Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

Li, Fang‐Fang; Shoemaker, Christine A.; Qiu, Jun; Wei, Jiahua

doi:10.1016/j.envsoft.2014.11.030

Cited by 33 publications

(6 citation statements)

References 42 publications

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“…Therefore, it was concluded that SFLA outperforms the GA and PSO. Li et al (2014) applied seven heuristic algorithms to minimize the amount of water use and maximize the total power generation by the Three Gorges Reservoir, including the binary-coded GA, PSO, SA, dynamically dimensioned search (DDS), dynamic coordinate search using response surface models (DYCORS), and stochastic radial basis function method (SRBF). The latter authors concluded that PSO performed best in optimizing two objectives of the selected case study, which occurred due to increase in diversity of solutions and less dependent on the previous generation compared with other discussed approaches.…”

Section: The Binary-coded Gamentioning

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: The Binary-coded Gamentioning

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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“…These decision variables are subjected to the following constraints: the radii of the Gaussian functions range from 0 to 1, the center coordinate of each function has the range 21 to 1, and the weighted combinations are non-negative and sum to 1 [Busoniu et al, 2011]. A genetic algorithm (GA) was linked with the system model in order to optimize system performance, as evolutionary algorithms have been shown to perform well for the optimization of reservoir operation [Giuliani et al, 2015;Li et al, 2015;Tsoukalas and Makropoulos, 2015;Yang et al, 2015].…”

Section: Assessing Optimized System Performancementioning

confidence: 99%

A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

Culley

Noble

Yates

et al. 2016

Water Resources Research

101

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Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large‐scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom‐up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.

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“…Meanwhile, when solving a MLGS problem, many complex constraints must be taken into account. These complex constraints and the conflicting objectives of annual hydropower generation and ecological flow demands make MLGS problems difficult to solve (Al-Aqeeli et al 2016;Feng et al 2018;Li et al 2015b). Therefore, this paper focuses on the MLGS problem of a CHS in which maximum annual hydropower generation and minimum annual ecological underflow and overflow water volume are considered simultaneously when satisfying a set of complex constraints.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

Yang

et al. 2019

Water Resour Manage

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Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade hydropower system (CHS). A novel adaptive multi-objective particle swarm optimization based on decomposition and dominance (D 2 AMOPSO) is developed in this paper to solve the MLGS problem. In D 2 AMOPSO, a constraint handling method based on repair strategy and individualconstraints and group constraints (ICGC) technique is embedded to address various constraints. An improved logistic map is adopted to initialize the population. During the evolutionary process, an improved Tchebycheff decomposition is introduced to select personal best and global best for each particle, and the non-dominated solutions found so far are stored in an external archive where crowding distance and elitist learning strategy are performed to improve its diversity. Meanwhile, an adaptive flight parameter adjustment mechanism based on Pareto entropy is adopted to balance the global exploration and local exploitation abilities of the population. A normal cloud mutation operator is used to keep the population diversity and escape local minima. In the case study of the Three Gorges Cascade hydropower system (TGC) under three typical years, the results of the proposed method and other four competitors show that D 2 AMOPSO can obtain better diversity and faster convergence solutions for the MLGS problem in less time.

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Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

Cited by 33 publications

References 42 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

A bottom‐up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

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