こ う えい フ ォ ー ラ ム第 23 号 / 2015.3

Optimization of Multi-reservoir Operation by Stochastic Dynamic
Programming for Moragolla Hydropower Project in Sri Lanka
Sohei UEMATSU*

Development of a new hydropower project in an existing river management system sometimes
impairs the original purpose of the system since it was not considered in the original design of theriver system. It is ideal and imperative that the operation performance of existing facilities withplanned new facilities are optimized together. However, this requires highly advanced computer
computational and modeling tools.
In this article, the optimization study for the Moragolla hydropower project in Sri Lanka isintroduced in detail. The project is a newly planned hydropower project in the Mahaweli river
system where the project was not originally designed during the first planning of the rivermanagement. Therefore the optimization is done with the existing hydropower facility in the study.
The method used for the optimization is multi-dimensional stochastic dynamic programming andoutline of the multi-dimensional stochastic dynamic programming is introduced in the article.

Keywords : multidimensional optimization, reservoir operation, optimization, hydropower,
stochastic dynamic programming

1．INTRODUCTION

optimization algorithm used for the problem is stochastic
dynamic programming (SDP). The program algorithm ofDevelopment of new hydropower project into an existing

SDP is expanded for n-dimension problems and appliedriver management system sometimes impairs the original

for a feasibility study of the planned hydropower project,purpose of the existing system, especially when the newnamely Moragolla Hydropower Project. The feasibility

project was not considered in the original design of thestudy was conducted by a joint venture of Nippon Koei

river system . Prior to development of the hydropowerCo., Ltd.(NK) and Nippon Koei India (NK India) PVT.

project, (i.e. in the planning stage) improvement ofLtd. under the contract with Ceylon Electricity Board ofoperational efficiency of existing river management

Sri Lanka5). NK and NK India reviewed and updated thefacilities should be optimized with planned facilities topast feasibility study, and the reservoir optimization was

maximize the beneﬁt of the system with the new facility.

conducted in the feasibility study.

3)

However, the countless numbers of river management
facilities developed along the river makes it practically
impossible to optimize the operation performance of

2．OUTLINE OF MORAGOLLA HYDROPOWER PROJECT

the whole river management facilities. The difficulties

The Moragolla hydropower project (the “Project” ) is amainly come from the fact that the number of the decision

planned run-of-river hydropower aiming supply electricity

variables for optimization solver exceed the power andspeed of the modern desktop computer. Studies of water
resource management have attempted to cope with such
multidimensional problem. The examples of the attempt,
by limiting the search ﬁelds, use approximation method,or heuristic or metaheuristic method, etc.
In this article, attempt of optimization of severalhydropower operation is explained as an example oflimiting the number of multidimension problems. The＊

Water Resources & Energy Department, Water Resources
Division, International Consulting Operations

Fig.1 Location of Moragolla Hydropower Project5)
79

Optimization of Multi-reservoir Operation by Stochastic Dynamic Programming for Moragolla Hydropower Project in Sri Lanka
for peaking load. The Project is located on the Mahaweli riverfeatures of the Kotmale hydropower project are shown inclose to Ulapane village in Kandy district of Central Province,

Table 2.

which is about 130 km to the northeast of Colombo. The
location of the Project is shown in the Fig.1.

The Kotmale hydropower station takes the water in at an
intake near Kotmale dam, and conveys it to the powerhouse;

The Project develops power output of 31.1 MW withthe water is released downstream of the powerhouse of thea rated head of 69 m. A concrete gravity type dam is

Project’s power station. The spilled water from the Kotmale

designed with 37 m height and 236 m crest length. The

dam ﬂows to the Kotmale river and subsequently ﬂows into

dam will create reservoir having capacity for daily peak

the Moragolla reservoir. The river system of the Moragolla

power generation. Annual energy output is estimated athydropower is shown in Fig.2. The Kotmale dam is also obliged

81.65 GWh of which the ﬁrm energy is 66.12 GWh.

to release irrigation water which is regarded as the first

In the upstream reach of the Project, there is an existinghydropower station, named Kotmale. Kotmale is reservoir

priority. Therefore, the water is ﬁrst allocated to irrigation
then hydropower follows.

