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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

144

Reservoir Operation Under Different Climate Change Scenarios

Azhar Husain

1

1

Assistant Professor, Department of Civil Engineering, Jamia Millia Islamia (Central Universiy), New Delhi, India

Abstract— As a result of tropical monsoon climate with strongly variable flow in time and space, India has lot of reservoirs, which play a significant role in the development of the country economy. Most of the reservoir is multipurpose including flood control. Hydropower generation, water supply, navigation, restoration, etc. Design of hydrological systems has traditionally been carried out on the assumption that the available flow records for a location reflect stationary climatic conditions. In view of the ongoing climate change the assumption of stationarity of climatic data is not justified. Operational decisions for water resources infrastructure such as reservoirs are dependent on both the timings and magnitude of flows, and therefore climate change impact assessment must consider both these characteristics. In this paper, optimal storage trajectories have been determined for Bhakra reservoir in Satluj River Basin for four different inflow sequences reflecting diverse set of climatic conditions using dynamic programming. The objective of operation was to maximize revenues from power generation subject to constraints on storages and releases from the reservoir. The intent was to create an ensemble of reservoir operating rules that would correspond to diverse climatic conditions, and therefore assist reservoirs managers in decision making. The results of analysis of derived storage trajectories clearly indicate that the climate change has potentially important implications for the operation of Bhakra reservoir. Analysis presented herein is likely to lend credibility to recent climate change modelling efforts for a reservoir fed by Himalayan rivers that are anticipated to experience potentially serious impacts of climate change. A distinct practical advantage of the simulation model presented here is that it can be used to simulate the operation of reservoirs under various user defined operating policies.

Keywords— Optimal, storage, climate, change, Bhakra, reservoir, dynamic, programming

I. INTRODUCTION

Climate change represents an additional stress on ecological and socioeconomic systems in India that are already facing tremendous pressures due to rapid urbanization, industrialization and economic development.

With its huge and growing population, a 7500-km long densely populated and low-lying coastline, and an economy that is closely tied to its natural resource base, India is considerably vulnerable to the impacts of climate change. . The hydrological systems are potentially very sensitive to change in climate as the changes in precipitation affect the magnitude and timing of runoff and the frequency and intensity of floods and droughts (IPCC, 2007). Changing global climate and weather patterns are impacting snow cover in the Himalayas due to which the spatial distribution of rainfall has undergone significant irreversible change. In the event of shifting pattern of precipitation and runoff associated with climate change, the timings and magnitude of water availability at reservoir sites is likely to be impacted.

A major impact of climate change is anticipated to be on the hydrological systems including reservoir operations. One of the most important aspects that would be impacted is the availability of water resources on a regional scale with the modification of the hydrological cycle (Xu and Singh, 2004). The changes in the timings of precipitation are likely to impact runoff generation processes both in terms of magnitude as well as timings. Operational decisions for water resources infrastructure such as reservoirs are dependent on both the timings and magnitude of flows, and therefore climate change impact assessment must consider both these characteristics. The operation of reservoir systems is, therefore, likely to be more reliable if the impacts of potential climate change on inflow sequences are considered.

II. LITERATURE REVIEW

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

145 Klemes (1985) performed an assessment of the anticipated sensitivity of water resource systems to climatic variations, and found that (a) decrease in reliability might occur much faster than any decrease in precipitation or increase in evaporation losses; (b) the impact of drier climate would be more severe where the present level of development is high than where it is low; and (c) the relative effect of the precipitation change would probably be greater than that of the evapotranspiration change. Burn and Simonovic (1996) investigated the potential impacts of changing climatic conditions on the operational performance of water resource systems. Reservoir operation was carried out for two potential monthly flow sequences reflecting two different sets of climatic conditions. Reliability (the probability of success) and resilience (a measure of how quickly the reservoir will recover from a failure) criteria were used to show that, despite moderate changes in inflow characteristics, the values of the performance criteria are substantially impacted. It was concluded that the reservoir performance was sensitive to the inflow data.

Minville et al. (2010) evaluated the impacts of climate change on medium-term reservoir operations for the Peribonka water resource system. The results of simulations clearly indicated the tendency for reduction in mean annual hydropower production and an increase in spills, despite an increase in the annual average inflow to the reservoirs. Marteen et al. (2011) studied sustainability of small reservoirs and large scale water availability under current and future climatic conditions. It was concluded that climate change impacts on water availability may be severe, and impacts on distributed water availability from small reservoirs may exceed impacts on centralized water availability from large reservoirs. Whitfield and Cannon (2000) analyzed recent (1976-1995) climatic and hydrological variations in Canada and found that even small changes in precipitation and temperature considerably affect river discharges. Christensen et al. (2004) claimed that statistically insignificant changes in the inflows would have large impacts on reservoir storage. Consequently, reservoir operation procedures are likely to be impacted.

