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Arnold Engelmann
Decision Support for Dam Operations
Senior Developer, Solutions Software, DHI Portland
Carter Borden, Senior Hydrologist, DHI Boise Ryan Kilgren, Water Resource Engineer, DHI Portland Dylan Kime, Developer, DHI South Africa
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Problem
? ?
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Solution: Bliss Dam DSS
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Workflow Analysis
Define use cases
Project Start-up
Design
Analyze Build and test
Deploy
Version 1, Version 2, …
Testing And
Feedback
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Time12:0013:0014:0015:0016:0017:0018:00
…
Q in55565757585958…
Q out56575960626360…
T0
Time
Q (c
fs)
Discharge
Reset
ProjectedHistoric
Q out
Q in
Q in = inflow to reservoirQ out = dam release
Iterative Design Process
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DSS Model
Rainfall Stream Flow Radar Rainfall
Multi-user Interface
Multiple Data
Sources
Data Management
Analysis
Decision Support Systems (DSS)
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Solution • 3-Tier Architecture
• Monitoring and Notification
Data
Microsoft SQL Server DIMS
Business
Hydraulic Model (MIKE 11) Optimization
User Interface
Web Service Dashboard Manager
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Data Sources and Management
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Data Sources and Management • Problem: How to Manage Data
• Why a separate database?
• DIMS: Dynamic Information Management System
– Real Time Data Management – Data Validation – Scheduled Execution
• DIMS Online vs. Offline
– Data “pushed” to SQL Server • Turbine Discharge • Spillway Discharge (Calculated) • Water Levels
SQL Server
DIMS
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Input Data Quality
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Input Data Quality • Discharge
• Water Level
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Real Time Data Validation • Sensor data quality can be inspected, using simple but robust methods,
for frequent errors, including: –Missing Data –Measurement values out of range –Peaks (rate of change) –Constant measurement values –Drift
• Each error type is assessed using confidence functions with physically based thresholds
• Overall confidence is determined as the minimum confidence for the error types assessed
Citation: Lynggaard-Jensen, A., Frey, M.: Use and Reporting of Data - Data Handling and Validation. In Online Monitoring for Drinking Water Utilities. Published by American Water Works Association Research Foundation and CRS Proaqua (2002). ISBN 1-58321-183-7.
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Real Time Data Validation • Gap Filling
• Range
• Rate of Change
• Running Variance
• Resulting Confidence
Sen
sor v
alue
Con
fiden
ce
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Real Time Data Validation • The database
archiving real time data is appended with the overall confidence at each timestep
• Quality descriptions are assigned to overall confidence ranges, such as: – > 80 = “Good” – 60-80 = “Fair” – <60 = “Poor”
• Automated warning messages and actions can be implemented based on specified confidence values
Sen
sor v
alue
Con
fiden
ce
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Discharge Planning
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Error Analysis / Uncertainty
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Error Analysis / Uncertainty • Sources of Error
– Measurement Error – Modeling Error
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Error Analysis / Uncertainty • Error is highest during low discharge values on sequentially activated
turbines • Error decrease as turbines releases are increased
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Error Analysis / Uncertainty • Monte Carlo Results
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Discharge Limits
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Optimization Routine • Objective = find maximum and minimum allowable discharge operations that
meet the compliance rules for downstream location
• Compliance Rules – Stage increases ±3 feet / hour OR ±1 feet / hour (continuous limit) – Stage increases ±6 feet / day OR unlimited (daily limit) – Discharge ≥ 4500 cubic feet /second (provides 8.12 feet of depth at
compliance location)
• Optimization steps 1. Select a set of three discharge values, which is the proposed solution. 2. Estimate the future stage at the compliance gauge by using the MIKE11
model. 3. Check how the future stage at the compliance gauge compares to the
calculated limit. It is considered the best choice so far when it is closest to the calculated limit without exceeding it.
4. Evaluate a convergence criteria to stop the iteration, or to go back to guess another solution (i.e., step 1).
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Optimization Routine
1. Select set of 3 discharge values for
the next 3 hours
2. Estimate future stage at compliance gauge by using the
MIKE 11 model
3. Is the future stage closest to the
calculated limit without exceeding?
Set of discharge values is the best
choice so far
4. Is convergence criteria met?
End
No
Yes
No
Yes
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Optimization Routine
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Optimization Routine
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Optimization Routine
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Optimization Routine
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Optimization Routine
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Optimization Routine
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User Interface • Web Interface
– One Screen – Dashboard Manager – Customized Components
• System Monitoring and Notification Application
– Configurable – Web Service – Items Checked
• Server Running • Model Service Running • Data Validation Running • Data Quality Warning • Gap Check • Age of Optimization Results
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Questions?