type hydropower project having a storage capacity forseasonal regulation of the Kotmale river stream ﬂow. The
Kotmale hydropower project is located along the Kotmale
river, and the river joins to the Mahaweli river at the
upstream of the planned Moragolla dam. The project

3．OPTIMIZATION OF RESERVOIR OPERATION（1） PURPOSE OF THE OPTIMIZATION STUDY
The beneﬁt of the project is assessed by the power supply
capability of the new hydropower plant, and it is unknown
whether the capability of the Moragolla hydropower plantis improved when the new plant and the existing Kotmalehydropower plant are considered as one system.

Table 1 Project Features of Moragolla Hydropower Project5)
Item
Unit
Data
249
Catchment area
km2
22.4
Mean annual inﬂow
m3/s
Type of hydropower
Run of River
Concrete
Dam type
gravity
Dam heightm
37
Full supply level
EL.m
548.0
Minimum water level
EL.m
542.0
Available depth
m
6.0
1.98
Reservoir volume (effective) million m3
Reservoir surface areaha
38.47
Crest lengthm
236
Length of water waym
2,727
Planned installed capacity
MW
31.1
Number of unit
2
Type of turbine
FrancisRated headm
69.0
Annual Energy
GWh
97.6
80

C.A.
565
km2

Kotmale Dam(Existing)

C.A.
249
km2

Spilled water

Mahaweli River

Photo 1 Dam and Reservoir of Kotmale Hydropower

Kotmale River

Photo taken by author

Table 2 Project Feature of Kotmale Hydropower Project 2)
Item
Unit
Data
565
Catchment area
km 2Type of hydropower
Reservoir
Concrete facing
Dam typerock fill
Dam heightm
87
Full supply level
EL.m
703.0
Minimum water level
EL.m
665.0
172.6
Reservoir volume
million m 3
Annual Energy
GWh494
Installed Capacity
MW
201
Commission year
year
1984

Moragolla Hydropower (Planned)
Kotmale Power Station(Existing)Polagolla Head Works(Existing)

Amban
River
SystemMahaweli River System
Victoria Dam,
Randenigala Dam,
Rantambe Dam

Prepared by author
Prepared by author

Fig.2 Flow Network of the Moragolla Projectこ う えい フ ォ ー ラ ム第 23 号 / 2015.3

As spilled water from the Kotmale dam will flow into
the Moragolla reservoir, the operation of the Kotmale dam
will inﬂuence the performance of the Project. Therefore,
the study aims to find this influence of Kotmale dam
operation to the Moragolla hydropower project anddetermine whether the operation of Kotmale hydropower
station is signiﬁcant to the Project.
（2） DEFINITION OF TERMS
The definitions of technical terms used for reservoir
operation optimization are as follows.
－ Peak Period
The peak period is from 6:30 PM to 9:30 PM from Monday
to Friday. The peak duration is three hours.
－ Peak PowerThe power generated during the peak period is consideredas peak power. Power generated other than peak period is
considered as “Off-Peak Power” .
－ Annual Energy
Annual energy is the average of annual energy producedby the power plant.
－ Dependable Capacity
Dependable capacity is the amount of the capacity thatthe power plant can reliably contribute towards meetingsystem peak power demand7). In this study, the dependable
capacity is deﬁned as the power output (MW) that the plant

Ref: Feasibility Study Report of Moragahakanda development project4)

Fig.3 Existing Hydropower Plants along the Mahaweli Rivercan produce with 90% probability of exceedance (POE)
reservoir optimization)] To find optimum reservoir

through the simulation duration (43 years).

operation rule of the Project without considerationof Kotmale hydropower operation. The Kotmale（3） RESERVOIRS CONSIDERED FOR OPTIMIZATION

hydropower operation is also optimized independently.