III. OBJECTIVES

This paper describes the application of dynamic programming (DP) technique to the determination of reservoir operating rules for Bhakra reservoir in Satluj River Basin in India under several plausible climate scenarios.

The pressure on the available water resources in the basin has been increasing at a rapid rate as a result of large scale industrial and commercial development. This has led to an increased emphasis on the development of optimal reservoir operating strategies for the Satluj basin. The intent behind applying DP to the operation of Bhakra Reservoir was to investigate the practicality of the approach in the optimisation of control curves for the reservoir system. The reservoir storage trajectories have been determined for four different climate scenarios corresponding to a dry year, a wet year, a hot year, and a cold year. The objective of operation was to maximize power generation subject to constraints on storages and releases from the reservoir.

IV. STUDY AREA

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

[image:3.612.52.288.129.299.2]

146

Fig. 1 Schematic of Satluj River basin

Being a mountainous basin, the gauging stations are sparsely located. Due to rugged and uneven terrain the collection of data in the basin is difficult. Streamflow data is available at Bhakra reservoir for the period 1966–2000. Precipitation and temperature data is available at several raingauge stations in the basin. The characteristics of Bhakra dam reservoir are provided in Table 1.

TABLE 1

CHARACTERISTICS OF BHAKRADAM RESERVOIR

Catchment Area 56980 km2

Normal Reservoir Level 512.0 m

Dead Storage Level 445.62 m

Live Storage Capacity 6911 Mm3

Gross Storage Capacity 9340 Mm3

Dead Storage Capacity 2430 Mm3

Area of Reservoir 162.48 Km2

V. STUDYAREACLIMATECHANGESCENARIO

The first step in the derivation of optimal storage and elevation trajectories for the Bhakra reservoir is to generate plausible climate scenarios reflecting conditions corresponding to the coldest, warmest, driest, and wettest years. The methodology adopted for the generation of climate scenarios can be regarded as an analogue approach to scenario generation that has some similarities with the approach used by Nkemdirim and Purves (1994), and Burn and Simonovic (1996).

The four inflow sequences considered are representative of different climatic conditions. The two climate change indicator variables (precipitation and temperature) are used to identify coldest, warmest, driest, and wettest years in the historical record. The methodology involves ordering the years on the basis of average annual maximum temperature and then determining the warmest and the coldest year in the historical record. On the similar lines, the years were sorted on the basis of total annual precipitation yielding the wettest and the driest years.

[image:3.612.52.286.418.591.2]

The first step in the derivation of optimal storage and elevation trajectories for the Bhakra reservoir is to generate plausible climate scenarios reflecting conditions corresponding to the coldest, warmest, driest, and wettest years. The methodology adopted for the generation of climate scenarios can be regarded as an analogue approach to scenario generation that has some similarities with the approach used by Nkemdirim and Purves (1994), and Burn and Simonovic (1996). The four inflow sequences considered are representative of different climatic conditions. The two climate change indicator variables (precipitation and temperature) are used to identify coldest, warmest, driest, and wettest years in the historical record. The methodology involves ordering the years on the basis of average annual maximum temperature and then determining the warmest and the coldest year in the historical record. On the similar lines, the years were sorted on the basis of total annual precipitation yielding the wettest and the driest years.

Fig. 2 Inflow series for the driest, wettest, hottest, and coldest years in the historical record

VI. DPSOLUTION PROCEDURE

[image:3.612.324.564.473.621.2]
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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

147 It is capable of treating non-convex, non-linear and discontinuous objective and constraint functions, and this is the greatest advantage of DP. Constraints on both decision and state variables introduce no difficulties. In fact, the constraints speed up the computational procedure. There is, however, no general formulation that can be used to solve all problems using DP. Some intuition and judgement is required on the part of the user to formulate the problem in a manner that makes it amenable to solution by DP. To solve a reservoir operation problem, it is required to divide the problem into states and stages. The states correspond to storage in the reservoir and stages correspond to the time steps. The goal of the DP is to determine the optimal states of the system at each stage of the operating horizon using the following recursive equation.

