Transmission network expansion planning under an improved genetic algorithm

2000 ◽  
Vol 15 (3) ◽  
pp. 1168-1174 ◽  
Author(s):  
E.L. Da Silva ◽  
H.A. Gil ◽  
J.M. Areiza
Keyword(s):  
Genetic Algorithm ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning

Transmission Network Expansion Planning under Improved Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 1458-1461
Author(s):  
Hong Fan ◽  
Yi Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Linear Optimization ◽  
Large Integer ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

Transmission network expansion planning under an improved genetic algorithm [discussion and closure]

2001 ◽  
Vol 16 (4) ◽  
pp. 930-931 ◽  
Author(s):  
H. Rudnick ◽  
R. Palma ◽  
E.L. da Silva ◽  
H.A. Gil ◽  
J.M. Areiza
Keyword(s):  
Genetic Algorithm ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning

Transmission Network Expansion Planning Considering Optimal Allocation of Series Capacitive Compensation and Active Power Losses

2021 ◽  
Vol 12 (1) ◽  
pp. 388
Author(s):  
Dany H. Huanca ◽  
Luis A. Gallego ◽  
Jesús M. López-Lezama
Keyword(s):  
Genetic Algorithm ◽  
Test System ◽  
Active Power ◽  
Mixed Integer ◽  
Planning Problem ◽  
Transmission Network ◽  
Power Losses ◽  
Network Expansion ◽  
Expansion Planning ◽  
Active Power Losses

This paper presents a modeling and solution approach to the static and multistage transmission network expansion planning problem considering series capacitive compensation and active power losses. The transmission network expansion planning is formulated as a mixed integer nonlinear programming problem and solved through a highly efficient genetic algorithm. Furthermore, the Villasana Garver’s constructive heuristic algorithm is implemented to render the configurations of the genetic algorithm feasible. The installation of series capacitive compensation devices is carried out with the aim of modifying the reactance of the original circuit. The linearization of active power losses is done through piecewise linear functions. The proposed model was implemented in C++ language programming. To show the applicability and effectiveness of the proposed methodology several tests are performed on the 6-bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system, presenting costs reductions in their multi-stage expansion planning of 7.4%, 4.65% and 1.74%, respectively.

A Comparative Study of Metaheuristic Methods for Transmission Network Expansion Planning

2012 ◽  
pp. 319-339 ◽  
Author(s):  
Ashu R. Verma ◽  
P. K. Bijwe ◽  
B. Panigrahi
Keyword(s):  
Genetic Algorithm ◽  
Power Flow ◽  
Optimization Techniques ◽  
Transmission Network ◽  
Swarm Optimization ◽  
Network Expansion ◽  
Expansion Planning ◽  
Dc Power ◽  
Metaheuristic Methods ◽  
Discrete Nature

Transmission network expansion planning is a very complex and computationally demanding problem due to the discrete nature of the optimization variables. This complexity has increased even more in a restructured deregulated environment. In this regard, there is a need for development of more rigorous optimization techniques. This paper presents a comparative analysis of three metaheuristic algorithms known as Bacteria foraging (BF), Genetic algorithm (GA), and Particle swarm optimization (PSO) for transmission network expansion planning with and without security constraints. The DC power flow based model is used for analysis and results for IEEE 24 bus system are obtained with the above three metaheuristic drawing a comparison of their performance characteristic.

A Comparative Study of Metaheuristic Methods for Transmission Network Expansion Planning

2010 ◽  
Vol 1 (4) ◽  
pp. 71-91 ◽  
Author(s):  
Ashu R. Verma ◽  
P. K. Bijwe ◽  
B. Panigrahi
Keyword(s):  
Genetic Algorithm ◽  
Power Flow ◽  
Optimization Techniques ◽  
Transmission Network ◽  
Swarm Optimization ◽  
Network Expansion ◽  
Expansion Planning ◽  
Dc Power ◽  
Metaheuristic Methods ◽  
Discrete Nature

Transmission network expansion planning is a very complex and computationally demanding problem due to the discrete nature of the optimization variables. This complexity has increased even more in a restructured deregulated environment. In this regard, there is a need for development of more rigorous optimization techniques. This paper presents a comparative analysis of three metaheuristic algorithms known as Bacteria foraging (BF), Genetic algorithm (GA), and Particle swarm optimization (PSO) for transmission network expansion planning with and without security constraints. The DC power flow based model is used for analysis and results for IEEE 24 bus system are obtained with the above three metaheuristic drawing a comparison of their performance characteristic.

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