Chaotic particle swarm optimization for economic dispatch considering the generator constraints

Cai, Jiejin; Ma, Xiaoqian; Li, Lixiang; Peng, Haipeng

doi:10.1016/j.enconman.2006.05.020

Cited by 243 publications

(88 citation statements)

References 22 publications

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“…The convergence curves make clear that the results converged from larger values guarantee that the proposed WEO algorithm is efficient and obtain better results than earlier reported techniques. [10] 1,051,163 NA NA 0.421 EP [10] 1,048,638 NA NA 15.049 HS [12] 1,046,726 NA NA NA DE [11] 1,036,756 1,040,586 1,452,558 0.20 GA [7] 1,033,481 1,038,014 1,042,606 NA SOA [15] 1,023,946 1,026,289 1,029,213 NA AIS [9] 1,021,980 1,023,156 1,024,973 25.346 ABC [13] 1,021,576 1,022,686 1,024,316 2.603 TLA [16] 1,019,925 1,020,411 1,021,118 0.049 ICA [14] 1,018,467 1,019,291 1,021,796 NA HDE [29] 1,031,077 NA NA NA IPSO [17] 1 …”

Section: Test Systemmentioning

confidence: 99%

“…To overcome this deficiency, turn to various heuristic techniques such as Genetic Algorithm (GA) [7], Simulated Annealing (SA) [8], Artificial Immune System (AIS) [9], Evolutionary Programming (EP) [10], Differential Evolution (DE) [11], Harmony Search (HS) [12], Artificial Bee Colony (ABC) [13], Imperialist Competitive Algorithm (ICA) [14], Seeker Optimization Algorithm (SOA) [15], Teaching Learning Algorithm (TLA) [16], Improved Particle Swarm Optimization (IPSO) [17], Chaotic Differential Evolution (IDE) [18], Modified Teaching Learning Algorithm (MTLA) [19], Self-Adaptive Modified Firefly Algorithm (SAMFO) [20], Improve Pattern Search (IPS) [21], Enhanced Cross Entropy (ECE) [25], Adaptive Particle Swarm Optimization (APSO) [28], Enhanced Bee Swarm Optimization (EBSO) [35], Deterministic Guided Particle Swarm Optimization (DGPSO) [37]. The main drawback of these heuristic techniques gives the results but struck the local minima and lack of guarantee of convergence infinite time for large scale DED problems.…”

Section: Introductionmentioning

confidence: 99%

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Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Venkadesh¹

2017

IOSR JEEE

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Section: Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Venkadesh¹

2017

IOSR JEEE

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“…So far, the various known untainted stochastic approaches such as the Genetic Algorithm [7] (GA), Differential Evolution Algorithm [8] (DEA), Particle Swam Optimization [9] (PSO), Artificial Immune System [10] (AIS), Artificial Bee Colony Optimization [11] (ABCO), Ant Colony Optimization [12] (ACO) and Bacterial Foraging Optimization [13] (BFO) have been successfully employed to solve the PDED. However, these heuristic methods suffer in exploring the nearer optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Scheduling practical generating system using an improved bacterial swarm optimization

Raviprabakaran

Ravichandran

2016

Teh. vjesn.

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Original scientific paper This paper offers an improved technique namely, Quorum sensing based Bacterial Swarm Optimization (QBSO) technique to solve Practical Dynamic Economic Dispatch (PDED) problem. Quorum sensing is the communication signalling mechanism that permits the bacteria to organize the collective behaviour. The enhanced QBSO method improves the exploration capability of bacterial swarm. The practicability of the proposed method is tested on standard 10 generating unit system and the public practical south Indian 20 thermal generating system with recorded load demand profiles. The enhanced technique increases the operational speed of scheduling and reduces the generating cost of standard system and the thermal generating units owned by Tamilnadu Generation and Distribution Corporation Limited (TANGEDCO). The presented technique outperforms when compared with conventional bacterial swarm optimization technique and some recently published methods. Keywords: Dynamic Economic Dispatch; Practical Load Demand Profiles; Quorum Sensing based Bacterial Swarm Optimization; 20 South Indian Thermal UnitsProgramiranje praktičnog sustava generiranja uporabom poboljšane optimizacije bakterijskog roja Izvorni znanstveni članak Ovaj rad nudi poboljšanu metodu, naime, Quorum sensingbased Bacterial Swarm Optimization (QBSO) metodu za rješavanje problema praktične dinamične ekonomične isporuke -Practical Dynamic Economic Dispatch (PDED). Prepoznavanje kvoruma je mehanizam za signaliziranje komunikacije koji omogućuje organiziranje kolektivnog ponašanja bakterija. Poboljšana QBSO metoda povećava istraživačku sposobnost roja bakterija. Praktičnost predložene metode ispitana je na standardnomsustavu od 10 jedinicageneriranja energije i javnom praktičnom južno indijskom sustavu od 20 jedinica za generiranjetoplinske energije sa snimljenim profilima potrebnogopterećenja. Ta poboljšana metoda povećava operativnu brzinu programiranjaoperacija i smanjuje troškove generiranja standardnog sustava i jedinicaza dobivanje toplinske energije u vlasništvu Tamilnadu Generation and Distribution Corporation Limited (TANGEDCO). Prikazana metodaje bolja u usporedbi s konvencionalnom metodom optimizacije roja bakterija inekim nedavno objavljenim metodama.Ključne riječi: dinamična ekonomična isporuka; 20 South Indian Thermal Units; optimizacija roja bakterija temeljena naprepoznavanju kvoruma; praktični profili potreba opterećenja

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“…GA has an advantage of using a chromosome coding technique concerned to the defined problem and the two basic disadvantages are very long execution time and the global optimum solution has no guarantee of convergence. Nonconvex problems are solved also using PSO and many of its variants (Selvakumar, 2007;Thanushkodi, 2008;Gaing, 2003;Cai, 2007) There are many advantages of PSO such as easy performance and minimum adjustable parameters. It is also very efficient in global search (exploration).…”

Section: Introductionmentioning

confidence: 99%

Solving unit commitment and economic load dispatch problems using modern optimization algorithms

Hassan¹,

Ezzat²,

Abdelaziz³

2017

Int. J. Eng. Sci. Tech

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Economic Load Dispatch (ELD) and Unit Commitment (UC) are very important applications to predict the optimized cost of load in a power system. UC determines working states for existing generating units under some operational constraints and then optimizing the operation cost for all running units w.r.t. load demand using economic dispatch. This paper introduces Genetic Algorithm (GA) or Dynamic Programming (DP) to solve UC and then Shuffled BAT (BAT) technique as an evolutionary based approach is presented to solve the constrained ELD problem of thermal plants depending on the results obtained from UC solution. The IEEE 30 bus system is used to test the demonstration of the solution quality, computation efficiency and the feasibility of the application of BAT algorithm for ELD problem.

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Chaotic particle swarm optimization for economic dispatch considering the generator constraints

Cited by 243 publications

References 22 publications

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Water Evaporation Optimization Algorithm for Solving Dynamic Economic Dispatch

Scheduling practical generating system using an improved bacterial swarm optimization

Solving unit commitment and economic load dispatch problems using modern optimization algorithms

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