uses pumps to convey water from a lower reservoir to an upper reservoir for energy storage and releases water back to the lower reservoir via a powerhouse for hydropower generation. PSH facility pump and generation cycling often follows economic and energy demand conditions. Across the United States, 43 PSH facilities are in operation and 55 projects are in various permitting or licensing stages. Altogether, the 43 operational projects provide the wide majority (95%) of utility-scale electricity storage in the United States. These facilities also provide significant power and nonpower grid benefits, including large-scale electrical system reserve capacity, grid reliability support, and electricity supply-demand balancing through quick-response capabilities and operational flexibility. PSH systems can accomplish these at a scale (e.g., size) and cost that makes these systems highly attractive from a technical standpoint. Although these research concepts are still in their infancy, they demonstrate promising potential as future PSH energy storage technologies. Although PSH has many advantages, development in the United States has effectively stalled since the 1990s, partially because of the magnitude of project costs and financing interest during development and construction, the length of time from project investment until project revenue begins, permitting challenges, construction risks, competition from other storage technologies (e.g., batteries, hydrogen storage), and electricity market evolution and uncertainty. In short, the time, cost, and risk associated with modern PSH development have resulted in limited growth in the United States recently, despite the growing energy storage demand stemming from increased wind and solar power deployment. Technology innovation is needed to help reduce PSH commissioning time, cost, and risk, particularly during the post-licensing phase of project development. To address challenges facing the PSH industry and to improve PSH commissioning timelines, the US Department of Energy (DOE) Water Power Technologies Office (WPTO) initiated the PSH Furthering Advancements to Shorten Time to (FAST) Commissioning Prize project.
Technology Application
Pumped Storage
Research Category
Technology
Research Sub-Category
Future Grid
Status
complete
Completion Date
2020
- Pumped Storage
Pumped Storage Hydropower FAST Commissioning Technical Analysis [HydroWIRES]
Lead Companies
ANL
Lead Researcher (s)
- Vladimir Koritarov, koritarov@anl.gov
This report was developed in tandem with the Furthering Advancements to Shorten Time (FAST) to Commissioning PSH Challenge and represents the underlying technical analysis that informed the competition. Lead by Oak Ridge National Laboratory, the report is designed to address barriers and solutions to PSH development by establishing baseline project development knowledge, defining key aspects of project development, and identifying opportunities to reduce project timelines, costs, and risks. The document’s scope includes post-licensing activities and excludes factors related to permitting or licensing. Technology Application
Pumped Storage
Research Category
Regulatory Management Process
Research Sub-Category
Regulatory Process
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Quantifying Fish Biomass X Distance from Environmental DNA Samples in a Hydrodynamically Complex Environment
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Andrew Schultz
Can monitoring of Environmental DNA (eDNA) in hydraulically dynamic systems be used as a tool for monitoring target species to facilitate optimization of water delivery operations? Our specific research question will investigate how much fish biomass X distance is present when a quantity of DNA is obtained in a water sample. It is not possible to calculate the biomass alone because an infinite number of combinations of fish biomass and distance could produce the same amount of DNA in a water sample. Thus it is necessary to calculate the biomass X distance.
Technology Application
Conventional Hydro
Research Category
Environmental and Sustainability
Research Sub-Category
Fish and Aquatic Resources
Status
ongoing
Completion Date
2021
- Conventional Hydro
Quantifying the Development and Dynamics of Reservoir Delta and Related Backwater Vegetation in the Context of Physical Drivers
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Nathan Holste
The goal of this project is to better to determine whether deltas and backwaters represent significant areas of riparian and wetland habitat on a landscape scale, especially in arid and semi-arid regions. Further, we hypothesize that early successional woody riparian species, which are declining along many regulated river reaches below dams, will be comparatively abundant where reservoirs experience large fluctuations in pool elevations. Understanding the drivers of delta-backwater vegetation can facilitate a predictive understanding of these habitats in response to, for example, changes in water management or in hydrology upstream from reservoirs.