Fig.3 shows the water resources management facilities
along the Mahaweli River. As shown in the ﬁgure, the waterii)[ “Combined operation” (which is solved by multireservoir

resources of the Mahaweli River have been intensively

optimization)] To ﬁnd optimum reservoir operation rule

developed for hydropower and irrigation.

of the Project in combination with Kotmale hydropower

The operation of the Project will directly or indirectly affectto the operation of downstream water resources management
facilities. However, it is practically impossible to optimize theoperation.
In the study, the results of single reservoir optimizations
and multireservoir optimization are compared.

operation rule of all facilities due to complexity of modeling,
and planning of new hydropower plant generally only（5） OPTIMIZATION TOOL

studies the operation of the subject plant and downstream

The optimization tool used for the study is theoperation are considered as constraints. In this study, thegeneralized software developed by Nippon Koei Co., Ltd.

operation of Kotmale and planned Moragolla hydropower(NK). The tool was developed incorporating extensive

plant directly affected, therefore, only these two reservoirs

experiences in the study of NK’s reservoir operation

are considered.

optimization 6). In the Project, the program code wascustomized for enhancement of multidimension SDP.

（4） OPTIMIZATION SCENARIOSThe program interface of the program is shown in Fig.4.

The optimization scenarios are as follows:

The software can optimize the operation of more than 10i)reservoir operations.

[ “Independent operation” (which is solved by single

81

Optimization of Multi-reservoir Operation by Stochastic Dynamic Programming for Moragolla Hydropower Project in Sri Lanka（6） OPTIMIZATION FUNCTIONS OF THE TOOL
The optimization tool developed by NK has functionsof dynamic programming(DP), stochastic dynamic
programming(SDP) and multidimension SDP. The outlineof each algorithm is as follows.
1） Dynamic Programming (DP)DP was proposed by R.Bellman [1954] as a method toseek the optimum decision process in a given timeframe
or in given multistage decision process1). Hence, DP has
been applied to problems of multistage decision processes
which include the hydropower reservoir operation8).

Prepared by author

Fig.4 Multireservoir Optimization DP Software
Developed by Nippon Koei

DP computes a sequence of optimal return functions

F T (x), F T －1(x), ..... , F 0 (x) using the following the recursive
equation.

Time Step (day)（1）

Equation “(1)” forms a recursive function and is called
Bellman equation. The function “f t(x t ,xt+1,ut ) ” is an objectivefunction to be optimized, such as maximizing electricity
production or maximizing income by selling electricity. DP
seeks the optimum path of decision process by finding thepath which produces maximum aggregated value of F t(x) .
The concept of optimum path ﬁnding by DP is shown in Fig.5.

………….……….

1

V1

t

………………………

+ F t+1(f t+1(x t+1 ,x t+2 ,u t+1 ))}Reservoir Discretized Volume

F t(x) = max {ft(xt,x t+1,u t )Vn: Optimum path2） Stochastic Dynamic Programming (SDP)
If the future inflow is perfectly predictable, DP solvesDamthe problem by considering the inflow as deterministic

Reservoir
Volume V1
V2

variable. This type of DP is called Deterministic DP.
Deterministic DP directly uses historical series data

V3:such as inflow into the reservoir. However, as it is notVn.

possible to perfectly predict the future inﬂow for long termplanning horizon, the estimation of power production bydeterministic DP sometimes overestimates the actual
power generation.
SDP assumes the future inﬂow as an unknown variable,

Preparedby author
Preparedby author

Fig.5 Concept of Finding Optimum Path of the Reservoir
by Dynamic Programmingsteady from present to future. The outline of SDP and
its application to water resources is well described by
J.Labadie3). The concept of the inﬂow with probability is
as shown in Fig.6.

Probability

but assumes that the probability distribution of inﬂow is

P (2)

P (3)

P (4) ….

.

P (1)The inﬂow with probability is treated as one of inputs to DP,
and as the inﬂow is considered with probabilities, the Bellman
equation changes its form to recursive function of expectedvalue of objective function as shown in the equation b.

F t(x) = max{E(f t(x t,x t+1,u t,p))+F t+1(f t+1(x t+1,x t+2,u t+1))}(2)“E( ) ” in the equation “(2)” is an operator of expectationof the objective function of f t(x t,x t+1,u t,p) . SDP also gives82

Qin(1)Qin(3)….
Qin(4)Qin(2)

Inflow with Probability in day “t”
Prepared by author

Prepared by author

Fig.6 Concept of Flow with Probability

Qinこ う えい フ ォ ー ラ ム第 23 号 / 2015.3

the reservoir operation table that is used for the power

target reservoir volume of reservoir I and II, respectively.

generation simulation.

