A preference aggregation model and application in AHP-group decision making

Yang, Taiyi; Yang, De Quan; Chao, Xiangrui

doi:10.1063/1.5033804

“…Grošelj et al (2015) utilized the SAT, fitting performance, GED, GMV, and WD indices to evaluate group AHP aggregation techniques in the construction engineering field. Yang et al (2018) implemented the GED and the GMV indices on some illustrative examples.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Several Aggregation Techniques for Deriving Analytic Network Process Weights

Elshaboury

¹

,

Attia

²

,

Marzouk

³

2020

Water Resour Manage

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Water pipelines are exposed to severe aging and deterioration challenges. The weights of importance of deterioration factors shall be estimated to predict the condition of underground water pipelines. Analytic network process (ANP) is one of the most common multiple criteria decision making techniques. The fuzzy set theory has been employed to tackle the vagueness and imprecision in human judgments. Accordingly, the overarching aim of this research is computing the weights of importance of these factors using fuzzy ANP (FANP). Chang's extent analysis method is one of the most frequently used algorithms in FANP. For group decision making, two aggregation approaches are considered, namely geometric mean (GEO) and minimum-maximum (MIN-MAX). The performance of the modified extent analysis method is evaluated against the original method using four evaluation measures. These measures are satisfactory index, group minimum violations, group Euclidean distance, and distance between weights. Most of the evaluation measures indicate that the modified extent analysis (MIN-MAX) method exhibits better results than other methods. A water distribution network in Shaker Al-Bahery, Egypt is used to demonstrate the application of the proposed model.

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“…Power systems are facing new issues in balancing supply and demand and sustaining normal frequency as the use of renewable energy supplies grows [1]. The general power system is a complicated electrical linkage which includes generating, transmission, and distribution networks, as well as loads that are scattered across the network across a vast geographic region [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

El-Gammal

¹

,

Ramlal

²

2021

EJENG

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This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

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Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

El-Gammal

¹

,

Ramlal

²

2021

EJERS

4

0

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This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

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A preference aggregation model and application in AHP-group decision making

Cited by 3 publications

References 17 publications

Comparison of Several Aggregation Techniques for Deriving Analytic Network Process Weights

Comparison of Several Aggregation Techniques for Deriving Analytic Network Process Weights

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

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