MILP for Optimizing Water Allocation and Reservoir Location: A Case Study for the Machángara River Basin, Ecuador

Veintimilla-Reyes, Jaime; Meyer, Annelies De; Cattrysse, Dirk; Tacuri, Eduardo; Vanegas, Pablo; Cisneros, Félipe; Orshoven, Jos Van

doi:10.3390/w11051011

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…The optimum water releases developed using LP model is compared with the actual releases (table 5). The model was successful in increasing the water release and water storage without any water shortages during monsoon and post monsoon [8] [10]. From Regression plot (Fig.…”

Section: Fig 4regression Of Releases and Demandmentioning

confidence: 84%

Optimization of Reservoir Operation using Linear Programming

Raju*,

Gowda C,

B S

2020

IJRTE

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The paper aims to derive the optimal releases monthly through linear programming for a single purpose reservoir. The releases from the reservoir are usually based upon the rule curves or operating policy adopted. The rule curve is the storage, indicating the water levels to be maintained in-order to satisfy the demand during the operation period. Linear programming (LP) is one of the global optimization techniques that have gained popularity as a means to attain reservoir operation. In the present study Linear Programming was used to develop an operation policy for Hemavathy Reservoir, Hassan District Karnataka, India. The decision variables were monthly reservoir releases for irrigation and initial storages in reservoir at beginning of the month. The constraint bound for the reservoir releases was reservoir storage capacity. The results derived by using Linear Programming shows that the downstream irrigation demands were satisfied and also considerable amount of water was conserved from reduced spills.

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Section: Fig 4regression Of Releases and Demandmentioning

confidence: 84%

Optimization of Reservoir Operation using Linear Programming

Raju*,

Gowda C,

B S

2020

IJRTE

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“…The pressure constraint for this study is upper and lower pressure that gives the optimal cost for clean water distribution which is given by equation ( 10) and equation (11).…”

Section: Pressure Constraintmentioning

confidence: 99%

“…PSO method was used in the study of optimization of tree Pipe networks layout and size, by [10]. [11] Used mixed integer Linear Programming (MLP) to optimize the allocation of water and the location of one more reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Clean Water Distribution Networks

Edward¹,

Mkandawile²,

Masanja³

2020

J. Math. Inf.

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In this study, an optimal clean water distribution system cost model has been developed to find the minimum cost to distribute clean water. The model was then tested with real data collected from Ihumwa water distribution network of Dodoma city and other treatment cost data from literature to test the workability of the model. Hydraulic parameters such as head losses of the pipes, flow velocity and pipe pressure are calculated using water flow software. The resulted model was solved using LINGO software and the optimal cost of clean water distribution system was found by testing the different maximum and minimum velocity and pressure that give an optimal cost.

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“…Meng et al [6] proposed an improved multi-objective cuckoo search algorithm and discussed the competition relationship between power generation and water supply of the Xiaolangdi cascade hydropower stations on the Yellow River. In addition, more and more water resources management, water allocation, and reservoir operation problems have also been studied [23][24][25][26].In China, the water resources problem is also one of the most important strategic problems affecting the development of the country. The Yangtze River plays an irreplaceable role in promoting the economic and social development of our country [3], and the upper reaches of the Yangtze River, from the source to Yichang, Hubei Province, have huge potential hydropower development capacity and utilization value due to its large terrain difference and high flowrate volume.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

Chen

Dong

Jia

et al. 2019

Water

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The multi-objective optimal operation and the joint scheduling of giant-scale reservoir systems are of great significance for water resource management; the interactions and mechanisms between the objectives are the key points. Taking the reservoir system composed of 30 reservoirs in the upper reaches of the Yangtze River as the research object, this paper constructs a multi-objective optimal operation model integrating four objectives of power generation, ecology, water supply, and shipping under the constraints of flood control to analyze the inside interaction mechanisms among the objectives. The results are as follows. (1) Compared with single power generation optimization, multi-objective optimization improves the benefits of the system. The total power generation is reduced by only 4.09% at most, but the water supply, ecology, and shipping targets are increased by 98.52%, 35.09%, and 100% at most under different inflow conditions, respectively. (2) The competition between power generation and the other targets is the most obvious; the relationship between water supply and ecology depends on the magnitude of flow required by the control section for both targets, and the restriction effect of the shipping target is limited. (3) Joint operation has greatly increased the overall benefits. Compared with the separate operation of each basin, the benefits of power generation, water supply, ecology, and shipping increased by 5.50%, 45.99%, 98.49%, and 100.00% respectively in the equilibrium scheme. This study provides a widely used method to analyze the multi-objective relationship mechanism, and can be used to guide the actual scheduling rules.2 of 23 requirements for the water head and flow of hydropower stations between power generation and other benefit objectives, there exists mutual influence and interdependence among these benefit objectives [5]. Therefore, how to deal with the impact between these objectives and maximize the benefit of limited water resources is the focus and difficult point of current research. Many scholars have focused on the optimal operation of reservoirs and reservoir groups in specific areas.Many algorithms can be used to solve reservoir optimal operation and water management problems with the development of computing ability; such algorithms can be classified as classic or evolutionary methods [6]. However, the classic methods always perform poorly in solving complex problems, which makes the evolutionary methods develop rapidly [7]. Therefore, evolutionary methods are frequently used in water management problems, such as particle swarm optimization (PSO) [8-10], genetic algorithm (GA) [11][12][13], and so on [14][15][16]. With the increase in the pursuit of multi-objective benefits by decision makers, the multi-objective optimization algorithms (MOEAs) have received more attention and been improved a lot, such as gravity search algorithm (GSA) [7], strength Pareto evolutionary algorithm (SPEA) [17], non-dominated sorting genetic algorithm-III (NSGA-III) [18], and so on. With the de...

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MILP for Optimizing Water Allocation and Reservoir Location: A Case Study for the Machángara River Basin, Ecuador

Cited by 13 publications

References 18 publications

Optimization of Reservoir Operation using Linear Programming

Optimization of Reservoir Operation using Linear Programming

Modeling and Optimization of Clean Water Distribution Networks

Multi-Objective Joint Optimal Operation of Reservoir System and Analysis of Objectives Competition Mechanism: A Case Study in the Upper Reach of the Yangtze River

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