Optimal power flow using differential evolution algorithm

El‐Ela, Adel A. Abou; Abido, M. A.; Spea, S.R.

doi:10.1007/s00202-009-0116-z

Cited by 105 publications

(138 citation statements)

References 28 publications

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“…For the system, there are three sub-cases with three types of fuel cost function where sub-case 3.1 considers single fuel with quadratic form, sub-case 3.2 considers single fuel with nonconvex form and POZ constraints, and sub-case 3.3 considers multi-fuels with piecewise form. The data of the fuel cost functions for these sub-cases are taken from [20,22,41], respectively. The main data belonging to transmission lines of the systems is taken from [25,42].…”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…The key variables corresponding to the best fitness function yielded by the proposed method for case 3 are given in Appendix A. [18] 799.56 --6000 HIGA [19] 799.56 799.6497 0.0406 4560 HIGA-BM [20] 800.0435 800.122 0.0385 12,000 DE [21] 801.23 801.282 0.0663 -DE [22] 799.2891 --25,000 PSO [23] 800.41 ---EADPSO [24] 800.2276 800.2625 0.0303 12,500 BBOA [26] 799.1116 799.1985 -10,000 (15,000) ARCBBOA [27] 800.5159 800.6412 -10,000 TLBO [28] 800.7257 --25,000 ABCA [31] 800.6600 800.8715 --GWO [32] 799.5585 ---MELMA [33] 799.1821 ---MCBOA [34] 799.0353 --25,000 (45,000) MSA [35] 800 [22] 799.2891 --25,000 PSO [23] 800.41 ---EADPSO [24] 800.2276 800.2625 0.0303 12,500 BBOA [26] 799.1116 799.1985 -10,000 (15,000) ARCBBOA [27] 800.5159 800.6412 -10,000 TLBO [28] 800.7257 --25,000 ABCA [31] 800.6600 800.8715 --GWO [32] 799.5585 ---MELMA [33] 799.1821 ---MCBOA [34] 799.0353 --25,000 (45,000) MSA [35] 800 …”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…Most methods are the family of meta-heuristic algorithms in which conventional methods, modified methods, combination of two different methods, and hybrid methods have been developed widely. In fact, there have been a huge number of applied methods such as the integration of improved genetic algorithm and effective decoupled quadratic load flow (IGA-EDQLF) [18], hybrid IGA with incremental power flow model (HIGA) [19], HIGA with boundary method (HIGA-BM) [20], differential evolution [21,22], conventional PSO [23], Evolving ant direction particle swarm optimization (EADPSO) [24], PSO with Pseudo-Gradient and constriction factor (PG-CF-PSO) [25], Biogeography-based optimization algorithm (BBOAA) [26] and adaptive real-coded biogeography-based optimization algorithm (ARCBBOA) [27], teaching-learning-based optimization algorithm (TLBO) [28], improved TLBO (ITLBO) [29], gravitational search algorithm (GSA) [30], Artificial bee colony algorithm (ABCA) [31], Grey wolf optimizer (GWO) [32], modified electromagnetism-like mechanism algorithm (MELMA) [33], modified Colliding Bodies Optimization algorithm (MCBOA) [34], moth swarm algorithm (MSA) [35], improved imperialist competitive algorithm (IICA) [36], cuckoo optimization algorithm (COA) [37], Gaussian bare-bones imperialist competitive algorithm (GBBICA) [38], and mathematical programming algorithm (MPA) [39]. In [18][19][20], different variants of GA have been developed in which GA has been improved first and then combined with another method for handling constraints of OPF problem.…”

Section: Introductionmentioning

confidence: 99%

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Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

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Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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show abstract

“…The OPF problem was divided into two sub-problems, i.e, active power dispatch and reactive power dispatch were considered. A DE-based approach to solve the OPF problem was developed in [47]. In their formulation, different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement were examined.…”

Section: Differential Evolution Based Approachmentioning

confidence: 99%

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming

Wan

2014

J. Mod. Power Syst. Clean Energy

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Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

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“…Some examples algorithm metaheuristic widely used are: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Tabu Search (TS), Biogeography based Optimization (BBO), Simulated Annealing (SA), etc. [6,7,8,9,10,11,12,13]. Do a comparison between the genetic algorithm with ant colony optimization algorithm to solve a scheduling problem subjects, genetic algorithm is an evolutionary methods that solve problems using a random way.…”

Section: Introductionmentioning

confidence: 99%

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Ashari

Muslim

Alamsyah

2016

SJI

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Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.

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Optimal power flow using differential evolution algorithm

Cited by 105 publications

References 28 publications

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

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