The concept of the output of 2-dimension DP is shown in3） SDP for Multireservoir Case

Fig.9.

The optimization of the combined operation of Kotmale5） Simulation after Optimization

reservoir and Moragolla reservoir is considered as DP for

The program gives the reservoir operation rule as themultireservoir system. This type of problem is a state-

output of multidimension SDP. In this case, the programof-the-art problem as dealing multireservoir system ingives reservoir operation rule given by two-dimensional

DP may still cause the explosion of computer time. The

SDP. In order to calculate the electrical energy and power

concept of the DP model for multireservoir system is

output, the tool carries out simulation of power generation.

shown in Fig.7.

The result of the simulation is summarized to enumerate

4） Reservoir Operation Rule Table

the annual electrical energy, and dependable capacity. The

The uniqueness of the multidimensional dynamic

ﬂow of the calculation is shown in Fig.10.

programming is the output. Dynamic programming for
one reservoir operation optimization gives target reservoir
volume for discretized reservoir volume for the next time
step. Fig.8 shows the concept of operation rule table given
by DP.
For the multidimension DP, the dimension of the table
is increased with the number of decision variables, i.e. thenumber of reservoirs to be considered. For example, incase of optimization for two reservoirs, the output of DP

t= 1
t= 2
t= 3
.
Time step

t= i
.

.

Reservoir I
Discrete Reservoir Vol.

V II, 1

t = 365

Reservoir II
Discrete Reservoir Vol.
VII, 3
・
V II, i・VII, NV I, 1 (, ) (, ) (, )㻙(, )V I, 2 (, ) (, ) (, )㻙(, )V I, 3 (, ) (, ) (, )㻙(, )

・V I, i|||・V I, N (, ) (, ) (, )(Vol I , Vol II)

is 3-dimension of t, VolI, and VolI, where VolI and VolII is

VII, 2㻙(, )Vol I = target Reservoir I Vol. in next timestep

Vol II = target Reservoir II Vol. in next time step Reservori volume does not change Increase reservoir volume

T time steps

 Decrease reservoir volume
F1(T)M discrete levels

by author
Prepared Preparedby author

Fig.9 Concept of Output for 2-Dimension DP

N reservoirs
Optimization Stage

㻌㻌

START

F2(T)

Fi(T): return function

Preparation of Data

Objective function:
max
FN(T)

.



ൌͳ

 ሺሻ

Optimum Path

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Preparedby author

Fig.7 Concept of DP Model for Multireservoir System(case: N=3)
Discrete
Time Step (Daily)
Reservoir
Volume1 2 3 ・ ・ t
・ ・ 364 365
㻌㻌
V1
  
 
㻙
  
 
V2
㻙
  
 
V3
㻙
・
| |
| |
Vi
・
  
 
VN
㻙
 Reservori volume does not change Increase reservoir volume
 Decrease reservoir volume
Preparedbyby
authorPreparedauthor

Fig.8 Concept of Output for 1-Dimension DP

Input- Stochastic Inflow- Evaporation- Resrvoir H-V- Turbine&Generator Spec.
- Electricity tariff (peak, off peak)

Solve by
Stochastic DP(t = 1 to 365 days)

Obtaining Optimum
Reservoir Water Level

OutputOptimum reservoir water level
table (Daily basis from 1 to 365
days)

.