Technology Application
Conventional Hydro
Research Category
Environmental and Sustainability
Research Sub-Category
Shoreline and Riparian Resources
Status
ongoing
Completion Date
2022
- Pumped Storage
Real Time Inertia Monitor Based on Pumped Hydro Operation Signatures [HydroWIRES]
Lead Companies
Oak Ridge National Laboratory (ORNL), University of Tennessee at Knoxville (UTK)
Lead Researcher (s)
- Yilu Liu (liu@utk.edu)
This project seeks to develop a real-time, low-cost, accurate inertia monitoring system for pumped storage hydropower plants. Monitoring inertia is essential for stable system operation, especially in high-renewable grids, but traditional inertia estimation approaches do not work in systems with high penetrations of inverter based resources. Researchers will demonstrate and deploy their monitoring system by the end of the two-year project. Technology Application
Pumped Storage
Research Category
Interconnect Integration and Markets
Research Sub-Category
Future Grid
Status
ongoing
Completion Date
TBD
- Conventional Hydro
Real-Time Condition Health Monitoring and its Application to Hydro Turbines
Lead Companies
Colorado School of Mines
Lead Researcher (s)
- Samuel Dyas
Hydroelectric power has been the number one renewable energy source in the U.S. since the beginning of the industrial revolution and continues to be today. Hydroelectricity is a critical component in the power production grid to keep greenhouse gas emissions and pollution minimized. As such, it is crucial that unexpected shutdowns and unplanned maintenance of hydropower turbines be kept to a minimum, so as to maximize hydroelectricity production. This thesis aims to investigate condition health monitoring (CHM) methods specifically designed for non-intrusive cavitation detection within hydropower turbines. Cavitation is a highly damaging phenomenon common within turbines. When allowed to continue undetected over an extended period of time, cavitation can lead to severe and crippling effects for efficient operation. The application of CHM will lead to less downtime and ultimately more electrical production from hydropower turbines, resulting in the maximization of the U.S.’s number one renewable energy source’s potential. An instrumented cavitation inducing apparatus was designed and built for laboratory testing. The goal of the cavitation inducing apparatus was to produce both non-cavitating and cavitating flows within the available flow range. Also, it was critical for the apparatus to be simple and allow the instrumentation utilized to be placed as close as possible to the cavitation within the flow. Instrumentation including pressure transducers, accelerometers and acoustic emission sensors were used to non-intrusively record cavitation signals from the cavitation apparatus. Multiple signal processing techniques, spanning both the time and frequency domains were utilized to develop methods and metrics to quantify the cavitation monitoring data. Most of the techniques are well documented, including analyzing the root mean square values of the signals and utilizing the Fast Fourier Transform for frequency domain analysis. There were also some signal processing techniques developed throughout this project, specifically for cavitation monitoring. The metrics and methods developed proved successful at identifying volatile flow rates and subsequently the onset of cavitation state change with the flow. It was also determined that time domain signal processing techniques were more successful at cavitation characterization than frequency domain techniques. There is confidence the methods developed for non-intrusive cavitation monitoring through this thesis could be easy transferred to on-site operational test data received from a cavitating turbine and successfully diagnose the onset of cavitation with the flow range.
Technology Application
Conventional Hydro
Research Category
Powerhouse Equipment
Research Sub-Category
Asset Management
Status
complete
Completion Date
2013
- Conventional Hydro
Real-time Forecasting and Hydropower Optimization
Lead Companies
Cornell University
Lead Researcher (s)
- Jonathon Lamontagne
This report summarizes research that I conducted with Hydro Research Foundation (HRF) support. The general focus was hydropower operations optimization using dynamic programming (DP) algorithms. More specifically, there were three areas of concern. The first concern was near real-time operations optimization using a time decomposition algorithm with different representations of uncertainty. The second concern was the value of forecasts and forecast precision to operations optimization. The third concern is the reduction of the computational burden of high-dimensional DP using intelligent sampling of the state space. Idealized reservoir systems based on the Kennebec River in Maine are used as case studies. The objective of hydropower operations optimization is to maximize the expected benefit obtained from a reservoir system from the present time to the end of some planning horizon. In practice time is often broken into decision stages, so the problem becomes the selection of a sequence of releases which maximizes the expected benefit, subject to a set of constraints. The problem is challenging because the future availability of water is uncertain and the benefits and constraints are non-linear in the decision variable. Stochastic DP (SDP) algorithms are well suited to this type of problem. This research used the sampling SDP algorithm, which represents uncertain future flows as a range of flow time series scenarios rather than a Markov process. The first concern addressed in this report is use of a time decomposition algorithm to optimize operation of a reservoir with a sub-daily time step. This involves solving nested optimization models, each with a different planning horizon and time-step, where the longer-term planning models inform the shorter-term models. This allows for rapid optimization of short-term operations, while efficiently considering seasonal objectives and constraints. A key consideration is how uncertainty is represented in each of the nested optimization models. By changing the probability of transitioning from one scenario to another, it is possible to generate a number of decision trees and the assessment of which is most advantageous. Three general transition cases were tested: the case which considers no transitions between scenarios, the case that assumes transitioning to any scenario is equally likely, and the case that bases the probability of transitioning on the flow forecast for the next period. Through testing on an idealized single reservoir system, it was discovered that use of forecasts to estimate transition probabilities is most