Combined flow prediction and reservoir control system optimizes production at Hoa Binh docx

The procedure is applied for optimization of reservoir operation rule curves of the Hoa Binh reservoir in Vietnam, considering hydropower production and downstream flood control, and dem

The International Journal on Hydropower & Dams (2007) Volume Fourteen, issue 1,

80 - 83

Combined flow prediction and reservoir control system optimizes production at

Hoa Binh

H Madsen and J Høst-Madsen, DHI Water & Environment, Denmark

Long Le Ngo, Water Resources University, Vietnam

D Rosbjerg, Technical University of Denmark

A combined flow prediction and control system is described for the optimization

of multi-purpose reservoir operation The system integrates a numerical model for simulation of river flow and reservoir operation with an optimization tool The procedure is applied for optimization of reservoir operation rule curves of the Hoa Binh reservoir in Vietnam, considering hydropower production and downstream flood control, and demonstrates that it could be possible to generate

an extra 210 GWh/year on average

Reservoir operation is a complex problem that involves a number of often conflicting objectives, including flood control, hydropower generation, water supply for various users, navigation control and so on Traditionally, fixed reservoir rule curves have been used for guiding and managing the reservoir operation These curves specify reservoir releases according to the current reservoir level, hydrological conditions, water demands and time of the year Established rule curves, however, are often not very efficient for balancing the demands of the different users Moreover, reservoir operation often includes subjective judgements by the operators Thus, there is a potential for improving reservoir operating policies, and even small improvements can lead to large benefits

For optimization of reservoir operation, procedures based on coupling simulation models with numerical search methods have been developed In this paper the MIKE

11 modelling system developed by DHI Water & Environment is adopted for simulating the flow in the river system and reservoir operations The structure operation module in MIKE 11 allows for the implementation of complex control strategies, whereby reservoirs can be operated by defining a number of different control strategies with various conditions The use of several control strategies makes

it possible to simulate multi-purpose reservoirs, which take into account a large number of objectives

The MIKE 11 modelling system is combined with a numerical optimization tool that is used to optimize different control variables defined for the reservoir operation strategies The optimization tool includes a general multi-objective optimization framework that searches for the set of non-dominated or Pareto-optimal solutions according to the trade-offs between the various objectives

The simulation-optimization procedure is used in an off-line mode for optimization of reservoir operation rule curves using historical data Implementation of the optimised rule curves with MIKE 11 then provides a base-line reservoir operation system This operation system can be further improved in real-time by fine-tuning the reservoir releases using real-time and forecast information In this case, the MIKE 11 modelling and reservoir control system uses weather forecasts to provide forecasts of reservoir inflows, which are combined with the optimization tool to derive short-term, Pareto-optimal operation strategies

1 System description

The simulation-optimization approach has been applied for optimizing the operation of the Hoa Binh reservoir in Vietnam Hoa Binh is the largest reservoir in the country, with an active storage of 5.6 x 109 m3 It is a multi-purpose reservoir providing flood control, hydropower and water supply The powerplant is equipped with eight turbines, with a maximum unit capacity of 240 MW corresponding to a total power generating capacity of 1920 MW It produces on average 7800 GWh/year

The Hoa Binh reservoir is on the Da River, which is the largest tributary of the Red River system The Red River basin is in the northern and north-eastern part of Vietnam and has a total catchment area of 169 000 km2, of which 48% is in China and about 1% in Laos Three major upstream tributaries the Da, Thao and Lo, join and form the Red River delta near Hanoi The annual average discharge at Hanoi is about 3700

m3/s, of which Da River contributes more than 50% The seasonal variability is significant with about 80% of the annual rainfall occurring in the flood season from

May to October In the analysis, the conflict between flood control and hydropower generation in the flood season is specifically addressed

Fig 1 MIKE 11 model setup of the lower part of the Red river basin, including the

Hoa Binh reservoir

The MIKE 11 modelling system is set up for the lower part of the Red River basin to simulate inflow to the Hoa Binh reservoir and water flow in the downstream part (see Figure 1) To simulate the releases from the Hoa Binh reservoir, operational structures, including bottom sluice gates, spillways and turbines, are specified as control structures in MIKE 11 The control structures are implemented with control strategies which determine how the structures are operated, based on the reservoir level and the water level at a downstream flood control point in Hanoi The operations consist of specifying the discharge through turbines as well as opening and closing bottom gates and spillways The control strategies are defined using a list of logical statements according to priorities of the different controls

Opening and closing of the bottom gates and spillways for flood control are described according to flood control rule curves in terms of target water levels in the reservoir and water levels at the flood control point in Hanoi The discharge through turbines is defined according to hydropower control curves in terms of critical, lower and upper reservoir level curves

2 Optimization of reservoir rule curves

The control variables to be optimized consist of the reservoir water level curves and water level targets at the flood control point at Hanoi The operation rules are optimized using a two-step procedure First, the flood control variables are optimized with respect to two objectives:

 Flood control in terms of the downstream water level; and,

 Hydropower potential in terms of reservoir level

In the second step, the hydropower control variables are optimized with respect to the hydropower generation in the flood season and the reservoir level at the end of the flood season (used as a surrogate for hydropower generation in the low flow season) For the optimization, selected data from the historical record are used as input to the MIKE 11 model

