Optimal Reservoir Operation Using Bat and Particle Swarm Algorithm and Game Theory Based on Optimal Water Allocation among Consumers

Zarei, Alí; Mousavi, Sayed‐Farhad; Gordji, M. Eshaghi; Karami, Hojat

doi:10.1007/s11269-019-02286-9

Cited by 41 publications

(13 citation statements)

References 32 publications

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“…In addition to the conventional BA many researchers have employed the improved versions of this algorithm, including improved BA (IBA) and Hybrid Bat–Swarm Algorithm (HB‐SA) (Table 11). Zarei et al (2019) implemented the PSO, BA and Hybrid Algorithm (HA) to a multi‐purpose reservoir. The HA obtained volumetric reliabilities equals 0.92, 0.89, 0.79 and 0.75 for urban, environmental, agricultural and industrial demands, respectively, similar to those of the BA and PSO.…”

Section: Algorithmsmentioning

confidence: 99%

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A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).

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Section: Algorithmsmentioning

confidence: 99%

“…In addition to the conventional BA many researchers have employed the improved versions of this algorithm, including improved BA (IBA) and Hybrid Bat-Swarm Algorithm (HB-SA) (Table 11). Zarei et al (2019) implemented the PSO, BA and Hybrid Algorithm (HA) to a multi-purpose reservoir.…”

Section: The Bat Algorithm (Ba)mentioning

confidence: 99%

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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“…This procedure maps input to output variables using fuzzy set theory and fuzzy logic (Mamdani, 1974;Zadeh, 1965). A fuzzy rule-based system consists of a set of logical rules expressed using IF-THEN statements (fuzzy rules), using fuzzy numbers and fuzzy operators (Sen, 2010;Shrestha, Duckstein, & Stakhiv, 1996).…”

Section: Fuzzy Rule-based Systemsmentioning

confidence: 99%

Inferring efficient operating rules in multireservoir water resource systems: A review

Macian-Sorribes

Pulido-Velázquez

2019

WIREs Water

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Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water‐stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation‐optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization–simulation–optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > Methods

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“…Among all renewable energy sources, hydropower is the most commonly used for electricity production, with the corresponding global capacity having increased by more than 30% between 2007 and 2015 [1,2]. In the past three decades, reservoirs and hydropower stations have been rapidly developed to provide clean energy and reduce greenhouse gas emissions in China [3,4].…”

Section: Introductionmentioning

confidence: 99%

Development of an Optimal Model for the Xiluodu-Xiangjiaba Cascade Reservoir System Considering the Downstream Environmental Flow

Dai

et al. 2020

Sustainability

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To explore the influence of the Xiluodu-Xiangjiaba cascade reservoir system on the appropriate environmental flow (AEF) of the Jinsha River, a multiobjective optimal cascade reservoir model was established with the aim of maximizing power generation while minimizing the downstream degree of AEF alteration. The AEF was determined using the range of variability approach (RVA). The optimal model was solved using an improved version of NSGA-II called INSGA2-DS. Inflows in typical normal and dry years were selected for optimization. The results show that in a normal year, power generation can be increased by 1.28% compared with that under the current regular operation conditions by prioritizing the maximization of power generation, in which case the degree of AEF alteration will increase by 13.86%. In contrast, the degree of AEF alteration will decrease by 22.53% if ecological protection is prioritized, but power generation will decrease by 0.62%. Similarly, in a dry year, power generation can be increased by 1.76% compared with that under the current regular operation conditions to maximize economic benefit, in which case, the degree of AEF alteration will increase by 4.95%. By contrast, the degree of AEF alteration can be decreased by 13.70% if the objective is AEF minimization, but power generation will decrease by 0.48%. These research results provide useful information for the formulation of ecological operation schemes involving cascade reservoirs on the Jinsha River.

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Optimal Reservoir Operation Using Bat and Particle Swarm Algorithm and Game Theory Based on Optimal Water Allocation among Consumers

Cited by 41 publications

References 32 publications

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Inferring efficient operating rules in multireservoir water resource systems: A review

Development of an Optimal Model for the Xiluodu-Xiangjiaba Cascade Reservoir System Considering the Downstream Environmental Flow

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