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Risk-based decision making in reservoir operations
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Jordan Lanini
A significant reservoir operations need in Great Plains Region is to rapidly evaluate a large number of potential reservoir inflow scenarios in order to make short- and mid-term operational decisions. These scenarios are currently created manually by adjusting inflow timing and volume within a daily operations spreadsheet. Risk is then assessed qualitatively to make a final operational decision. GP reservoir operators do not have a systematic, risk-based methodology for operational decisions, nor are we aware of any within Reclamation. This results in operators using professional judgment on an ad-hoc basis to evaluate risk. This research will provide a case study for automating and rapidly evaluating numerous potential inflow scenarios, significantly improving staff efficiency. The project will also provide clarity to the decision-making process. The current lack of operational clarity leaves Reclamation management and staff open to criticism, or in the extreme, to lawsuits, if operations damage water users in some manner.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Optimization
Status
ongoing
Completion Date
2021
- Conventional Hydro
Software Tool Development to Generate Stochastic Hydraulic Simulations using HEC-RAS
Lead Companies
Bureau of Reclamation
Lead Researcher (s)
- Ari Posner
Implementation of this project will facilitate implementation of probabilistic modeling and reduce time required to implement them, by several orders of magnitude. Stochastic simulation and representation of modeling results as probabilistic is a growing field and identified as an important and valuable effort in many fields of science and engineering (Romanowicz & Beven,1996, 1998, 2003; Aronica et. al., 1998, 2002; Bates et. al., 2004; Hall et. al., 2005; Pappenberger et. al., 2005, 2006). Probabilistic modeling is required for most risk analyses associated with large infrastructure projects. Development of this tool will allow HEC-RAS modelers from the most sophisticated regional efforts to the most simple project implemented at the most local level to enter their calibrated and validated model into this software tool and produce probabilistic results, by doing nothing more than putting in the location of their model program file. This tool could save on the order of weeks of time for any project to develop probabilistic results.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Optimization
Status
ongoing
Completion Date
2021
- Conventional Hydro
Stochastic Energy Scheduling
Lead Companies
University of Washington
Lead Researcher (s)
- Adam Greenhall
Large amounts of wind generation have been added to the power system in recent years. However, wind breaks many of the core assumptions in the process used to schedule energy and is particularly difficult to forecast accurately. Rather than scheduling based on a single forecast, stochastic Unit Commitment (UC) minimizes the expected cost over several wind scenarios for the next day. Stochastic UC is often held up as a solution to help alleviate the high costs related to uncertain renewables. Yet there is no widely accepted method for creating high quality stochastic scenarios. In this dissertation, we examine two wind power scenario creation methods – moment matching and analogs. Moment matching is a general technique where scenarios are synthesized to match a set of statistics or moments. We propose a method for estimating these desired moments based on historical wind data. The analogs method looks back in time to find similar forecasts and uses the matching observations from those analogous dates directly as scenarios. This work proposes and tests a simple analogs method based solely on aggregate wind power forecasts. The performance of these methods is tested on a realistic model of the Electric Reliability Council Of Texas (ERCOT) power system based on actual data from 2012. UC and dispatch simulations showed modest stochastic savings for the relatively flexible ERCOT model at 25% wind energy penetration. The scenario creation method and number of scenarios had a significant impact on these stochastic savings. Contrary to our hypothesis and the increase in perfect forecast savings, stochastic savings decreased as wind penetration increased to 30%. Stochastic savings are often largely due to a few high cost events during peak load periods; stochastic UC costs may be higher than deterministic UC for extended periods – generally when demand and marginal prices are low. Together these results paint a more nuanced picture of stochastic UC and provide a roadmap for future scenario creation research.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Optimization
Status
complete
Completion Date
2013
- Conventional Hydro
TIP 345: Advanced Visualization for Improving State Awareness for the BPA Power System
Lead Companies
BPA/Pacific Northwest National Laboratory (PNNL) Alstom Grid USDOE
Lead Researcher (s)
- Scott Winner, BPA
The project developed advanced user-centric modular visualization tools to help present critical information from a large amount of multidimensional data in an effective way for improved power system state awareness. The project demonstrated the visualization tools through examples from pressing challenges for hydro operations as they integrate 15-minute scheduling. Probabilistic flow and load forecasting was also achieved.
Technology Application
Conventional Hydro
Research Category
Interconnect Integration and Markets
Research Sub-Category
Hydraulic Optimization
Status
complete
Completion Date
2017
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Contact Marla Barnes at: marla@hydro.org