Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

Spiliotis, Mike; Mediero, Luis; Garrote, Luís

doi:10.1007/s11269-016-1285-y

Cited by 46 publications

(25 citation statements)

References 30 publications

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“…Ant colony optimization is based on how ants find the shortest paths to food, being suited to deal efficiently with discrete variables, and with a low dependence between the problem size (variables and constraints) and the quality of optimal solution (e.g., Kumar & Reddy, 2006;Safavi & Enteshari, 2016). Particle swarm optimization is inspired by natural grouping behaviors (e.g., Kumar & Reddy, 2007;Ostadrahimi, Mariño, & Afshar, 2012;Spiliotis, Mediero, & Garrote, 2016;Taormina, Chau, & Sivakumar, 2015). It can handle nonlinearities and nonconvexities, although it can be trapped by local optima (Kumar & Reddy, 2007;Spiliotis et al, 2016).…”

Section: Heuristic Optimizationmentioning

confidence: 99%

“…Particle swarm optimization is inspired by natural grouping behaviors (e.g., Kumar & Reddy, 2007;Ostadrahimi, Mariño, & Afshar, 2012;Spiliotis, Mediero, & Garrote, 2016;Taormina, Chau, & Sivakumar, 2015). It can handle nonlinearities and nonconvexities, although it can be trapped by local optima (Kumar & Reddy, 2007;Spiliotis et al, 2016). Honey bees mating reproduces honey bees behavior, and can solve highly nonlinear constrained and unconstrained optimization problems with discrete and/or continuous variables (e.g., Haddad, Afshar, & Mariño, 2006).…”

Section: Heuristic Optimizationmentioning

confidence: 99%

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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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Section: Heuristic Optimizationmentioning

confidence: 99%

Section: Heuristic Optimizationmentioning

confidence: 99%

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

Macian-Sorribes

Pulido-Velázquez

2019

WIREs Water

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“…The essence of hedging rules is to save water by frequent small water shortages so as to reduce the risk of severe water shortages in the later period [6]. Hedging rules limit water supply when supply availability is small, the point at which water supply is limited is called the hedging interval [7,8]. The two ends of the hedging interval are called SWA (starting water availability) and EWA (ending water availability) [9], and the ends with larger supply capacity are SWA and EWA [10].…”

Section: Introductionmentioning

confidence: 99%

Research on Hedging Rules Based on Water Supply Priority and Benefit Loss of Water Shortage—A Case Study of Tianjin, China

Men

Liu

et al. 2019

Water

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When a city’s water demand cannot be fully satisfied, the hedging rule can reduce water loss by limiting water supply in advance. Based on water supply priority and benefit loss of water shortage for different users, this paper improved the objective function of hedging rules considering the benefit loss of water shortage. At the same time, according to the idea of restricting water supply by water users in turn, improved hedging rules (IHR) are applied to the urban water supply in Tianjin. The conclusions achieved from this study are as follows: (1) IHR increased water supply assurance rates for domestic water with high-priority and avoided destructive water shortages in agricultural water with low-priority. (2) IHR can better reduce the destructive loss caused by a large number of water shortages and the loss of production caused by a small numbers of water shortages than traditional hedging rules, which ensures high efficiency of water supply during the dry period. The results show that the IHR can improve the operational performance of the urban water supply.

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“…They have used gridded precipitation forecast from a climate model to obtain reservoir inflow forecasting. Spiliotis et al [23] adopted particle-swarm-optimization algorithm to derived optimal drought hedging rules that are based on appropriate identification of activation thresholds and rationing factors. The use of predefined activation functions reduces the number of parameters to be adopted in the optimization.…”

Section: Introductionmentioning

confidence: 99%

Season-Dependent Hedging Policies for Reservoir Operation—A Comparison Study

Bhatia

Srivastav

Srinivasan

2018

Water

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During periods of significant water shortage or when drought is impending, it is customary to implement some kind of water supply reduction measures with a view to prevent the occurrence of severe shortages (vulnerability) in the near future. In the case of operation of a water supply reservoir, this reduction of water supply is affected by hedging schemes or hedging policies. This research work aims to compare the popular hedging policies: (i) linear two-point hedging; (ii) modified two-point hedging; and, (iii) discrete hedging based on time-varying and constant hedging parameters. A parameterization-simulation-optimization (PSO) framework is employed for the selection of the parameters of the compromising hedging policies. The multi-objective evolutionary search-based technique (Non-dominated Sorting based Genetic Algorithm-II) was used to identify the Pareto-optimal front of hedging policies that seek to obtain the trade-off between shortage ratio and vulnerability. The case example used for illustration is the Hemavathy reservoir in Karnataka, India. It is observed that the Pareto-optimal front that was obtained from time-varying hedging policies show significant improvement in reservoir performance when compared to constant hedging policies. The variation in the monthly parameters of the time-variant hedging policies shows a strong correlation with monthly inflows and available water.

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Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

Cited by 46 publications

References 30 publications

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

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

Research on Hedging Rules Based on Water Supply Priority and Benefit Loss of Water Shortage—A Case Study of Tianjin, China

Season-Dependent Hedging Policies for Reservoir Operation—A Comparison Study

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