Optimal Choices in Decision Supporting System for Network Reservoir Operation

Techarungruengsakul, Rapeepat; Ngamsert, Ratsuda; Thongwan, Teerawat; Hormwichian, Rattana; Kuntiyawichai, Kittiwet; Ashrafi, Seyed Mohammad; Kangrang, Anongrit

doi:10.3390/w14244090

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…The term "reservoir operating policy" is frequently employed to describe this. While it may seem like an obvious challenge to avoid flooding throughout the monsoon season, water conservation measures must be taken (Kaczmarek and Kindler, 1982;Techarungruengsakul et al, 2022). Therefore, to attain the best possible system performance in reservoir operating issues, decisions on releases in addition to storage must be made over time, while taking into significant differences in inflows and demands (Turner et al, 2021).…”

Section: Relevant Literature Studymentioning

confidence: 99%

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Verma,

Sahu,

Prasad

et al. 2023

J ENVIRON INFORM LET

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The effective operation of reservoirs has been a key concern among the water resources management authorities as a consequence of global water shortages and decreased surface water runoff owing to increasing water demand and the effect of climate change. Water shortages, economic constraints, and environmental concerns have all contributed to the optimization of operations becoming increasingly replaced by new technologies. Optimization operation of water resources system is a challenging issue because of its nonlinearity, multiple constraints, and several dimensions. To address the challenges associated with the operation of the multipurpose Mahanadi reservoir in Chhattisgarh, India. The present study utilizes the application of modified particle swarm optimization (MPSO). The present study was conducted to evaluate the performance of the particle swarm optimization (PSO) model in comparison to the current operating reservoir system, to minimize the sum of squared deviations in downstream release and demand. The average percentage changes in reliability (100.31%), resilience (85.02%), sustainability (24.54%), as well as vulnerability reduced up to 69.54%. The model also had the lowest error parameters in the system such as (RMSE = 1.3892, MAPE = 0.1003, NMSE = 0.1025, MAE = 38.6689, and MSE = 1.9299), despite having the highest R2, i.e., 0.8974. When applied to the Ravishankar Sagar reservoir, MPSO yields optimal, worst, average, and standard deviation (SD) values of 0.45, 0.56, 0.51, and 0.038, respectively. In terms of optimizing the release and storage rates, MPSO performed consistently better than the PSO and other metaheuristics reviewed from the literature during the study. Therefore, MPSO is advantageous in the search for the optimal reservoir operation policy because it is easy to implement, requires less functional evaluations, and quickly tracks global optimum. Hence this study provides significant evidence that MPSO can be used to effectively solve real optimization challenges.

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Section: Relevant Literature Studymentioning

confidence: 99%

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Verma,

Sahu,

Prasad

et al. 2023

J ENVIRON INFORM LET

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“…Several notable swarm algorithms have been employed in the field of optimization. These include particle swarm optimization (PSO) [33], cuckoo search algorithm (CS) [34,35], firefly algorithm (FA) [36], flower pollination algorithm (FPA) [37], gray wolf optimizer (GWO) [38], wind-driven optimization (WDO) [39,40], ant colony optimization (ACO) [41][42][43], honey-bee mating optimization (HBMO) [44][45][46], and Harris Hawks optimization (HHO) [47], among others. These swarm algorithms have shown effectiveness in various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Sriworamas,

Prasanchum,

Ashrafi

et al. 2023

Water

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This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for anticipated situations. The implementation of these curves can significantly enhance reservoir management practices and facilitate the resolution of water resource challenges, such as floods and droughts.

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“…Events that influence water resources also affect people, animals, and plants and require systematic planning. The system is required to consider many elements of information, such as physical data, hydrological data, and the amount of sediment in the area, as well as the participation of stakeholders, to achieve the highest efficiency and be suitable for various situations [50][51][52][53]. The decision supporting system in water resources management directly involves many factors, such as social factors, environmental factors, economic factors, and engineering factors, as well as reservoir management.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Solution in Decision Supporting System for River Basin Management Consisting of a Reservoir System

Ngamsert

Techarungruengsakul

Kaewplang

et al. 2023

Water

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Decision support systems tackle problems and require systematic planning. They consider physical data, hydrological data, and sediment levels to achieve efficiency and adaptability in various situations. Therefore, this research aims to identify alternative engineering choices for the management of a river basin with a single reservoir system. Optimization techniques, including marine predator algorithm (MPA), genetic algorithm (GA), genetic programming (GP), tabu search (TS), and flower pollination algorithm (FPA), were applied to find the optimal reservoir rule curves using a reservoir simulation model. The study focused on the Ubolratana Reservoir in Thailand’s Khon Kaen Province, considering historic inflow data, water demand, hydrologic and physical data, and sedimentation volume. Four scenarios were considered: normal water scarcity, high water scarcity, normal excess water, and high excess water. The optimal rule curves derived from the reservoir simulation model, incorporating sedimentation and hedging rule (HR) criteria, were found to be the best engineering choices. In the normal and high water scarcity scenarios, they minimized the average water shortage to 95.558 MCM/year, with the lowest maximum water shortage 693.000 MCM/year. Similarly, in the normal and high excess water scenarios, the optimal rule curves minimized the average excess water, resulting in a minimum overflow of 1087.810 MCM/year and the lowest maximum overflow 4105.660 MCM/year. These findings highlight the effectiveness of integrating optimization techniques and a reservoir simulation model to obtain the optimal rule curves. By considering sedimentation and incorporating HR criteria, the selected engineering alternatives demonstrated their ability to minimize water shortage and excess water. This contributes to improved water resource management and decision-making in situations of scarcity and excess.

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Optimal Choices in Decision Supporting System for Network Reservoir Operation

Cited by 5 publications

References 43 publications

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Modified Particle Swarm Optimization for the Optimum Use of Multi-Reservoir Systems: MRP Complex, Chhattisgarh

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Optimizing Solution in Decision Supporting System for River Basin Management Consisting of a Reservoir System

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