scholarly journals Comparative Analysis of Constructive Heuristic Algorithms for Transmission Expansion Planning

2018 ◽  
Vol 2 (2) ◽  
pp. 55-64
Author(s):  
Phillipe Vilaça Gomes ◽  
João Tomé Saraiva
Keyword(s):  
Comparative Analysis ◽  
Heuristic Algorithms ◽  
Computational Effort ◽  
Local Optima ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Constructive Heuristic ◽  
Subsequent Step ◽  
Increasing Demand

Transmission Expansion Planning (TEP) is a complex optimization problem that has the purpose of determining how the transmission capacity of a network should be enlarged, satisfying the increasing demand. This problem has combinatorial nature and different alternative plans can be designed so that many algorithms can converge towards local optima. This feature drives the development of tools that combine high robustness and low computational effort. This paper presents a comparative analysis and a detailed review of the main Constructive Heuristic Algorithms (CHA) used in the TEP problem. This kind of tools combine low computational effort with reasonable quality solutions and can be associated with other tools to use in a subsequent step in order to improve the final solution. CHAs proved to be very effective and showed good performance as the test results will illustrate.

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

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Luis A. Gallego ◽  
Marcos J. Rider ◽  
Marina Lavorato ◽  
Antonio Paldilha-Feltrin
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Heuristic Algorithms ◽  
Initial Population ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Local Improvement ◽  
Linear Programming Problems

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.

scholarly journals Simulated Annealing with Gaussian Probability Density Function for Transmission Expansion Planning

2017 ◽  
Vol 1 (1) ◽  
pp. 104-113
Author(s):  
Phillipe Gomes
Keyword(s):  
Simulated Annealing ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Optimization Problems ◽  
Local Optima ◽  
Gaussian Probability Density Function ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima. This tool has the ability to escape from a local optima accepting relatively bad solutions for a period and searching for good solutions in your neighborhood. This paper describes the use of SA based on Gaussian Probability Density Function as a decision support criteria in resolution of Transmission Expansion Planning (TEP) problem. This method consists in starting from an initial solution with all possible circuits added and over the iterations removing, replacing or adding new circuits. The method proved to be a reasonable computational effort and proved able to find optimal values known in the literature.

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