END

Simulation Stage
START

Preparation of Data

Power Simulation

Input- Daily Inflow (1968 - 2010)- Reservoir operation rule
- Evaporation- Electricity Tariff (peak, off peak)

Output (Daily 1968 to 2010)- Energy and power- Revenue

Calculation of Annual
Energy and Dependable
Power

END

Prepared by
Preparedby authorauthor

Fig.10 Study Flow of Optimization and Simulation
83

Optimization of Multi-reservoir Operation by Stochastic Dynamic Programming for Moragolla Hydropower Project in Sri Lanka（7） SELECTION OF THE OBJECTIVE RETURN FUNCTION

generally is measured by the dependable capacity ofthe plant. The dependable capacity is used for project

Discharge (cms)

The Project is designed for the power supply during
peaking electricity load, and the benefit of such project

Stochastic Inflow

60
50

20%
35%

40

50%

65%

30

80%

20
10

economic evaluation. Therefore, the objective return0function is selected to maximize the dependable capacity

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Days in a year

(Moragolla Reservoir)of the plant. The equation of objective function forms asshown below.
－ Objective function

Pmax

P max －P primary(t )P max －P min*(3):Maximum power output of Kanan hydropower plant(MW)

Pprimary (t ):Primary power output of day “t ” (MW)

Pmin

Discharge (cms)ft=

Stochastic Inflow

60

2

50

20%
35%

40

50%

30

65%
80%

20
10
0

Jan

Feb

Mar

:Minimum power output of Kanan hydropower plant (MW)capacity.

The constraints of the optimization are: reservoir capacity,maximum/minimum plant discharge, and spillway capacity.

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Days in a year

(Kotmale Reservoir)* The function forms as quadratic equation as this form ofequation empirically fits to maximization of dependable

Apr

Fig.11 Stochastic Inflow to Moragolla Reservoir andKotmale Reservoir 5)discharge is estimated from 1968 to 2010 for the use of
power generation simulation.

The irrigation supply is also considered as constraints forrelease from the Kotmale dam, since the irrigation supply is
the ﬁrst priority of water supply of the Kotmale dam.
（8）	DAILY STOCHASTIC DISCHARGE FOR STOCHASTIC
OPTIMIZATIONThe stochastic data set for the Project and the Kotmale

4．RESULT OF MORAGOLLA AND KOTMALE RESERVOIROPERATION OPTIMIZATION (COMBINED OPERATION)
The reservoir operation of the two reservoirs is optimized
by SDP, and the power generation simulation is performed
using the estimated stream flow data from 1968 to 2010.

hydropower station was prepared for the calendar days

The result of the simulation is discussed in this section.

from January 1st to December 31st. The discharge with

1） Water Level

probability prepared for the optimization study is shown

The daily average water level of the simulation for

in Fig.11.

Moragolla and Kotmale reservoir is shown in Fig.12.

（9） DAILY DISCHARGE FOR POWER GENERATION SIMULATION

level of combined operation, whereas the brown lines are－ Moragolla Reservoir

those of the independent reservoir operation optimization

The daily discharge at the Moragolla dam site was estimatedresult. As shown in the graphs, the water level of thefor the power simulation on the basis of available rainfall

Moragolla reservoir of combined operation is slightly

data in the hydrological study of the revised feasibility study

lower (around 0.1 m) than that of independent reservoirof the Project . The Moragolla river discharge is estimatedoperation case.

The black lines in the graph are the reservoir water

5)

from 1968 to 2010. The estimated daily discharge is used for

For Kotmale reservoir, the reservoir water level ofpower generation simulation.

combined operation is greater for 6 m at maximum. This

－ Kotmale Reservoir

indicates that the combined operation of Moragolla and

The daily discharge at the Kotmale dam site was estimatedKotmale reservoirs may make the Kotmale reservoir waterusing the stream flow data of the Kotmale dam and at

level higher than the independent reservoir operation.

Morape which was located at the Kotmale reservoir before

It is noted that the water level of the Kotmale reservoir

the construction of Kotmale dam. The Kotmale riveris much lower than the Full Supply Level (FSL) of 703

84こ う えい フ ォ ー ラ ム第 23 号 / 2015.3
549.0
548.5

547.5
547.0

546.5
Combined Operation

546.0

40
35
30
Almost the same energy

25
20
15
10
5

Independent Operation

0

545.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month(Moragolla Hydropower)

545.0
Jan

Feb

Mar

Apr

May

Jun

Jul
Aug
Month

Sep

Oct

Nov

Dec

(Moragolla Reservoir)
Monthly Energy (GWh)

45

680.0

Independent Operation

Reservoir WL (m asl)

