Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

Kumari, M. Sailaja; Sydulu, M.

doi:10.1016/j.ijepes.2010.01.010

Cited by 244 publications

(106 citation statements)

References 8 publications

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“…Various stochastic optimization techniques have been suggested and utilized to deal with OPF problems, like the genetic algorithm [11][12][13], particle swarm optimization (PSO) [2], differential evolution (DE) [14], harmony search (HS) algorithm [15], artificial bee colony algorithm [4,16], gravitational search algorithm (GSA) [17], distributed algorithm (DA) [18], and biogeography-based optimization (BBO) [19,20]. A survey of the non-deterministic search (stochastic search) algorithms utilized to solve variants of OPF is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

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Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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“…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%

“…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. In fact, Decoupled Quadratic Load Flow has been used in [18] for dealing with OPF problem while IGA has acted as an optimization tool for searching optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

“…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. In fact, Decoupled Quadratic Load Flow has been used in [18] for dealing with OPF problem while IGA has acted as an optimization tool for searching optimal solutions. Hybrid IGA has been applied in both [19,20] while incremental power flow model has been employed in [19], but boundary method has been used in [20].…”

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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“…Optimal power flow (OPF) is one of the most important MO problems in power system. The main goal of OPF is to find the optimal adjustments of the control variables to minimize the selected objective function while satisfying various physical and operational constraints imposed by equipment and network limitations (Kumari and Maheswarapu, 2010). Since the real power generation levels and voltage magnitudes are continuous variables whereas the transformer winding ratios and shunt capacitors are discrete variables, the OPF problem is considered as a non-linear multi-modal optimization problem with a combination of the discrete and continuous variables (Abou El Ela et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

[ARTICLE WITHDRAWN] Dynamic Population Artificial Bee Colony Algorithm for Multi-Objective Optimal Power Flow

Liu¹,

Liu²,

Ma³

et al. 2016

j comput theor nanosci

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This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP), which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF) problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII) and multi-objective ABC (MOABC), are presented to illustrate the effectiveness and robustness of the proposed method.

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Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

Cited by 244 publications

References 8 publications

Optimal Power Flow Using the Jaya Algorithm

Optimal Power Flow Using the Jaya Algorithm

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

[ARTICLE WITHDRAWN] Dynamic Population Artificial Bee Colony Algorithm for Multi-Objective Optimal Power Flow

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