Multiobjective Evolutionary Approach to Optimal Reservoir Operation

Schardong, André; Simonović́, Slobodan P.; Vasan, Arunchandar

doi:10.1061/(asce)cp.1943-5487.0000213

Cited by 20 publications

(5 citation statements)

References 23 publications

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“…Therefore, it was reported that the MODE obtained better Pareto optimal solutions than the NSGA-II. The MODE was applied in two papers (Reddy and Kumar 2008;Schardong et al 2013) dealing with non-hydropower reservoirs. Schardong et al 2013 compared the MODE with the NSGA-II considering three test functions and reported that the MODE outperformed the NSGA-II in terms of convergence and coverage of the true Pareto front.…”

Section: Multi-objective Differential Evolutionmentioning

confidence: 99%

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2019

Environ Monit Assess

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Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

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Section: Multi-objective Differential Evolutionmentioning

confidence: 99%

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2019

Environ Monit Assess

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“…EM-MOPSO was applied to the operation of the Bhadra reservoir in India, with three objectives considered: maximizing hydropower generation, minimizing irrigation deficit, and maximizing water quality satisfaction downstream. Schardong et al [11] applied a multi-objective differential evolution (DE) algorithm to a complex water supply problem related to the Sao Paulo metropolitan area in Brazil. Six reservoirs provide water for almost 20 million people in the metropolitan area.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

Sun

Wang

et al. 2016

Energies

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Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system's economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

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“…A great number of NSGA-II applications can be found in multi-objective water-resources problems, such as those described in [23][24][25][26][27][28].…”

Section: Nsga-iimentioning

confidence: 99%

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Qiu

2015

Energies

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Abstract:To maximize annual power generation and to improve firm power are important but competing goals for hydropower stations. The firm power output is decisive for the installed capacity in design, and represents the reliability of the power generation when the power plant is put into operation. To improve the firm power, the whole generation process needs to be as stable as possible, while the maximization of power generation requires a rapid rise of the water level at the beginning of the storage period. Taking the minimal power output as the firm power, both the total amount and the reliability of the hydropower generation are considered simultaneously in this study. A multi-objective model to improve the comprehensive benefits of hydropower stations are established, which is optimized by Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The Three Gorges Cascade Hydropower System (TGCHS) is taken as the study case, and the Pareto Fronts in different search spaces are obtained. The results not only prove the effectiveness of the proposed method, but also provide operational references for the TGCHS, indicating that there is room of improvement for both the annual power generation and the firm power.

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Multiobjective Evolutionary Approach to Optimal Reservoir Operation

Cited by 20 publications

References 23 publications

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

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