Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study

Karamouz, ‪Mohammad; Nazif, Sara; Sherafat, Mohammad Ali; Zahmatkesh, Zahra

doi:10.1007/s11269-014-0686-z

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…First, the random parameters, as well as the initial velocity and position, are considered for the PSO algorithm [49]. The objective function is calculated for each member, and the best particle among the remaining particles is determined; then, the velocity and position are updated based on Equations (6) and (7), respectively [46,49]. Thus, the convergence criteria are stopped, and if the algorithm is satisfied, the algorithm finishes; otherwise, the algorithm returns to the first step.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…The optimal operation of stored water resources in the form of reservoirs behind dams is an important and complicated issue for decision-makers and designers worldwide, because optimal operations can decrease the expenditure of constructing large dams for policymakers in the water resource management field [5]. Thus, several studies have investigated the optimal operation of reservoirs to satisfy downstream consumer demands and supply water based on high certainty [6][7][8]. Recently, mathematical models and evolutionary algorithms have been used in the management and planning of water resources [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Yaseen,

Ehteram,

Hossain

et al. 2019

Sustainability

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Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Yaseen,

Ehteram,

Hossain

et al. 2019

Sustainability

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“…In such scenarios, traditional nonlinear optimization methods are susceptible to local optima and struggle to identify global optimal solutions [16]. In recent years, intelligent algorithms [17] and evolutionary algorithms [18] have offered potential solutions to this problem. Among the available computational methods, the particle swarm optimization algorithm [19], ant colony algorithm [20], artificial neural networks [21], and especially genetic algorithms [22][23][24], along with their improved versions [25][26][27], have been widely adopted in optimal reservoir scheduling and management due to their notable advantages.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Control of Flood Limited Water Levels for Parallel Reservoirs by Considering Forecast Period Uncertainty

Li,

Feng

et al. 2023

Sustainability

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The objective of this study is to achieve the dynamic optimization of the flood limited water level (FLWL) in parallel reservoirs, using Luhun Reservoir and Guxian Reservoir as case studies. The innovation lies in establishing a dynamic control optimization model for the FLWL of parallel reservoirs, considering the uncertainty in the forecasting period of the flood forecast due to the varying locations of the rainstorm center from upstream to downstream. To commence, the Fisher optimal segmentation method is employed for flood season staging to determine the staged FLWL of each reservoir. Subsequently, considering the uncertainty in the foresight period, the upper range of the dynamic FLWL is determined through the improved pre-discharge capacity constraint method and Monte Carlo simulation. Finally, a multi-objective optimization model is established to determine the optimal dynamic FLWL control operation scheme for parallel reservoirs, utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This model takes into account both downstream flood control requirements and the water supply benefits of the parallel reservoirs. Through the optimization of the scheme, the water supply of the parallel reservoirs can be augmented by 15,347.6 m3 during the flood season. This optimization effectively achieves a harmonious balance between flood control and water supply, holding significant implications for mitigating drought risks amid changing conditions.

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“…Lately, with the advancement of optimization methods and the development of meta‐heuristic techniques in general and the imperialist competitive algorithm (ICA) in particular, the optimal operation of dam reservoir systems has entered a new phase. Owing to the wide scope of themes identified with water assets, different examinations on the utilization of the ICA calculation, for example, Acharya, Panda, & Lakshmi (2010); Karamouz et al (2014); Afshar, Emami Skardi, & Masoumi (2015); Hosseini‐Moghari et al (2015); and Biyanto et al (2015), have been conducted.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation of water resources systems using MOICA algorithm with reservoir hedging approach in low‐water regions*

Yarmohammadi

Izadbakhsh

Rajabi

et al. 2021

Irrigation and Drainage

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Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study

Cited by 18 publications

References 18 publications

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Dynamic Control of Flood Limited Water Levels for Parallel Reservoirs by Considering Forecast Period Uncertainty

Optimal operation of water resources systems using MOICA algorithm with reservoir hedging approach in low‐water regions*

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