)]

s

(

F

)

d

,

s

(

V

[

max

)

s

(

F

n n

n n n

n1 n1 (1)

Where sn is the state variable, dn is the decision variable, Vn (sn, dn) is the objective function value,

)

s

(

F

n n is the cumulative return at stage n with

F

0

(

s

0

)

known, and

s

n1

g

(

s

n

,

d

n

)

is the stage to stage

transformation function.

The dynamics of a multi reservoir system can be described by the following equation

)

t

(

E

)

t

(

MR

)

t

(

I

)

t

(

S

)

1

t

(

S

i

i

i

i

i (2)

Where Si (t) = vector of reservoir storages at time t in reservoirs i=1, n; Ii(t) = vector of reservoir inflows in time period t to reservoirs i=1, n; Ri(t) = vector of reservoir releases in time period t from reservoirs i = 1, n; Ei(t) is the vector of reservoir evaporation in time period t from reservoirs i= 1, n; and M =

n

n

matrix of indices of reservoir connections. The matrix M depends upon the configuration of the system, and consists of -1, 0, and 1.

The transformation from stage to stage is governed by equation (1). In addition to this, the releases and storages are limited by physical considerations. A minimum turbine release is required to avoid cavitation, and there are restrictions on the maximum allowable release in any time period of the optimization horizon. The storage in the reservoir should not fall below a specified minimum level nor should it exceed maximum allowable storage in any time step. The system is, therefore, subject to constraints expressed as follows:

max , i i min ,

i

S

(

t

)

S

S

(3)

max , i i min ,

i

R

(

t

)

R

R

(4)

Where Si,min and Si,max are the allowable minimum and maximum storages in reservoir i = 1 ,n, and Ri,min and Ri,max are the lower and upper bounds on the releases from reservoir i = 1, n.

The optimization model may be formulated as

Maximize

T 1 t t

P

Z

(5)

Subject to

S

min

S

t

S

max (6)

R

min

R

t

R

max (7)

Where Smin and Smax are the minimum and maximum storage respectively, and Rmin and Rmax are the maximum and minimum release respectively from the reservoir, and T is the number of stages. The hydropower produced during time step t is computed using

t t

t

9

.

81

R

H

P

(8)

Where Rt is the release made during time step t, Ht is the average reservoir level during time step t, and

is the efficiency of turbines.

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

148 The objective of operation considered in this research is the maximization of power produced from the reservoir under each plausible climate scenario. This objective is highly significant in view of the power shortages that the northern region of India faces especially during the summer season. For the calculation of power produced, the effective head is required. The reservoir level is continually changing as both inflows occur and releases are made. . A minimum level of 620.0 m has been assumed for power production. If the water level falls below this level, no power will be produced. Since the reservoir state is discretized and only one power calculation is made for each 10 day operating period, the power function use the average reservoir level for each stage. The average of the state before and after a release is made is used to determine the average reservoir level in a particular time step. Finally, the power produced can be computed using (8).

VII. RESULTS AND DISCUSSION

[image:5.612.328.562.121.258.2]

The optimal reservoir elevations obtained using the DP model for the driest year in the historical record is shown in Figure 3. The optimal elevation trajectories for the wettest, hottest and coldest years are shown in Figure 4, Figure 5, and Figure 6 respectively. These optimal elevation trajectories are similar in pattern to the optimal storage trajectories, and may be considered as rule curves for optimal operation of reservoirs under a given climate scenario.

Fig. 3 Optimal storage trajectory for the driest year in the historical record

[image:5.612.326.562.295.440.2]

Fig. 4 Optimal storage trajectory for the wettest year in the historical record

[image:5.612.325.562.406.573.2]

Fig. 5 Optimal storage trajectory for the hottest year in the historical record

[image:5.612.52.283.465.587.2]
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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

149 It can be seen that the model attempts to fill up the reservoir near the beginning of the operating horizon. This is due to the fact that the power produced is a function of water head, and since the model attempts to maximize the power produced it tends to keep the storage level as high as possible. The model tends to lower the reservoir level during the drier periods. It can be observed from the trajectories presented in Figure 3 that during the first half of the operating horizon, the reservoir keeps filling up and the levels become considerably high. These trajectories suggest that the reservoir should be maintained at higher levels during wet periods while amounts of release should be implemented during drier periods. The drop in elevation at the end of each year is caused because there is a constraint in the optimization model that the elevation at the end of the operating horizon must not be less than the elevation at the start of the operating horizon. Introduction of this constraint in optimization model ensures that the reservoir is not emptied at the end of the operating horizon. Based upon the forecast of temperature and precipitation, the reservoir manager shall have the flexibility of choosing the appropriate rule curve that could be adopted to maximize hydropower production.