useful in short-term optimization. In fact, long- and mid-term forecasts provide little improvement over configurations which allow no transitions between scenarios. In contrast, models that assume transition to any scenario is equally likely performed poorly, because they poorly reflect the persistence of flow when computing the long-term future value of water. Thus, it seems a successful algorithm configuration will use forecasts for short-term planning and a reasonable model of the persistence of flow for the long- and medium-term models (i.e. no transitions between scenarios or transitions based on forecasts). The second research concern is the value improved forecast precision to optimization performance, and how this might change depending on the planning horizon and time step considered. For example, does the precision of long-term forecasts significantly affect the performance of the time decomposition algorithm, or is the precision of short-term forecasts more important. This is examined by applying the same model configurations previously described, but with varying forecast precision. It was determined that for summer operations optimization, long- and mid-term forecast precision contributes very little to the efficient operation of the reservoir. As was discussed above, use of forecasts for long and mid-term planning is of little value, thus it is not surprising that forecast precision for longer-term planning is not important. In contrast, the performance of the algorithm is highly sensitive to the precision of the short-term forecast. The third research concern is the reduction of the computational burden of high-dimensional DP. In DP, the state of the system in any time is described by state variables. In reservoir optimization, the state variable is reservoir storage, so the addition of a reservoir to the system involves the addition of a state variable, and a new dimension to the state space. This exponentially increases the difficulty of numerically solving the optimization problem. However, many points in the state-space represent unreasonable combinations of storages. The proposed Corridor DP algorithm saves computational effort by focusing the optimization effort on the region of the state space which the system will likely visit. One challenge of implementing Corridor DP is that cubic spline interpolation can perform poorly when basis points are irregularly placed. Instead, this research used radial basis function (RBF) interpolation, which is not constrained by the placement of basis points. Because the Corridor DP optimizes over the entire state space, not just inside the corridor, a coarse spline is fit to a few points in the extremes of the state space. An RBF surface models the deviation from the spline in the corridor region. The Corridor DP algorithm is applied to an idealized four reservoir system. The corridor region was determined through simulation of the historic operation of the actual system. In numerical trials, an early implementation of the Corridor DP algorithm achieves a given relative error with about 1/2 the computational effort of DP with spline interpolation, and only 1/10 the computational effort of DP with linear interpolation. Theoretical results are presented which suggest how the radial basis function interpolation and corridor selection criteria might be improved in the future. This report focuses on the research conducted with HRF support. This research has focused on three concerns. First, the merit of various representations of uncertainty. Second, the value of forecast precision to the optimization effort. Finally, the reduction of the computational burden of solving highdimensional DP problems. Results addressing each concern are presented, as well as suggestions for future work, Before ending this executive summary, I’d like to express my gratitude to the Hydro Research Foundation for their support over the last three years. In addition to the financial support, they have provided many industry and government contacts who have proved invaluable as this research has evolved.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2013
- Conventional Hydro
Reduced Order Description of Experimental Two-Phase Pipe Flows: Characterizations of Flow Structures and Dynamics via Proper Orthogonal Decomposition
Lead Companies
University: Portland State University
Lead Researcher (s)
- Bianca Viggiano
Multiphase pipe flow is investigated using proper orthogonal decomposition for tomographic X-ray data, where holdup, cross-sectional phase distributions and phase interface characteristics within the pipe are obtained. Six cases of stratified and mixed flow with water content of 10%, 30% and 80% are investigated to gain insight into effects of velocity and proportion of water on the flow fields. Dispersed and slug flows are separately analyzed to consider the added interface complexity of the flow fields. These regimes are also highly applicable to industry operational flows. Instantaneous and fluctuating phase fractions of the four flow regime are analyzed and reduced order dynamical descriptions are generated. Stratified flow cases display coherent structures that highlight the liquid-liquid interface location while the mixed flow cases show minimal coherence of the eigenmodes. The dispersed flow displays coherent structures for the first few modes near the horizontal center of the pipe, representing the liquidliquid interface location while the slug flow case shows coherent structures that correspond to the cyclical formation and break up of the slug in the first 5 modes. The low order descriptions of the high water content, stratified flow field indicates that main characteristics can be captured with minimal degrees of freedom. Reconstructions of the dispersed flow and slug flow cases indicate that dominant features are observed in the low order dynamical description utilizing less than 1% of the full order model. POD temporal coefficients a1, a2 and a3 show a high level of interdependence for the slug flow case. The coefficients also describe the phase fraction holdup as a function of time for both dispersed and slug flow. The second coefficient, a2, and the centerline holdup profile show a mean percent difference below 9% between the two curves. The mathematical description obtained from the decomposition will deepen the understanding of multiphase flow characteristics and is applicable to long distance multiphase transport pipelines, fluidized beds, hydroelectric power and nuclear processes to name a few
Technology Application
Conventional Hydro
Research Category
Water Conveyance
Research Sub-Category
Penstock
Status
complete
Completion Date
2017
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