# Optimal Choices in Decision Supporting System for Network Reservoir Operation

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Area

^{6}m

^{3}) and a dead storage capacity of 3800 MCM. The amount of inflow flowing into the Bhumibol reservoir for 57 years (during 1964–2020) is shown in Figure 3.

#### 2.2. Establishing a Decision Support System

#### 2.2.1. Establishing a Database

#### 2.2.2. Establishing a Model Base

#### Network Reservoir Operation Model

_{ν},

_{τ}is the available water for month τ during year ν; S

_{ν},

_{τ}

_{−1}is the stored water at the end of month τ − 1 during year ν; Q

_{ν},

_{τ}is the monthly inflow to the reservoir during year ν; E

_{τ}is the monthly evaporation loss during year ν; and R

_{ν},

_{τ}is the monthly release during year ν, which is considered via the operating policy.

#### Network Reservoir Operating Policy

- Standard operating policy

- Hedging rule

_{ν}is the water shortage during year ν (the year in which releases are lower than the target demand), and Sp

_{ν}is the excess spilled water during year ν (the year in which releases are higher than the target demand).

## 3. Results and Discussion

#### 3.1. The Best Choice in Decision Making for Network Reservoirs

#### 3.1.1. Scenario of Normal Water Scarcity

#### 3.1.2. Scenario of High Water Shortage

#### 3.1.3. Scenario of Normal Overflow

#### 3.1.4. Scenario of High Overflow

#### 3.2. The Model Basis

- Firstly, the model starts with the input data and all initial necessary data, such as the upper and lower bound data of the reservoir and objective function.
- The optimization technique starts with a set of network reservoir rule curves generated for the initial population {X1, X2, ..., Xn} that is created randomly within the feasible space (note: the feasible space is the value between the dead storage capacity and the normal high water level of the considered reservoir).
- After the first set in the initial population have been calculated (48 simultaneous decision variables consist of 24 values from the upper rule curves and 24 values from the lower rule curves for both reservoirs), for this study, each decision variable represents the monthly rule curves of the reservoir, which are defined as the upper and lower rule curves of the Bhumibol and Sirikit reservoirs.
- The monthly release of water will be calculated by the reservoir simulation model considering these rule curves (fitness evaluations) in accordance with the criteria.
- Next, the released water is used to determine the objective functions that were described in the previous section’s procedure. After this, the reproduction process will create new values of rule curves in the next generation (population of agents). Thereafter, the procedure is repeated until the criteria are satisfied and optimal rule curves are then obtained for different scenarios.
- The obtained rule curves based on the above optimization techniques with the same condition on each scenario will be similar according to the optimal solution, and the obtained rule curves are as shown in Figure 13.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Historical inflow and average downstream water requirements of the Bhumibol and Sirikit reservoirs.

**Figure 6.**Alternative choices for operating network reservoirs under different rule curve scenarios.

**Figure 9.**Optimal choice for operating rule curves under serious effect of high water shortage situation.

Data | Period | Source |
---|---|---|

Historical inflow | 1964–2020 | Electricity Generating Authority of Thailand (EGAT) |

Average downstream water requirements | 1964–2020 | Royal Irrigation Department |

Rainfall data | 1964–2020 | Thai Meteorological Department (TMD) |

Hydrological data of reservoirs | 2020 | Electricity Generating Authority of Thailand (EGAT) |

Reservoir physical data | 2020 | Electricity Generating Authority of Thailand (EGAT) |

Synthetic inflow of 1000 events | 1964–2020 | HEC-4 simulation results |

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**MDPI and ACS Style**

Techarungruengsakul, R.; Ngamsert, R.; Thongwan, T.; Hormwichian, R.; Kuntiyawichai, K.; Ashrafi, S.M.; Kangrang, A.
Optimal Choices in Decision Supporting System for Network Reservoir Operation. *Water* **2022**, *14*, 4090.
https://doi.org/10.3390/w14244090

**AMA Style**

Techarungruengsakul R, Ngamsert R, Thongwan T, Hormwichian R, Kuntiyawichai K, Ashrafi SM, Kangrang A.
Optimal Choices in Decision Supporting System for Network Reservoir Operation. *Water*. 2022; 14(24):4090.
https://doi.org/10.3390/w14244090

**Chicago/Turabian Style**

Techarungruengsakul, Rapeepat, Ratsuda Ngamsert, Teerawat Thongwan, Rattana Hormwichian, Kittiwet Kuntiyawichai, Seyed Mohammad Ashrafi, and Anongrit Kangrang.
2022. "Optimal Choices in Decision Supporting System for Network Reservoir Operation" *Water* 14, no. 24: 4090.
https://doi.org/10.3390/w14244090