In this research, DP has been used to derive operating rules for four inflow sequences comprising of a dry year, a wet year, a hot year and a cold year. The reason for choosing DP to obtain storage trajectories is that the solutions determined by DP for a given number of states and release decisions are globally optimal. Another reason for choosing DP is the generality of the objective functions that can be treated with the approach. The efficiency of the DP model however depends largely upon the levels of discretization of the state variables. Any increase in the number of discretizations would increase the number of evaluations of the recursive formula given by equation (1) and the computing time increase accordingly. The likelihood of achieving an optimum is also reduced with the increase in the number of discretizations. The approach developed here does not require discretization of state variables and is equally applicable to problems with discontinuous and non-differentiable objective functions. The approach leads to a computational procedure that has minimal memory and computational requirements without any loss in the generality of the problems that can be solved. A generic methodology based on the dynamic programming approach has been developed for the optimisation of operation of Bhakra reservoir.

VIII. CONCLUSIONS

The operation of reservoir systems is, therefore, likely to be more reliable if the impacts of potential climate change on inflow sequences are considered. A distinct practical advantage of the DP model developed herein is that it can be applied to develop optimal storage and elevation trajectories for any given inflow sequence. Further, operational policies of important reservoirs need to be re-examined in light of the changing climatic conditions. Rule curves derived using the DP model may be fed into the simulation model for economic post processing. The research presented herein is likely to lend credibility to recent climate change modelling efforts for a reservoir fed by Himalayan rivers that are anticipated to experience potentially serious impacts of climate change.

REFERENCES

[1] Bellman, R. 1957. Dynamic programming, Princeton University Press, Princeton, New Jersey.

[2] Burn, D. H., and Simonovic, S. P. 1996. Sensitivity of reservoir operations performance to climatic change. J. Water Resource Management, 10,463-478.

[3] Burn, D, Sharif, M., and Zhang, K. (2010) " Detection of Trends in Hydrological Extremes for Canadian Watersheds”, Journal of hydrological processes, doi: 10.1002/hyp.7625

[4] Christensen, N. S., Wood, A. W., Voisin, N., Lettenmaier, D. P., and Palmer, R. N. 2004. The effects of climate change on the hydrology and water resources of the Colorado River basin. Climate Change, 62, 337-363.

[5] IPCC. (2007). Climate change 2007: Impacts, adaption and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change, Cambridge, U.K.

[6] Khattak, M. S, Babel, M. S., and Sharif, M. (2011). Hydro-meteorological trends in upper Indus river basin. J. Climate Research, 46: 103-119.

[7] Klemes, V. 1985. Sensitivity of water resource systems to climatic variations, World Climate Program Report, WCP-98, WMO, 115 pp. [8] Marteen, S. K., Vries de, M. J., Van Oel, P. R., Araujo, J. S. (2011). Sustainability of small reservoirs and large scale water availability under current conditions and climate change, Water Resour Manage, doi 10.1007/s11269-011-9787-0.

[9] Minville, M., Brissette, F., Leconte, R. 2010. Impacts and uncertainty of climate change on water resource management of the Peribonka River System (Canada). J. Water Resource Planning and Management, 136(3), 376-385.

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 5, Issue 8, August 2015)

150 [11] Oliveira, R., and Loucks, D. P. 1997. Operating rules for

multireservoir systems. Water Resour. Res., 33(4), 839-852. [12] Sharif, M., and Wardlaw, R. 2000. Multi reservoir systems

optimization using genetic algorithms. J. Computing in Civil Engineering, 14(1), 255-263.

[13] Tu, M.-Y., Hsu, N. S., and Yeh, W. W. –G. 2003. Optimization of reservoir management and operation with hedging rules. J. Water Resource Planning and Management, 129(2), 86-97.

[14] Whitfield, P. H., and Cannon, A. J. 2000. Recent variations in climate and hydrology in Canada. Canadian Water Resources Journal, 25(1), 19-65.

Figure

Fig. 2 Inflow series for the driest, wettest, hottest, and coldest years in the historical record
Fig. 6 Optimal storage trajectory for the coldest year in the historical record

References

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