ESET ALEMU

WEST Consultants, Inc.

Bellevue , Washington

Purpose for DSS

Project Area Description

Construction Steps

Experimental Design

Results and Implications

Presentation Outline

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 forecastso Hydrologic Forecasts (Ensemble Streamflow)o Energy Price Forecasts

Demonstrate the time of year (season) when forecasts are the most valuable

Purpose of DSS

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

1390

1400

1410

1420

1430

1440

1450

1460

1-J

ul

10

-Aug

19

-Sep

29

-Oct

8-D

ec

17

-Jan

26

-Feb

7-A

pr

17

-May

26

-Jun

Wate

r S

urf

ace E

leca

tio

n (

ft)

State 1-2 Boundary State 2-3 Boundary State 3-4 Boundary

State 1

State 2

State 3

State 4

Project Description

Operational Models

Components of a Decision Support System

Simulation Model Optimization Model

Forecast Generation and Integration

Evaluation of Values of Forecasts

Real-Time Operation Support

Construction Steps

Operational Models

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

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

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

Retrospective Streamflow Forecasts

Forecast Generation

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

Retrospective Energy Price Forecasts

Impacts of each forecast on reservoir operations

Streamflow forecasts quantity of water released for energy production

Energy Price forecasts 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

Integration of Forecasts

Simulation model Optimization model

Energy Price Forecasts

Retrospective 60 day

Forecast Input

Monthly Reservoir storages

Updated weekly

reservoir storages

Derive weekly

operations

Update system weekly with

observed streamflow

Framework for operating the models

Activated by Change in WeekStreamflow Forecasts

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

Revenue Generated from Integration of Forecasts

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

Evaluation of Values of Forecasts

Ensemble Forecasts at Summer Drawdown

Ensemble Forecasts at Spring Refill

Real-Time Operation Support

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

Monday Morning-Decision Making

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