Specialised branch-and-bound algorithm for transmission network expansion planning

Haffner, Sérgio; Monticelli, A.; Garcia, A.V.; Romero, Rubén

doi:10.1049/ip-gtd:20010502

Cited by 68 publications

(37 citation statements)

References 7 publications

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“…In [3][4][5] constructive heuristic algorithms have been used to solve the TNEP problem. Mathematical models based on classical optimization techniques, such the Benders' decomposition [6][7][8] and branch and bound methods [9,10], have also been used to solve the TNEP problem. Intelligent metaheuristic algorithms such as (1) simulated annealing, (2) tabu search, (3) genetic algorithms, and (4) harmony search algorithm have been proposed in [11][12][13][14], respectively, to solve the TNEP problem.…”

Section: The Transmission Network Expansion Planning Problemmentioning

confidence: 99%

“…Also, the metaheuristics are more robust in terms of processing time and find better solutions than the other classical optimization methods, such as the Benders decomposition and branch and bound methods. Thus, for example, while analyzing a huge and complex problem for the relaxed model called transportation, in [9], it was verified that the Benders decomposition algorithm presents great difficulty in convergence and the branch and bound algorithm finds quality solutions but does not converge due to the prohibitive processing time. However, the metaheuristics present the best solution for the dc model with acceptable processing time [13].…”

Section: The Transmission Network Expansion Planning Problemmentioning

confidence: 99%

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An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Pareja

Rider

Lavorato

et al. 2012

Journal of Electrical and Computer Engineering

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An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem.

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Section: The Transmission Network Expansion Planning Problemmentioning

confidence: 99%

Section: The Transmission Network Expansion Planning Problemmentioning

confidence: 99%

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Pareja

Rider

Lavorato

et al. 2012

Journal of Electrical and Computer Engineering

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“…The authors indicate that the algorithm is consistent, and intend to implement it to solve the DC model with security and multi-stage planning considerations. Other versions of B&B algorithm applied to transmission network expansion problem are presented in the papers of Haffner et al [33] and Choi et al [12].…”

Section: Branch and Bound Algorithmmentioning

confidence: 99%

Algorithms and Models for Transmission Expansion Planning

2011

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This chapter presents an overview of algorithms and optimization models used for solving Transmission Expansion Planning (TEP) problem. Being a very complex problem TEP attracts much attention from both researchers and practitioners. A great number of publications in the technical literature address this problem by providing various optimization models and applying different algorithms to solve the TEP problem. Besides literature review and brief classification of the proposed algorithms and models this survey covers examples for most of the methods.

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“…Classical optimization techniques, such as linear programming [1,2], bender decomposition [3][4][5], and branch and bound methods [6,7], are applied previously to solve the Transmission network expansion problem (TNEP) problem. Simulated annealing, tabu search, harmony search algorithm, and GA are metaheuristic techniques which have been applied to solve the problem of transmission planning [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Hybrid GA and PSO Approach for Transmission Expansion Planning

Sisodia

Kumar

Wadhwani

2015

Advances in Intelligent Systems and Computing

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Metaheuristic techniques are enormously being used nowadays for their applications in field like Power system and are being applied to many optimization problems of power system due to difficult approach of classical methods. Transmission expansion planning (TEP) is a challenging task to deal in power system. A new approach, hybrid genetic algorithm particle swarm optimization (HGAPSO) for solving TEP problem, is introduced to eliminate the drawback of GA and PSO. Problems of immature convergence in particle swarm optimization (PSO) and low convergence speed in genetic algorithm (GA) mitigate to the hybridization of both techniques. Proposed HGAPSO Algorithm is tested for three standard electric test systems for TEP problem in MATLAB tool. Experimental results found by HGAPSO are compared with GA and PSO Algorithm to test its performance for TEP problem.

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Specialised branch-and-bound algorithm for transmission network expansion planning

Cited by 68 publications

References 7 publications

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

Algorithms and Models for Transmission Expansion Planning

Hybrid GA and PSO Approach for Transmission Expansion Planning

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