Reservoir optimisation using El Nio information Henrik Madsen DHI, - - PowerPoint PPT Presentation

Reservoir optimisation using El Niño information

Henrik Madsen DHI, Denmark Emiliano Gelati, Dan Rosbjerg DTU, Denmark

HydroPredict 2010, 20-23 September 2010, Prague, Czech Republic

Daule Peripa

SST anomalies [oC] El Niño event (December 1997)

Correlation between El Niño events and streamflow regime

Niño 1+2 (temperature) Trans Niño Index (temperature gradient) +

• El Niño indices

Standard indices Forecasts

Daule Peripa

Optimisation of reservoir system

Planned extension: Baba diversion Existing: Daule-Peripa Optimisation objectives:

• Maximise hydropower
• Minimise downstream

water deficits Stochastic optimisation:

• Stochastic inflow

model

• Reservoir simulation

model

• NSGA-II optimisation

algorithm

Stochastic inflow model

Markov switching autoregressive model:

• Streamflow driven by a hidden climate state process
• Transition between states follow a first order Markov process
• Transition probabilities depend on climate state
• Streamflow modelled by an ARX model conditioned on climate

state Estimation for Daule-Peripa inflow:

• Niño 1+2 and trans-Niño indeces
• Two state model

Gelati et al., WRR, 2010

Inflow model results

Simulation Forecasts (expected values)

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 200 400 600 800 1000 1200

Year Mean annual inflow [m

3 /s]

Observed Expected (simulation) 10% - 90% quantile interval (simulation)

10 10

1

10

2

10

3

10 10

1

10

2

10

3

1 month lead time Observed inflow [m

3

/s] Forecasted inflow [m

3

/s]

10 10

1

10

2

10

3

10 10

1

10

2

10

3

9 month lead time Observed inflow [m

3

/s] Forecasted inflow [m

3

/s]

Major El Niño events Dryest years

Rule curve optimisation

Release = f ( storage, month, hydropower water demand )

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 70 72 74 76 78 80 82 84 86 100% 40% 30% 25% 20% 10%

Level [m]

142.15 142.2 142.25 142.3 142.35 142.4 0.2 0.4 0.6 0.8 1

x 10

• 3

Expected root mean square hydropower deficit [MW] Water supply failure frequency Dominated policies Optimised policies Chosen policy

5 curves  12+4 decision variables Chosen policy with water supply failure frequency < 10-3

Real-time optimisation using inflow forecasts

• 1. Generate 100 series of 9-month

long inflow forecasts (given past inflow and El Niño forecasts) At each time step:

• 2. Optimise 9 monthly releases

(optimise hydropower in 9- month period and penalise future costs/benefits)

1 2 3 4 5 6 7 8 9 200 400 600 800 1000 1200

Months from the beginning of 2006 Inflow [m

3 /s]

Observed Single forecast

1 2 3 4 5 6 7 8 9 50 100 150 200

Months from the beginning of 2006 Release [m

3 /s]

• 3. Implement the first release
• 4. Go to the next time step…

Penalty function – storage target

Expected optimal hydropower production in a twelve-month period as function of the month and reservoir level (Dynamic Programming on 1950-1999 period)

Optimisation results existing system

Root mean square hydropower deficit [MW] Water supply failure frequency Average generated power [MW] Historical

147.0 70.6

Rule curves

144.7 71.4

Forecast

142.9 73.1

Dynamic programming

139.8 74.6

1.1% 3.5% 5.7%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 70 72 74 76 78 80 82 84 86

Year (beginning) Water level [m]

• Dyn. programming

Historical operation Rule curves Forecasts Level

Reservoir water level (monthly)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 100 200 300 400 500 600 700 800 900

Year (beginning) Inflow [m

3

/s]

Inflow

Optimisation results existing system

Optimisation results extended system

Increase in average production with extended scheme: 108%

Average generated power Daule Peripa [MW] Average generated power Baba [MW] Average generated power Total [MW] Historical - existing

70.6

• 70.6

Forecast optimisation - existing

73.1

• 73.1

Dynamic programming - existing

74.6

• 74.6

Forecast optimisation - extended

129.0 26.3 155.3

Dynamic programming - extended

133.3 26.0 159.3

Concluding remarks

• Stochastic simulation-optimisation approach using inflow model

with climatic indices as covariate information

• Using El Niño information has a large potential for improving the

current reservoir management

• General stochastic model
• Use with other large scale climatic information
• Apply as downscaling and impact assessment tool for climate

change studies

Thank you for your attention

Henrik Madsen hem@dhigroup.com

HydroPredict 2010, 20-23 September 2010, Prague, Czech Republic