Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm

Rani, Deepti; Srivastava, D. K.

doi:10.1007/s40899-015-0036-1

Cited by 12 publications

(4 citation statements)

References 25 publications

(35 reference statements)

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“…Bilal et al [68] and Reddy and Kumar [69] used hydrological parameters as inputs in the optimization algorithm. Rani and Srivastava [70] involved dynamic programming and genetic algorithms to optimize irrigation and water supply. Jothiprakash and Shanthi [71,72] used a simple genetic algorithm in the Pechiparai dam.…”

Section: Application Of Meta-heuristics Algorithmsmentioning

confidence: 99%

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

Karakatsanis,

Patsialis,

Kalaitzidou

et al. 2023

Water

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The optimization of dam operations to transform them into multi-objective facilities constitutes a challenge for both hydrology, hydrogeology, and hydropower generation. However, the use of the optimal algorithm for such transformation is critically important. Additionally, the literature has highlighted that dams might negatively influence the recharge of groundwater. Within this study, we provide an overview of the available algorithms for the optimization of dam operations. Additionally, an overview focusing on hydropower generation in Greece illustrates the high potential of the Mediterranean region for hydropower generation and the application of MAR. The water quality of the reservoirs is also highlighted as a critical parameter. Within this study, we present indices for water quality monitoring in dam reservoirs, while the most prevailing index is the SRDD. This study constitutes a guide for researchers in choosing the optimal tools for the optimization of dam operations and the water quality monitoring of reservoirs. The present study suggests a meta-heuristic optimization methodology using the harmony search algorithm. The model uses a geometric model of the reservoir and calculates the level–supply curve. Furthermore, a multi-criteria optimization model was developed with two objective functions: the maximum power output from the hydroelectric power plant turbines and the optimal groundwater recharge. The model with appropriate parameter modifications can be applied to any small dam as it is a decision- and policy-making methodology, independent of local conditions. A further step is the application of these approaches dealing with field data and the numerical modeling of case studies. The interdisciplinary approach of this study links deferent aspect and scientific perceptions, providing a comprehensive guide to optimal water resource management and environmental sustainability.

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Section: Application Of Meta-heuristics Algorithmsmentioning

confidence: 99%

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

Karakatsanis,

Patsialis,

Kalaitzidou

et al. 2023

Water

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“…In the PSO algorithm, particle position vectors are used to dynamically represent possible solutions, allowing for rapid exploration and exploitation of the search space in pursuit of optimal solutions (Marini and Walczak, 2015;Houssein et al, 2021;Gad, 2022). Thus, the simplest way to proceed is to make decisions about supply and storage as iteration variables (Rani and Srivastava, 2016;Karami et al, 2019).…”

Section: Modified Particle Swarm Optimization (Mpso)mentioning

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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“…The optimization methods based on evolutionary theory have developed rapidly in recent years, and include the non-dominated sorting genetic algorithm II (NSGA-II) [29][30][31], ant colony optimization (ACO) [32,33], the artificial bee colony algorithm (ABCA) [34], particle swarm optimization (PSO) [35][36][37], the artificial neural network (ANN) [38,39], and the simulated annealing algorithm (SAA) [40]. The hybrid optimization method is a calculation method obtained by coupling more than two optimization methods [41,42]. This approach represents a trend in the research on complex problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stakeholder Coordinated Operation of Reservoir Considering Irrigation and Ecology

Wei

Lu³

et al. 2022

Water

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In traditional ecological operation, it is difficult to coordinate the balance among the interests of stakeholders, and stakeholders find it difficult to accept the operation scheme. To address these problems, this study proposed a method of multi-stakeholder coordinated operation of reservoir (MSCOR). By comprehensively considering the interest demands of stakeholders, the multi-stakeholder interval coordination mechanism (MSICM) for reservoir operation was established. The multi-stakeholder coordinated operation model (MSCOM) was constructed. The multi-stakeholder solution algorithm based on the MSICM, the non-dominated sorting genetic algorithm II, and the approach of successive elimination of alternative schemes based on the k-order and p-degree of efficiency (MSIC-NSGA-II-SEABODE) were applied to solve the MSCOR problem. The coordination mechanism, model construction, multi-stakeholder optimization, and multi-attribute decision making were coupled to establish a multi-stakeholder coordinated operation method, comprising the whole process of mechanism–modeling–optimization–decision making. Taking Baojixia Reservoir as an example, the performance of the coordinated operation method was compared with that of the traditional optimal operation method, and the relationship between the irrigation benefits and ecological benefits of the reservoir was explored. The results show that: (1) On the premise of the same satisfaction degree of basic irrigation interests, the ecological AAPFD value of coordinated operation decreased by 0.184, 0.469, and 0.886 in a normal year, dry year, and extraordinary dry year, respectively. The effect of coordinated operation on balancing various stakeholders was more obvious with the decrease in water inflow. (2) The MSICM ensures that the multi-stakeholder operation of the reservoir conforms to the principles of comprehensiveness, balance, and sustainability. (3) The coordination scheme obtained by the MSIC-NSGA-II-SEABODE algorithm is more reasonable and feasible. The research results provide a new idea and method to address the MSCOR problem.

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Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm

Cited by 12 publications

References 25 publications

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

Optimization of Dam Operation and Interaction with Groundwater: An Overview Focusing on Greece

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

Multi-Stakeholder Coordinated Operation of Reservoir Considering Irrigation and Ecology

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