Economic dispatch solution using a genetic algorithm based on arithmetic crossover

Yalçınöz, Tankut; Altun, Halis; Uzam, Murat

doi:10.1109/ptc.2001.964734

Cited by 68 publications

(37 citation statements)

References 11 publications

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“…To assess the efficiency of PSO algorithms, they are compared to other four algorithms previously presented using the same data, available in [5]. It can be seen that both PSO Classical, and PSO Accelerated algorithm reach the same cost F as the algorithms presented in [3], [5], [6] and [7].…”

Section: Pso Parameters and Pso Convergencementioning

confidence: 99%

“…-may be overcome by applying the artificial intelligence techniques. The most common optimization techniques based upon artificial intelligence used for solving economic power dispatch problems are: the genetic algorithm [3][4][5][6], the Hopfield neural networks [2,7], the differential algorithm [8], the evolutionary programming [9,10], fuzzy-optimization [12,13], tabu search [14], particle swarm optimization [15,16,27,28]. Also, the EPD can be formulated as a multi-objective optimization problem [11,13,17].…”

Section: Introductionmentioning

confidence: 99%

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A Swarm Intelligence Approach to the Power Dispatch Problem

Secui

Felea

Dziţac

et al. 2010

INT J COMPUT COMMUN

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This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units.

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Section: Pso Parameters and Pso Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Approach to the Power Dispatch Problem

Secui

Felea

Dziţac

et al. 2010

INT J COMPUT COMMUN

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“…Crossover operators is responsible for global search as one of the properties of the genetic algorithm. Two chromosomes, randomly chosen for crossover can produce two offspring [3].…”

Section: Introductionmentioning

confidence: 99%

“…In connection with the problem of minimization, the most fitted individuals will have the lowest value of the objective function. Value of the fitness function is usually used to change the value of the objective function to measure the relative fitness [3].…”

Section: Introductionmentioning

confidence: 99%

Scheduling and Power Distribution System on Diesel Power Plant in North Sulawesi Using Genetic Algorithm

Sundah

2014

Jurnal Sistem Informasi

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Research regarding scheduling and power distribution system on diesel power plant in North Sulawesi using genetic algorithm has been done. This research was conducted to provide a new approach for scheduling and power distribution in North Sulawesi. Method of genetic algorithm is used in this study to address the problem of scheduling and distribution system of diesel power plant. Method of genetic algorithms is a heuristic search algorithm that mimics the mechanisms of biological system. Under this concept, the problem will be solved using the pattern of biological evolution. Solutions for solving problems will apply the method of selection, crossover, and mutation. Scheduling using genetic algorithm can optimize the scheduling done by by PT. PLN North Sulawesi, Minahasa System AP2B. Based on the results and the discussion it can be concluded that the genetic algorithm can be used as an efficient method for scheduling and power distribution system on the diesel power plants in North Sulawesi. Keywords: Genetic Algorithm, Scheduling, Power Distribution, Power Plant North Sulawesi. AbstrakPenelitian menyangkut penjadwalan dan distribusi daya pada pembangkitan listrik tenaga diesel di Sulawesi Utara dengan menggunakan algoritma genetik ini telah dilakukan untuk memberikan pendekatan baru terhadap permasalahan pembangkitan ini. Metode algoritma genetik adalah algoritma pencarian heuristic yang meniru mekanisme evolusi yang terjadi secara biologis. Melalui konsep ini, masalah yang akan dipecahkan didekati dengan pola evaluasi biologi. Solusi pemecahan masalah yang terbaik menerapkan metode seleksi, crossover, dan mutasi. Penjadwalan dengan menggunakan algoritma genetik dapat mengoptimasi penjadwalan yang dilakukan oleh oleh PT. PLN Sulawesi Utara, AP2B Sistem Minahasa. Berdasarkan hasil dan pembahasan ini maka dapat disimpulkan bahwa algoritma genetik dapat dijadikan metode yang efesien untuk penjadwalan pembangkit dan distribusi pembebanan pada sistem pembangkit listrik tenaga diesel di PLN Sulawesi Utara.

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“…In addition, an integrated parallel GA incorporating ideas form simulated annealing (SA) and Tabu search (TS) techniques was also proposed in [14] utilizing generator's output power as the encoded parameter. Yalcinoz has used a real-coded representation technique along with arithmetic genetic operators and elitistic selection to yield a quality solution [15]. GA has been deployed to solve ELD with various modifications over the years.…”

Section: Introductionmentioning

confidence: 99%

Economic Load Dispatch Using a Chemotactic Differential Evolution Algorithm

Biswas¹,

Dasgupta²,

Panigrahi³

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper presents a novel stochastic optimization approach to solve constrained economic load dispatch (ELD) problem using Hybrid Bacterial Foraging-Differential Evolution optimization algorithm. In this hybrid approach computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossover of the optimization agents. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.

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Economic dispatch solution using a genetic algorithm based on arithmetic crossover

Cited by 68 publications

References 11 publications

A Swarm Intelligence Approach to the Power Dispatch Problem

A Swarm Intelligence Approach to the Power Dispatch Problem

Scheduling and Power Distribution System on Diesel Power Plant in North Sulawesi Using Genetic Algorithm

Economic Load Dispatch Using a Chemotactic Differential Evolution Algorithm

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