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- Pumped Storage
Predicting Unique Market Pumped Storage Significance [HydroWIRES]
Lead Companies
EPRI
Lead Researcher (s)
- Aidan Tuohy, atuohy@epri.com
While no new PSH plants have been developed in the past two decades, there is renewed interest in the technology due to increases in VRE penetration. The objective of this project is to develop a framework and outline the parameters needed to analyze the energy and ancillary services PSH provides to the electricity grid currently and how that value may change as the generation asset mix—especially as it relates to increased penetration of VRE—changes over time. A key focus will be to develop understanding of the trends that impact PSH value so that utilities can determine strategy for further development of PSH. Technology Application
Pumped Storage
Research Category
Interconnect Integration and Markets
Research Sub-Category
Renewable Integration
Status
ongoing
Completion Date
TBD
- Pumped Storage
PSH Portfolio Evaluation and Innovation Study [HydroWIRES]
Lead Companies
ANL
Lead Researcher (s)
- Vladimir Koritarov, koritarov@anl.gov
PSH, with a total of 22 GW of installed capacity in the United States, represent over 95% of the domestic electric energy storage available today. However, no large PSH projects have been commissioned in the last 20 years due to challenges associated with the magnitude of project costs and financing interest during development and construction; the length of time from project investment until project revenue; permitting challenges and construction risks; competition from other storage technologies; and unrecognized energy storage valuation. To address these challenges, research and development efforts have focused on radically new designs and technologies that can dramatically reduce costs and commissioning timelines. In this study, Argonne National Laboratory will perform a landscape analysis to establish the current state of the art of PSH technology, identify promising new concepts and innovations, and highlight technology gaps that have yet to be addressed. Technology Application
Pumped Storage
Research Category
Interconnect Integration and Markets
Research Sub-Category
Markets
Status
ongoing
Completion Date
TBD
- Pumped Storage
Pumped Hydroelectric storage balances a solar microgrid
Lead Companies
Cornell University
Lead Researcher (s)
- Kevin Kircher
We consider the problem of reliably operating a microgrid with solar generation and pumped hydroelectric storage. We show that reliable operation is possible if storage equipment is sufficiently flexible and storage control is sufficiently robust to solar variability. Pumped storage flexibility can be achieved through a ternary configuration; this enables rapid switching between pumping and generating modes. Controller robustness can be achieved through a novel control synthesis method based on convex optimization and resampled historical solar data. The proposed equipment and controller perform well in simulations including twenty months of real solar data at five minute resolution. These results highlight the potential of pumped storage to enable reliable integration of wind and solar power into the grid.
Technology Application
Pumped Storage
Research Category
Interconnect Integration and Markets
Research Sub-Category
Renewable Integration
Status
complete
Completion Date
2017
- Pumped Storage
Pumped Storage Hydro Operations and Benefits in the United States: Review and Case-Studies
Lead Companies
EPRI
Lead Researcher (s)
- Joe Stekli
In recent years, there has been growing interest in how ongoing changes to the electric power resource mix, wholesale markets, and utility operations will affect valuation of existing pumped storage hydro (PSH) plants as well as create opportunities for expansion or repowering of those plants, and construction of new PSH plants. This study conducts comparative case studies of recent and future economic benefits—and any other benefits—of three large existing PSH plants: the New York Power Authority’s (NYPA) Blenheim-Gilboa plant located in New York, and Duke Energy Carolina’s Bad Creek and Jocassee plants, both located in South Carolina. The objective is to examine the policy, market, and utility operating environment for these plants in detail, and to gather both public and certain non-public utility data on recent historical performance and forecasts of future operations. The framework shown here can then be further developed and applied to other existing PSH plants as a basis for improved communication and analysis regarding these plants’ historical and future economic costs and benefits.
Technology Application
Pumped Storage
Research Category
Interconnect Integration and Markets
Research Sub-Category
Markets
Status
complete
Completion Date
2020
- 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 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
Review and Recommendation of Hydrologic Forecast Verification Strategies and Methods
Lead Companies
CEATI International
Lead Researcher (s)
- #0432
Provide a verification framework that can be implemented on hydrologic parameters at a variety of levels of sophistication.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
ongoing
Completion Date
Expected 2020
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Contact Marla Barnes at: marla@hydro.org