Combined Operation

50

685.0

Combined Operation

675.0

670.0

MOL (665 m asl)

665.0

Independent Operation

45

Monthly Energy (GWh)

FSL (548 m asl)

548.0

Reservoir WL (m asl)

Combined Operation

50

Independent Operation

Increase in
dry period

40
35
30

Decrease in endof wet period

25
20
15
10
5
0

660.0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month(Kotmale Hydropower)

655.0
Jan

Feb

Mar

Apr

May

Jun

Jul
Aug
Month

Sep

Oct

Nov

Dec

(Kotmale Reservoir)

Fig.13 Monthly Energy for Combined Operation andRef: 5) Independent Operation 5)

Ref:5) Daily Average Reservoir Water Level of Moragolla
Fig.12and Kotmale Reservoir 5)m ASL. This is caused by the release for irrigation
requirement at Kotmale dam which inhibits the Kotmale
reservoir to keep the water level close to FSL.
2） Electrical Energy (unit: GWh)
The monthly electrical energy supplied by Kotmale andMoragolla hydropower plants for combined and independent

Average Peak Power (MW)180

operation are shown in Fig.13.

140

120
100
80
40
20
0

180

December. This increment of energy in dry season isaround 10%. As increase in electrical energy production
in dry season leads to increase in dependable capacity,
it implies that the optimization model is able to make
Kotmale hydropower station to generate more electricityof the combined operation than that of independent
operation in dry season.

Average Peak Power (MW)between the two modes of operation in Kotmale reservoir

energy decreases during the period from October to

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month(Moragolla Reservoir)

plant are almost the same, while there are some differences

period from January to May increases while the monthly

Almost the same peak power

60

operation and combined operation of Moragolla hydropower

It is noted that the monthly energy during the dry

Independent Operation

160

As shown in the ﬁgure, the differences between independent

operation.

Combined Operation

Combined Operation

Independent Operation

160

140
120

100
80

60
40

20
0

Increase in
dry period

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month(Kotmale Hydropower)

Fig.14 Monthly Average Peak Power for Combined
Operation and Independent Operation 5)
85

Optimization of Multi-reservoir Operation by Stochastic Dynamic Programming for Moragolla Hydropower Project in Sri Lanka
3） Power Output (unit : MW)power is increased just 1.3%. This means that even if

The monthly average of power output during peak load

the Moragolla hydropower plant operation is closely tiedis shown in Fig.14. The tendency of the power output ofwith Kotmale hydropower plant, improvement of theMoragolla hydropower and Kotmale hydropower plants

economical benefit of the Project is not significant; inare similar to the monthly energy discussed in item .

addition, optimization and developing operation rule of

The differences between the two modes are negligible for

combined operation is complicated.

2)

Moragolla hydropower plant operation, and around 10%

Therefore, in the feasibility study of the Project, theincrease during dry period for Kotmale hydropower. This

combined operation of Kotmale and Moragolla hydropower

indicates that the combined operation helps to increase

plants are not considered to determine the dependable

the dependable power of Kotmale hydropower.

capacity of the Project, and the dependable capacity of

The duration curves of the peak power of Kotmale

independent operation is adopted for economic evaluation.

and Moragolla hydropower plants for combined andindependent operation are shown in Fig.15.

The increment of power of combined operation is 3%. This
increase is mainly due to increase of peak power during
the dry period, which will result in increase in dependable
capacity. The allocation of peak power output of Kotmale

Daily Peak Power (MW)operation is increased from that of independent operation.

Combined Operation

250

As shown in the figure, the peak power of combined

200

150

KotmaleMoragolla

100

50

and Moragolla hydropower plants are shown in the0

duration curves in Fig.16.

1

31

61

91

121 151 181 211 241 271 301 331 361

Days in a year

As shown in the figure, Moragolla hydropower plant
contributes as base power in peak power supply for bothscenarios, and the peak power supply by Kotmale is high（2） SUMMARY AND CONCLUSION FOR COMBINED OPERATIONThe results of the combined operation and independent
operation are summarized as shown in Table 3.
The table shows that the combined operation of Kotmale
and Moragolla hydropower plants will improve the output
of annual power, dependable power and average power.

