ESET ALEMU
WEST Consultants, Inc.
Bellevue , Washington
Presentation Outline

Purpose for DSS

Project Area Description

Construction Steps

Experimental Design

Results and Implications
Purpose of DSS





Construct a system that represents the physical configuration
and operational aspects of a of the Jackson Hydropower Project
System wide management tool used for identifying and
evaluating operation alternatives in a multipurpose system
Provide a basis for effecting a collective understanding between
the different departments involved with managing the project
Demonstrate the value of forecasts
o Hydrologic Forecasts (Ensemble Streamflow)
o Energy Price Forecasts
Demonstrate the time of year (season) when forecasts are the
most valuable
Project Description
Project Description
▶ The project is located on the Sultan River which drains into the
Skykomish River in the Snohomish River Drainage
▶ The system is fed by snowmelt and is characterized by a double
humped peak in the fall and spring
▶ It is operated for water supply, flow regulation and hydroelectric
power generation
▶ It provides the city of Everett with water supply and about 8% of its
electricity
▶ Spada Lake has a storage of 153,000 acre feet while Lake Chaplin
has about 18,000 acre feet of storage
Project Description
Project Description
 Instream flow requirements are enforced at different segments of
the Sultan River and take precedence over hydropower generation.
 Project is operated based on a July-June water year in four different
zones.
 Operations target to maintain the pool within State 3
1460
State 1
State 2
1440
1430
State 3
1420
1410
State 4
1400
State 1-2 Boundary
State 2-3 Boundary
State 3-4 Boundary
26-Jun
17-May
7-Apr
26-Feb
17-Jan
8-Dec
29-Oct
19-Sep
10-Aug
1390
1-Jul
Water Surface Elecation (ft)
1450
Construction Steps
Components of a Decision Support System
Operational Models
Simulation Model
Optimization Model
Forecast Generation and Integration
Evaluation of Values of Forecasts
Real-Time Operation Support
Operational Models
Simulation Model





Built with GoldSim simulation software
Captures system operations at the project with hydraulic
formula and conditional statements
Inputs streamflow and energy prices time-series , instream flow
requirements and starting pool elevations
Runs on a daily timestep to represent real-time operations of
reservoir releases, environmental flow requirements, routing
priorities
Used to set operational guidelines for optimization model
Simulation Model
Operational Models
Optimization Model







Built with Lingo linear programming language
Captures the hydraulic and operational elements of the system in
a mathematical framework
Input variables are forecasts of streamflow and energy prices
Decision variables used are power tunnel releases, timing of
releases
Calculates the quantity and timing of reservoir releases that
maximizes energy production
Optimizes system operations within 60 days
Uses simulation model output for monthly storage targets and
hydraulic capacities as constraints
Forecast Generation
Retrospective Streamflow Forecasts

Produced using Ensemble Streamflow Prediction (ESP) method

Generated by running a hydrological model (DHSVM) with historical
meteorological data of the Sultan River Basin

Model is run with using observed precip and temp from the first 7
days and climatology for the following days

Produces streamflow traces that have equal probability of occurring

Captures the high daily variation in actual streamflow

Produced for a period of two months and updated weekly
Forecast Generation
Retrospective Energy Price Forecasts

Generated using historic daily spot prices from the mid-Columbia
energy market

Produce weekly averages of recent (2008-09) 60-day forecasts of
daily spot prices

Calculate the average weekly error between the weekly averages
and actual spot prices

Apply average weekly error by forecast lead time to historic spot
prices for the years evaluated (2001-04)

Disaggregate synthesized weekly forecasts to a daily time-step
Integration of Forecasts

Impacts of each forecast on reservoir operations
Streamflow forecasts
Energy Price forecasts

quantity of water released
for energy production
timing of releases to capture
energy price peaks
Evaluation of the value of individual and combined forecasts
1. Forecasted Energy Prices + Actual Streamflow
2. Forecasted Streamflow + Actual Energy Prices
3. Forecasted Energy Prices + Forecasted Streamflow
4. Actual Energy Prices + Actual Streamflow Forecasts
Integration of Forecasts
Framework for operating the models
Retrospective 60 day
Forecast Input
Streamflow
Forecasts
Energy Price
Forecasts
Simulation model
Updated weekly
reservoir
storages
Activated by Change in Week
Monthly
Reservoir
storages
Update system
weekly with
observed streamflow
Optimization model
Derive weekly
operations
Evaluation of Values of Forecasts

Investigations are conducted for three years representing a range
of hydrological conditions

Simulations based on Rule Curves used as baselines for measuring
the improvement of reservoir operations


Reservoir operations are improved though refining

Quantity of releases

Timing of releases
Metrics used for quantifying the improvement in skill in operation

Cumulative revenue generated

Quantity of energy produced
Values of Forecasts
Revenue Generated from Integration of Forecasts
$20,000,000
$18,000,000
$16,000,000
$14,000,000
$12,000,000
$10,000,000
$8,000,000
$6,000,000
$4,000,000
$2,000,000
$0
2001–2002
2002–2003
Simulation
2003–2004
Mean
Forecast Energy + Forecast Streamflow
Forecast energy + Perfect streamflow
Forecast streamflow + Perfect energy
Perfect Energy + Perfect Streamflow
Actual avoided cost
Evaluation of Values of Forecasts

Assess the value of forecasts in periods of pre-specified
operational procedures

Review the range of optimal operation policies based on an
ensemble streamflow traces

Calculate probabilities of elevation targets in drawdown/ refill
periods
Ensemble Forecasts at Summer Drawdown
Ensemble Forecasts at Spring Refill
Real-Time Operation Support
Monday Morning-Decision Making
1. Obtain the streamflows forecasts for next 60 days from a data
center (RFC/other government agencies)
2. Select ensemble streamflow forecasts to be used
3. Run a combination of simulation and optimization model
4. Record the ensemble optimal releases for individual forecasts
5. Evaluate the mean, median and distribution of releases
6. Consider other factors such as state of the snowpack and
energy market and other operational factors to make an
informed decision




There are significant economic benefits to be gained from
effectively incorporating forecasts.
Reservoir operations can be improved with use of forecast
information.
Simulation model provides insight into the probabilistic range of
historical reservoir storages based on current rule curves.
DSS supplements the overall management process of making
operating decisions for a multipurpose project.
Questions