Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis

Malekmohammadi, Bahram; Zahraie, Banafsheh; Kerachian, Reza

doi:10.1016/j.eswa.2010.12.119

Cited by 81 publications

(41 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

show abstract

“…NSGA-II algorithm has been used to solve the optimization problem. Malekmohammadi et al [13] optimized series reservoir with short-term objective of ood control and long-term objective of supplying series of river-reservoir by NSGA-II algorithm. For ranking the model results, ELECTRE-TRI is used.…”

Section: Introductionmentioning

confidence: 99%

The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

Hojjati

Neyshabouri

2017

Scientia Iranica

View full text Add to dashboard Cite

Abstract. Ski jump is one of the most e ective structures in energy dissipation over spillways. Spillways have long been of practical importance to safety of dams. The major criteria in hydraulic design are based on the analytical and empirical methods. In the current study, in order to increase chute spillway e ciency, a multi-objective evolutionary algorithm known as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been utilized to design the optimal triangular bucket angle and chute width. In design method, two separate objective functions have been used. In the rst objective function, equations of dynamic pressure of the bucket, the jet length after bucket, and the scour depth have been used. The second objective function is related to construction volume of chute spillway. For calibrating the rst objective function, characteristics of Karoon III dam have been used as a case study. The di erence between design parameters of Karoon III spillway and those from NSGA-II algorithm method is less than 12 percent. According to the results, if the jet length is considered as the most impressive parameter in the rst objective function, design of the spillway becomes frugal.

show abstract

“…They adopted the maximum curvature criterion to select one solution from among the nondominated solutions, because the Pareto front they obtained was convex and continuous. Malekmohammadi et al (2011) proposed an optimisation model for a multipurpose reservoir system, explicitly including the expected flood damage in the objective function formulation. They also implemented a method for outranking the Pareto solutions, which requires definition of many parameters and incorporating the preferences of DMs to select a particular solution.…”

Section: Introductionmentioning

confidence: 99%

“…On occasions, calibration of the parameters of the RFCM is performed with the aim of obtaining operating policies similar to the recorded ones (Ginn and Houk, 1989;USACE, 1999), which may be far away from optimal operation rules. Some authors tune the parameters of a RFCM to obtain the optimal performance in a few selected events (Wei and Hsu, 2008;Malekmohammadi et al, 2011;Ngo et al, 2007). Koutsoyiannis et al (2002) applied a parameterisation-simulation-optimisation approach to two reservoir systems with the purpose of supplying water for different uses.…”

Section: Introductionmentioning

confidence: 99%

Risk-based methodology for parameter calibration of a reservoir flood control model

Bianucci

Sordo‐Ward

Pérez

et al. 2013

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Flash floods are of major relevance in natural disaster management in the Mediterranean region. In many cases, the damaging effects of flash floods can be mitigated by adequate management of flood control reservoirs. This requires the development of suitable models for optimal operation of reservoirs. A probabilistic methodology for calibrating the parameters of a reservoir flood control model (RFCM) that takes into account the stochastic variability of flood events is presented. This study addresses the crucial problem of operating reservoirs during flood events, considering downstream river damages and dam failure risk as conflicting operation criteria. These two criteria are aggregated into a single objective of total expected damages from both the maximum released flows and stored volumes (overall risk index). For each selected parameter set the RFCM is run under a wide range of hydrologic loads (determined through Monte Carlo simulation). The optimal parameter set is obtained through the overall risk index (balanced solution) and then compared with other solutions of the Pareto front. The proposed methodology is implemented at three different reservoirs in the southeast of Spain. The results obtained show that the balanced solution offers a good compromise between the two main objectives of reservoir flood control management

show abstract

Ranking solutions of multi-objective reservoir operation optimization models using multi-criteria decision analysis

Cited by 81 publications

References 31 publications

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

Risk-based methodology for parameter calibration of a reservoir flood control model

Contact Info

Product

Resources

About