Daily Peak Power (MW)variation from 190 MW to 12 MW for both cases.

Independent Operation

250
200
150

KotmaleMoragolla

100
50

0
1

31

61

91 121 151 181 211 241 271 301 331 361

Days in a year

But effect of improvement is limited since the dependable

Fig.16 Duration Curve of Peak Power Output of Kotmale
and Moragolla Hydropower Plants for Combined
Operation5)250.0

Increase in PeakPower Supply

Daily Peak Power (MW)200.0

Table 3 Summary of Result for Reservoir Operation
Optimization for Combined Operation of
Kotmale and Moragolla Hydropower Plants5)

150.0
Combined Operation

100.0

Independent Operation

Items

50.0

0.0

0

50

100

150

200

250

300

Days in a Year

Fig.15 Duration Curve of Peak Power Output of
Average Year of Kotmale and Moragolla
Hydropower Plant for Combined Operation
and Independent Operation5)
86

350 365

AnnualEnergy(GWh)Dependable
Capacity(MW)

AveragePower(MW)

Independent
Operation

Total

456.9

29.9

156.7

Combined Operation

Total

459.9

30.3

161.7

Value

3

0.4

5

%

0.70%

1.30%

3.20%

Increment

こ う えい フ ォ ー ラ ム第 23 号 / 2015.3

5．CONCLUSION
According to the result of optimum reservoir operation

References1）of the planned Moragolla hydropower and the existing
Kotmale hydropower project, the production of electrical

Bellman, R., “The Theory of Dynamic Programming” the Rand
Corporation, July 1954.

2）

Electric Power Development Co., Ltd., Nippon Koei Co., Ltd.

energy is certainly increased when the operation of two“Study of Hydropower Optimization in Sri Lanka, Final Report”

hydropower stations optimized together.

Japan International Cooperation Agency, Ceylon Electricity

However the result of the study also shows thatsuch improvement of the optimization of operational

Board, February 2004.
3）

performance of the two hydropower stations is limited.

Journal of water resources planning and management, State-of-

One of the possible reasons of the small improvement of
the performance is that the Moragolla hydropower is run-

Labadie, J., “Optimal operation of multireservoir systems,
the-Art Review” , March/April 2004, pp.93-111, ASCE.

4）

Lahmeyer International in association with Central Engineeringof-river type project and the reservoir capacity is limited

Consultancy Bureau in Sri Lanka, United Consulting Group in

to regulate the ﬂow regime of the river.

Kuwait, and Chuo Kaihatsu Corporation in Japan, “Feasibility StudyIn the feasibility study of the Project, further studyof Moragahakanda Development Project” Mahaweli Authority offor combined operation by multidimension DP was

Sri Lanka, Ministry of Mahaweli Development, Sri Lanka, 2001.

not conducted. But it is recommended conducting

5）

Nippon Koei in Joint Venture with Nippon Koei India PVT. LTD.,

optimization study involving relating water resources“Moragolla Hydropower Project, Review of Feasibility Study

facilities when the project is implemented.

and Preparation of Detailed Design and Bidding Documents” ,Challenges for application of the multidimension DP is

Ministry of Energy, Ceylon Electricity Board, Sri Lanka, May

to express the operation output table when the number ofdimension is more than three. The dimension of output

2013.
6）

Uematsu, S., “Reservoir Operation Study for the Nam Ngum River

7）

US Army Corps of Engineers, “Engineer Manual No.1110-2-

table is corresponding to the number of dimension. The
3-dimension DP needs 4-dimension table which may be

Basin,” Koei Forum No.19, pp.53-62, March 2011.

difﬁcult to express. One of the possible countermeasures
is to establish the artiﬁcial neural network (ANN) model

1701, engineering and Design, Hydropower.” 1985, USA.
8）

Yakowitz, S., “Dynamic Programming in Water Resources,”by determining the network coefficients by learning theWater Resources Research, Vol. 18, No.4, pp.673-696, August

DP output table data. But of course it needs some studies

1982.

to prove the effectiveness of application of ANN.

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