Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm

Al-Aqeeli, Yousif H.; Agha, Omar M. A. Mahmood

doi:10.1007/s11269-020-02583-8

Cited by 34 publications

(5 citation statements)

References 19 publications

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“…One of the most popular EAs in this area is the MPSO algorithm. Numerous research has used conventional PSO or provided a variant of it (Al-Aqeeli and Mahmood Agha, 2020).…”

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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“…One of the most popular EAs in this area is the MPSO algorithm. Numerous research has used conventional PSO or provided a variant of it (Al-Aqeeli and Mahmood Agha, 2020).…”

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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“…EC models, known as heuristic searching tools, include metaheuristic algorithms such as Genetic algorithms (GA) [67][68][69][70][71], Ant colony optimization (ACO) [72], Particle swarm optimization (PSO) [73,74], Simulated annealing (SA) [75,76], and Honey bees mating optimization (HBMO) [77]. Unlike other algorithms, meta-heuristic algorithms are known for their efficiency in searching for near-optimal (i.e., Pareto optimal) solutions.…”

Section: Topic 1: Optimization Modelsmentioning

confidence: 99%

Dam System and Reservoir Operational Safety: A Meta-Research

Badr,

Li,

El-Dakhakhni

2023

Water

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Dams are critical infrastructure necessary for water security, agriculture, flood risk management, river navigation, and clean energy generation. However, these multiple, and often conflicting, objectives introduce complexity in managing dam operations. In addition, dam infrastructure has been evolving as complex systems-of-systems with multiple interacting components and subsystems, all susceptible to a wide range of uncertainties. Such complexities and uncertainties have triggered extensive research initiatives focused on dam systems and reservoir operational safety. Focusing on the latter, this paper meta-researches (conducts research-on-research) previously published studies to identify the critical research gaps and propose future research directions. In this respect, this paper first performs a quantitative analysis of the pertinent literature, using text mining and subsequent topic modeling, to identify and classify major and uncover latent topics in the field. Subsequently, qualitative analysis is conducted to critically review the identified topics, exploring the concepts, definitions, modeling tools, and major research trends. Specifically, the study identified seven topics: optimization models; climate change; flood risk; inflow forecasting; hydropower generation; water supply management; and risk-based assessment and management. The study also presents three main research gaps associated with the limitations in modeling concepts, modeling tools capabilities, and the lack of resilience-guided management of dam operational safety. Overall, this study presents a road map of the currently available dam and reservoir operational safety research and associated knowledge gaps, as well as potential future research directions to ensure the resilience of such critically important infrastructure, especially in the age of climate change.

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“…PSO is well-known and is widely used in various engineering fields [ 112 ]. The trade-off between maximising hydropower and flood mitigation, with the PSO at the Mosul Dam and Badush Dam in Iraq was demonstrated by [ 34 ]. The first model was the PSO algorithm for a single reservoir system (PSOS), which was developed to validate the optimisation model.…”

Section: Review Of Swarm Intelligence (Si) For Optimising Reservoir O...mentioning

confidence: 99%

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

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Optimal Operation of Multi-reservoir System for Hydropower Production Using Particle Swarm Optimization Algorithm

Cited by 34 publications

References 19 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

Dam System and Reservoir Operational Safety: A Meta-Research

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

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