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Presented By

Ashish Shrestha

M.Sc. Student

Water Resources Engineering, Department of Civil Engineering, Pulchowk

Campus, IoE, Nepal

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Contents of presentation

• Introduction

• Stochastic Dynamic programming and its prospects

• Reservoir operation study of Kulekhani

• Optimization using Stochastic Dynamic programming

• Conclusion

• References

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INTRODUCTION

Source: https://www.flickr.com/photos/abhujel/5797076962

Optimal operation of a reservoir system is a complex decision making process

involving many variables and objectives (Oriveira and Loucks, 1997). Therefore,

development of suitable operation policies is essential for each reservoir system

considering its characteristics.

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Stochastic Dynamic Programing and its prospects

• Deterministic dynamic programming: The currentstate and decision variable are known and governsthe state at the next stage

• Stochastic dynamic programming: Usesprobability distribution to determine theforthcoming state.

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Stochastic Dynamic Programing and its prospects

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In reservoir operation, the optimal solution is

reached when the optimal releases associated with

each initial storage is the same as the

corresponding releases and storage in the previous

time step (Karamouz, 2003).

Recursive function of backward moving SDP algorithm

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KULEKHANI RESERVOIR

Salient features

Type: Zoned rockfill dam

Dam height: 114m

Rated net head: 550m

Design discharge: 12.1 m3/s

Installed capacity: 60MW

Catchment area: 126 km2

(approx.)

High Water Level: 1530masl

Source: Author

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KULEKHANI RESERVOIR

Source: Author

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Lost about 30% of its original capacity in 28 years of operation and is losing its capacity at

the average rate of 1% annually (Shrestha, 2012).

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Reservoir operation study of Kulekhani

• Monthly effective inflow data i.e. reservoir inflow minus theevaporation loss was ascertained

• Seasonal discharge pattern and monthly volume wasestablished using mass curve analysis with respectivedependability and seasonal operation pattern.

• Comparison study of planned inflow, seasonal operationpattern and peak operation hours in dry season werecarried out.

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Reservoir operation analysis flow chart, (JICA, 2003)

Optimal operation of a reservoir for Hydropower production using stochastic dynamic programming

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Operation Rules as studied for (I) Dry Season Wet Season

Seasonal Operation Pattern Dec. to May (6months) Jun. to Nov. (6 months)

Peak Operation 13.1 m3/s x 4 hrs 6.55 m3/s x 4 hrs

Off-peak Operation 4.8 m3/s x 20 hrs 1.21 m3/s x 20 hrs

Planned Inflow 70% dependable inflow

Seasonal Operation pattern 4-month dry season operation

Peak operation hour in dry season 8-hour

Operation Rule for Kulekhani I, (JICA, 2003)

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Reservoir operation study of Kulekhani

Optimal operation of a reservoir for Hydropower production using stochastic dynamic programming

Optimization using SDP• Reservoir storage volume and inflows discretization

• Discretized into representative classes with correspondingrepresentative or characteristic storage levels.

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Source: Author

Source: Shrestha, H. S. (2012). Sedimentation and sediment

handling in Himalayan Reservoirs, PhD Thesis, NTNU.

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• Objective function:

∆S is the deviation of reservoir storage and, ∆R is the possible release from maximum

supply level and volume corresponding to design output.

Storage Class Inflow Class Optimum Storage

Class of next period

1 1 1

1 2 1

2 1 1

2 2 1

Storage Class Inflow Class Optimum Storage

Class of next period

1 3 2

1 4 2

2 3 2

2 4 2

Operation policy at period t=1 Operation policy at period t=2

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Optimization using SDP

Optimal operation of a reservoir for Hydropower production using stochastic dynamic programming

Conclusion

• The optimized result shows that the operation of Kulekhanireservoir using stochastic dynamic programming provided a logicto store the available water whenever abundant and judiciallymaintain the storage volume during time of low flows.

• The result shows the usefulness of SDP model.

• The results can be improved by micro level discretization of thereservoir storage and inflows.

• Incorporation of sediment effects needed.

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• Bellman. (1957). Dynamic programming. Princeton University Press, Princeton.

• Budhi Gandaki Hydroelectric project. (2011). Review Report of Budhi Gandaki Hydroelectric Project. Budhi Gandaki Hydroelectric Project, Nepal.

• Ghimire and Reddy. (2013). Optimal Reservoir Operation for Hydropower Production Using Particle Swarm Optimization and Sustainability Analysis of Hydropower. ISH Journal of Hydraulic Engineering, Taylor and Francis .

• JICA. (2003). Kulekhani III HPP Final Report.

• Karamouz, M. (2003). Water Resources Sustem Analysis. Washington D.C.: Lewis Publishers.

• NEA (2011), Annual Report, Nepal Electricity Authority

• Loucks, D. P. (2005). Water Resources System Planning and Management.UNESCO Publishing.

• Oriveira and Loucks. (1997). Operating rules for multi-reservoir systems. Water Resources Research .

• Shrestha, H. S. (2012). Sedimentation and sediment handling in Himalayan Reservoirs, PhD Thesis, NTNU.

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References

Optimal operation of a reservoir for Hydropower production using stochastic dynamic programming

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Acknowledgement:

• Dr. Bhola NS Ghimire, Associate

Professor Water Resources Engineering,

Department of Civil Engineering,

Pulchowk Campus, IoE, Nepal

• Amity University

Source: Author