The main purpose of the optimization is to highlight the trade-offs between the flood control and hydropower objectives Multi-objective optimization seeks the non-dominated or Pareto-optimal set of solutions with respect to the given objective functions for evaluation of these trade-offs A set of solutions are identified, where none of the objective functions can be improved without violating one or more of the others From this curve (denoted the Pareto front) the decision-maker can choose a preferred strategy One important benefit of using Pareto optimization is that different objective functions measured in different units can be optimized simultaneously without the need to use a common monetary unit, which is often difficult to apply

The results of the optimization of flood control variables are shown in Figure 2 The optimization problem is defined as minimization of the hydropower deficit compared with the maximum hydropower generation capacity (denoted hydropower objective in Figure 2) and minimization of the maximum water level at Hanoi (denoted flood control objective in Figure 2) As expected, a significant trade-off is observed between the two objectives That is, an improvement in hydropower generation (decrease of hydropower deficit) can only be obtained by an increase in the maximum water level

at Hanoi, and vice versa The figure shows a balanced optimum solution obtained as part of the optimization, which is seen to provide an appropriate balance between the two objectives In the figure also shows the point corresponding to using the present reservoir regulations Importantly, the optimization provides Pareto-optimal solutions that are better with respect to both hydropower generation and flood control Thus, more optimal flood control rules can be implemented that provide an increase in hydropower production in the flood season without violating the basic flood control objective

Present regulation

Balanced optimum 1100

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Flood control objective

Pareto optimum

Fig 2 Pareto optimal solutions for optimization of flood control variables compared

to the present regulations

Also in the case of optimisation of the hydropower control curves a significant trade-off between the two considered objectives is observed That is, an increase in hydropower production in the flood season can only be obtained by a decrease of the water level at the end of the flood season, and vice versa The results of this optimization show that Pareto-optimal solutions can be chosen which are better with respect to both objectives compared with the present regulations, that means, more optimal solutions can be chosen to provide increased hydropower production in the flood season as well as larger reservoir level at the end of the flood season (and hence increased hydropower potential in the low flow season)

3 Simulation with optimized rule curves

A 20-year historical record was used as input to the MIKE 11 model to simulate reservoir operation using the present regulations and the balanced Pareto-optimal solution The hydropower generated in the analysed flood seasons using the two operation strategies, is shown in Figure 3 In most flood seasons the balanced optimum solution provides an increase in hydropower production compared with the present regulations On average an increase of 1.8 percent is obtained, corresponding to 80 GWh/year

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Fig 3 Simulated hydropower generation in the flood season using, respectively, the

present regulations and the balanced Pareto-optimal rule curves

The simulated reservoir level at the end of flood season using the two operation strategies is shown in Figure 4 In most seasons the balanced optimum solution provides an increase in water level compared with the present regulations Importantly, the balanced solution provides a substantial increase in water level in the dry years On average an increase of about 3 m is obtained The increase in water level at the end of

the flood season provides an increased hydropower potential in the low flow season, corresponding to about 130 GWh/year on average Thus, in total, the balanced optimum solution offers an increased hydropower production of 210 GWh/year on average without compromising flood control

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Present regulations Balanced optimum

Fig 4 Simulated water level at the end of the flood season using, respectively, the

present regulations and the balanced Pareto-optimal rule curves

4 Real-time optimization

Operation of reservoir systems using optimized rule curves will provide a general optimal operation of the system To further improve the performance, real-time optimization can be adopted, where real-time and forecast information about reservoir levels, reservoir inflows and water demands for various users are used In this case, the reservoir system is optimised with respect to the short-term operation, using both

long-term benefits, and hence the inclusion of long-term objectives in the optimization

is important

For the Hoa Binh reservoir a real-time optimization strategy has been implemented The control variables that include discharge through turbines and opening and closing

of bottom gates and spillways are optimized at 6 hour time intervals in a three-day forecast period Short-term objectives are defined in terms of hydropower production and flood risk at Hanoi in the forecast period Long-term objectives are implemented

by penalizing the deviation of the reservoir level at the end of the forecast period from the target levels defined by the optimized rule curves Thus, short-term optimizations resulting in operations that provide large deviations in reservoir level compared with the rule curves are penalized In the Pareto optimization the trade-off between short-term operation objectives and long-short-term penalizing short-terms is evaluated From the Pareto-optimal set the decision-maker can then choose a preferred solution taking other considerations into account

A real-time optimization test is considered in a situation where a large flood is forecasted in the Da River as inflow to the Hoa Binh reservoir (see Figure 5) The forecasted inflow show a peak about 12 hours after time of forecast, followed by a decrease in the remaining forecast period Thus, in this case a preferred operation strategy is to try to keep as much water in the reservoir as possible, to reduce the downstream flood risk That is, a Pareto-optimal solution is chosen that allows a larger water level in the reservoir at the end of the forecast period compared with the flood control rule curve to reduce the water level at Hanoi At the same time an increase in hydropower production during the forecast period is obtained The results of this preferred solution are shown in Table 1 and can be compared with the results obtained

by operating the reservoir according to the optimized rule curves Real-time

optimization provides an increase in hydropower production in the forecast period of 5.5 GWh (4.3 percent) and a decrease in the maximum water level at Hanoi of 0.81 m

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3 /s]

Da River Thao River

Lo River

Time of forecast

Forecast Observed

Fig 5 Inflow forecasts at the three upstream tributaries used for optimising

short-term reservoir operations

Table 1 Comparison of simulation results using the optimised rule curves and the

real-time optimal solution

Reservoir level at the end of the

Hydropower generation

Maximum water level at Hanoi