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- Conventional Hydro
Influence of Deregulated Electricity Markets on Hydropower Generation and Downstream Flow Regime
Lead Companies
University of North Carolina-Chapel Hill
Lead Researcher (s)
- Jordan Kern
Hydropower producers face a future beset by unprecedented changes in the electric power industry, including the rapid growth of installed wind power capacity and a vastly increased supply of natural gas due to horizontal hydraulic fracturing (or “fracking”). There is also increased concern surrounding the potential for climate change to impact the magnitude and frequency of droughts. These developments may significantly alter the financial landscape for hydropower producers and have important ramifications for the environmental impacts of dams. Incorporating wind energy into electric power systems has the potential to affect price dynamics in electricity markets and, in so doing, alter the short-term financial signals on which dam operators rely to schedule reservoir releases. Chapter 1 of this doctoral dissertation develops an integrated reservoir-power system model for assessing the impact of large scale wind power integration of hydropower resources. Chapter 2 explores how efforts to reduce the carbon footprint of electric power systems by using wind energy to displace fossil fuel-based generation may inadvertently yield further impacts to river ecosystems by disrupting downstream flow patterns. Increased concern about the potential for climate change to alter the frequency and magnitude of droughts has led to growing interest in “index insurance” that compensates hydropower producers when values of an environmental variable (or index), such as reservoir inflows, crosses an agreed upon threshold (e.g., low flow conditions). Chapter 3 demonstrates the need for such index insurance contracts to also account for changes in natural gas prices in order to be cost-effective. Chapter 4 of this dissertation analyzes how recent low natural gas prices (partly attributable to fracking) have reduced the cost of implementing ramp rate restrictions at dams, which help restore subdaily variability in river flows by limiting the flexibility of dam operators in scheduling reservoir releases concurrent with peak electricity demand.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2014
- 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
Managing Hydrologic Financial Risk in Hydropower Production with Index Insurance Contracts
Lead Companies
University of North Carolina-Chapel Hill
Lead Researcher (s)
- Ben Foster
Hydropower generators rely on stream flows to serve as “fuel,” which can lead to volatility in revenues that is financially disruptive. This vulnerability to hydrologic uncertainty, and the possibility of increased hydrologic variability in the future, suggests that hydropower producers need new tools for managing these financial risks. This study uses an integrated hydro-economic model of the Roanoke River Basin to characterize the financial risk faced by hydropower generators as a result of changes in water supply. Several index-based financial instruments are developed and evaluated using 100-year simulations of Kerr, Gaston and Roanoke Rapids Dam operations. Index basis risk, pricing, and contract design are all explored. Contracts built on average daily inflow are shown to be capable of reducing water supply risk at a range of levels, with even significant levels of risk (i.e. inflows under 75% of average) mitigated at a relatively low cost (under 3% of average revenues).
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
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
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
- Conventional Hydro
Roadmap for Applied Statistical Analysis Techniques for Hydro Generation and Runoff
Lead Companies
CEATI International
Lead Researcher (s)
- #0430
an actionable tool that guides hydro operators to appropriate statistical methods to process the vast amounts of data related to inflow forecasts and runoff determination.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2020
- Conventional Hydro
The Effects of Climate Change on the Water Resources and Hydropower Production Capacity of the Upper Colorado River
Lead Companies
Colorado School of Mines
Lead Researcher (s)
- Marina Kopytkovskiy
The Upper Colorado River Basin (UCRB), comprised of the Colorado and Gunnison River basins, is regulated by 17 major reservoirs to provide water supply, flood control, and hydropower. It is the prime water source for much of the western United States, as well as key wildlife and fish habitat. Climate change is an issue of concern on the basin due to the sensitivity of snow accumulation processes that dominate runoff generation within the region. Climate models project an average warming of up to 4o F, coupled with a decline in precipitation falling as snow. There is no numerical consensus of the magnitude of change in precipitation, but there is general agreement that precipitation changes will be exacerbated by increased evapotranspiration rates, reducing overall runoff. This is expected to cause a decline in runoff and hydropower generation capacity. Potential impacts of climate change on the hydrology and water resources of the UCRB were assessed through a comparison of simulated stream flow, temperatures, and reservoir volumes and storage levels. Future climate conditions derived from climate centers: Meteorological Research Institute (MRI-CGCM2.3.2), Canadian Centre for Climate Modeling and Analysis (CGCM3.2 T47), and the Center for Climate System Research at the University of Tokyo with the National Institute for Environmental Studies and Frontier Research Center for Global Change (MIROC 3.2) under A2 and B1 emission scenarios were compared to historical conditions. From the joint venture of the United States Bureau of Reclamation (USBR) and other research and university facilities, bias-corrected constructed dialogues (BCCA) daily downscaled precipitation and climate data was processed and used to drive the Watershed Analysis Risk Management Framework (WARMF) hydrologic model to simulate future changes in the UCRB. WARMF performs daily simulations of snow and soil hydrology to calculate surface runoff and groundwater accretion to river segments, lakes, and reservoirs. All model scenarios project a reduction in 21st century flows, though the magnitude varies with location and elevation. Results illustrate basin-wide temperature increases at low elevations, with extreme seasonality increasing at high elevation stations in future climate. Reservoir levels in Blue Mesa declined more than 70%, but other reservoirs showed varying results dependent on location and climactic conditions. The resultant climate change scenarios will motivate adaptive watershed planning and management decisions and policies in response a changing climate and mitigate future concerns.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2015
- Conventional Hydro
TIP 258: Development of a State-of-the-Art Computational Framework and Platform for the Optimal Control of Multi-Reservoir Systems under Uncertainty
Lead Companies
Oregon State University (OSU)
Lead Researcher (s)
- Dennis Mai, BPA
- Dr. Arturo Leon, OSU
The research produced a robust and computationally efficient hybrid and parallelized framework for the real-time operation of multi-objective and multi-reservoir systems that accounts for uncertainty and flexibility. The resulting model could potentially replace the current tool used by BPA for short‐term operation of the FCRPS and be used as the main computational engine for future real‐time operation of this system under different streamflow and load scenarios.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2015
- Conventional Hydro
TIP 259: Short-Term Hydropower Production and Marketing Optimization (HyProM)
Lead Companies
Deltares USA
Lead Researcher (s)
- Chris Allen, BPA
- Dirk Schwanenberg, Deltares NL
This project advanced a state-of-the-art software infrastructure for short-term management of the FCRPS. It included multi-objective deterministic and stochastic optimization techniques with a modular, open-source, computationally efficient and multithreaded IT design. The project addressed the need for more precise knowledge of future stream flow, wind reserve, and power load, with a user-friendly tool to integrate those entities. This was vital to solving the multi-objective problem of reservoir management.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
complete
Completion Date
2015
- Conventional Hydro
TIP 416: CEATI – Hydropower Operations and Planning Program (HOPIG)
Lead Companies
CEATI
Lead Researcher (s)
- Erik Pytlak, BPA
The long term strategic direction of this group will be to develop new and innovative technological approaches and options to support the role of the water manager in managing and enhancing the underlying fundamental value of the industry, and to provide a knowledge-based leading edge technological resource to assist the hydropower industry in meeting its many new challenges.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Forecasting
Status
ongoing
Completion Date
2020
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Contact Marla Barnes at: marla@hydro.org