An Initialization Procedure in Solving Optimal Power Flow by Genetic Algorithm

Todorovski, Mirko; Rajicic, D.

doi:10.1109/tpwrs.2006.873120

Cited by 95 publications

(47 citation statements)

References 33 publications

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“…In the 26-bus system, as can be seen, the optimal settings of control variables obtained by the proposed SCPSO method can still maintain the least possible deviation of bus voltage even when line L 2-7 faulted. In the IEEE 57-bus system, the same phenomenon was obtained by the proposed SCPSO method when line L [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] faulted. The results show that the optimal settings of control variables allow systems to be operated defensively.…”

Section: Discussionmentioning

confidence: 63%

“…Previous efforts in solving OPF problems have employed various optimization techniques, such as genetic algorithms (GA) [7][8][9][10][11], tabu search (TS) [12,13], evolutionary programming (EP) [14,15], differential evolution [14][15][16], and particle swarm optimization (PSO) [5,[17][18][19][20][21]. In particular, because of its simple concept, easy implementation, and quick convergence, PSO has by now gained much attention and has been widely employed in solving OPF problems [22][23][24][25][26][27].…”

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

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Contingency‐Constrained Optimal Power Flow Using Simplex‐Based Chaotic‐PSO Algorithm

Gaing

Lin

2011

Applied Computational Intelligence and Soft Computing

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This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems.

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Section: Discussionmentioning

confidence: 63%

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

Contingency‐Constrained Optimal Power Flow Using Simplex‐Based Chaotic‐PSO Algorithm

Gaing

Lin

2011

Applied Computational Intelligence and Soft Computing

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“…The ORPP problem is decomposed into and optimization modules, and the evolutionary algorithms optimize each module in an iterative manner to obtain the global solution. In 2006, Todorovski and Rajicic [25] presented a new method to solve the OPF problem by using genetic algorithm. It depends on the application of new initialization procedure, which utilizes voltage angles at generator-buses as control variables to achieve voltages at load-buses with less computation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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“…GA has been applied to many problems, including stability studies [20], load frequency control [21], reactive power compensation [22], V/Q/THD control [23], unit commitment [24], economic dispatch [25], optimal power flow [26], generation expansion planning [27], optimal location of FACTS devices [10], optimized system topology [28], loss minimization [29], distributed generator placement [30], and reliability assessment [31].…”

Section: Development Of the Proposed Real Code-based Gamentioning

confidence: 99%

Maximizing the Overall Satisfaction Degree of all Participants in the Market Using Real Code-based Genetic Algorithm by Optimally Locating and Sizing the Thyristor-Controlled Series Capacitor

Nabavi¹,

Hajforoosh²,

Hajforoosh³

et al. 2011

Journal of Electrical Engineering and Technology

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-The present paper presents a genetic algorithm (GA) to maximize social welfare and perform congestion management by optimally placing and sizing one Thyristor-Controlled Series Capacitor (TCSC) device in a double-sided auction market. Simulation results, with line flow constraints before and after the compensation, are compared through the Sequential Quadratic Programming SQP method, and are used to analyze the effect of TCSC on the congestion levels of modified IEEE 14-bus and 30-bus test systems. Quadratic, smooth and nonsmooth (with sine components due to valve point loading effect) generator cost curves, and quadratic smooth consumer benefit functions are considered. The main aims of the present study are the inclusion of customer benefit in the social welfare maximization and congestion management objective function, the consideration of nonsmooth generator characteristics, and the optimal locating and sizing of the TCSC using real code-based GA to guarantee fast convergence to the best solution.

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An Initialization Procedure in Solving Optimal Power Flow by Genetic Algorithm

Cited by 95 publications

References 33 publications

Contingency‐Constrained Optimal Power Flow Using Simplex‐Based Chaotic‐PSO Algorithm

Contingency‐Constrained Optimal Power Flow Using Simplex‐Based Chaotic‐PSO Algorithm

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Maximizing the Overall Satisfaction Degree of all Participants in the Market Using Real Code-based Genetic Algorithm by Optimally Locating and Sizing the Thyristor-Controlled Series Capacitor

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