Solving unit commitment and economic dispatch simultaneously considering generator constraints by using nested PSO

Farsadi, Murtaza; Hosseinnejad, Hadi; Dizaji, Tohid Sattarpour

doi:10.1109/eleco.2015.7394478

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…While the initial speed is a random value between the maximum speed (Vmax) and the minimum speed (Vmin) from the following formula [9]. The Pbest value is obtained by comparing the Pbest value in the previous iteration with the results from the calculation of the new position update (new Pbest).…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

Aliyah,

Utami,

Oktadini

2021

SJIA

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Palm oil is a reliable vegetable oil producer because the oil produced has advantages than oils from other plants. The amount of Fresh Fruit Bunches (FFB) raw material from Palm oil has a significant impact on the palm oil production process. Therefore, we need a method to forecasting the amount of palm oil (FFB). One of the suitable forecasting methods is fuzzy time series (FTS). However, FTS still has shortcomings such as innacurate determination of the interval length. For this reason, we need to optimize FTS interval to get optimal forecasting. This research implements Particle Swarm Optimization as the optimization method, FTS Chen-Hsu as the forecast method, and Mean Absolute Percentage Error (MAPE) as the measurement of error. The optimization result using PSO produce an error value of 2.0262% smaller than FTS 3.7108%.

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Section: Methodsmentioning

confidence: 99%

Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

Aliyah,

Utami,

Oktadini

2021

SJIA

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“…The proposed strategy for PSO control parameter (ω, c l , and c g ) determination is called the nested PSO (NESTPSO). The term nested PSO has been introduced in many researches [38][39][40][41][42][43][44] to handle certain functions. However, it has been used in this paper to determine the control parameters of PSO or any other metaheuristic techniques.…”

Section: Proposed Strategymentioning

confidence: 99%

A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

Eltamaly

2021

Sustainability

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This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.

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“…Many methods have been used to solve the unit commitment (UC) problem as integer programming [3], branch-and-bound methods [4], dynamic programming (DP) [5]- [8], mixed-integer programming [9], and lagrangian relaxation methods (LR) [10], [11], and priority list method [12]. Also, there are many optimization algorithms suggested to solve unit commitment as whale optimization-differential evolution and genetic algorithm (WODEGA) [13], on mixed-integer programming formulations for the unit commitment problem [14], solving unit commitment and economic load dispatch problems using modern optimization algorithms [15], sested particle swarm optimization (PSO) [16], dynamic formulation of the unit commitment…”

Section: Introductionmentioning

confidence: 99%

The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

Abbas

Anwer

2022

IJECE

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The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

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Solving unit commitment and economic dispatch simultaneously considering generator constraints by using nested PSO

Cited by 6 publications

References 16 publications

Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

Fuzzy Time Series Optimization using Particle Swarm Optimization for Forecasting the Number of Fresh Fruit Bunches (FBB) of Palm Oil

A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

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