Static planning of the expansion of electrical energy transmission systems using particle swarm optimization

Mendonça, Isabela Miranda de; Silva, Ivo Chaves da; Marcato, André L. M.

doi:10.1016/j.ijepes.2014.02.028

Cited by 41 publications

(27 citation statements)

References 27 publications

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“…The swarm is successful over time because the position of each particle is updated, taking into account the best position of the particle in the past generations and the best position of all particles in the swarm. The velocity of each particle is given by (10) and the position is obtained by (11 …”

Section: Particle Swarm Optimization -Psomentioning

confidence: 99%

“…Larger values of the weighting function facilitate a global search while smaller values tend to represent a local one. Results provided in the literature mention that it is better to adjust the weighting function in a larger value at the beginning of the search process, promoting a more comprehensive search, and gradually, throughout the process, reduce it to refine the search [11]. Therefore, in the developed approach we used for w values of 0.9 ( max w ) in the beginning of the process and of 0.4 ( min w ) in the end of the process in accordance with (12).…”

Section: Particle Swarm Optimization -Psomentioning

confidence: 99%

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Static transmission expansion planning using Heuristic and metaheuristic techniques

Gomes

Saraiva

2015

2015 IEEE Eindhoven PowerTech

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-This paper describes a hybrid tool to perform Static Transmission Expansion Planning, STEP, studies and its application to the Garver6-Bus academic system and to the Southern Brazilian Transmission equivalent real system. The developed STEP tool integrates two phases as follows. The first one uses Constructive Heuristic Algorithms (CHA) to reduce the search space, and the second uses Particle Swarm Optimization (PSO) to identify the final solution. This hybridization between CHAs and PSO proved to be very effective and shows good performance to reduce the size of the STEP search space and to identify good quality solutions. These are relevant issues given the combinatorial nature of investment problems leading to the explosion of the number of alternative plans, one of the greatest difficulties faced in this planning problem.

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Section: Particle Swarm Optimization -Psomentioning

confidence: 99%

Section: Particle Swarm Optimization -Psomentioning

confidence: 99%

Static transmission expansion planning using Heuristic and metaheuristic techniques

Gomes

Saraiva

2015

2015 IEEE Eindhoven PowerTech

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“…In the long‐term planning of electric power systems, generation expansion planning (GEP) and transmission expansion planning (TEP) play vital roles to achieve a reliable and secure operation of the system. The aim beyond implementing such planning in the traditional power system with regulated structure was to specify the location, number, and the timing of new generation units along with new transmission lines to satisfy the future peak load demand added to the electric power system . In traditional power systems, the planning is affected by the increasing load demand considered as the only factor putting stress on the transmission assets, while in modern power systems, the integration of renewable energy sources can also cause such a stress.…”

Section: Introductionmentioning

confidence: 99%

Intelligent particle swarm optimization augmented with chaotic searching technique to integrate distant energy resources

Javadi

Nezhad

2017

Int Trans Electr Energ Syst

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Summary This paper proposes a long‐term framework for generation expansion and transmission expansion planning taking into account the renewable energy integration. To solve the problem, a hybrid technique is used. The mechanism of this technique is based on decomposing the original problem into master and slave subproblems where the master subproblem is solved using a heuristic optimization algorithm and slave subproblems are solved using general algebraic modeling system, which is a well‐known software with powerful mathematical solvers. The proposed heuristic algorithm is a combination of the intelligent particle swarm optimization and chaotic searching technique. Finally, the proposed model is simulated using 3 case studies including 6‐bus Garver test system, IEEE 24‐bus, and modified IEEE 118‐bus test systems to validate the effectiveness of the long‐term planning framework while the simulation results are compared to those obtained from classic genetic algorithm (GA‐Classic) and classic particle swarm optimization (PSO‐Classic) to verify the efficiency of the technique used in this paper.

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“…In traditional manner, the objective of TEP is to determine the location, number, and timing of new transmission assets to meet estimated future peak demand [3]. In traditional planning studies, the increasing load demand was only one factor that increases the stress on transmission lines/transformers.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective transmission expansion planning considering minimization of curtailed wind energy

Ugranlı

Karatepe

2015

International Journal of Electrical Power & Energy Systems

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Static planning of the expansion of electrical energy transmission systems using particle swarm optimization

Cited by 41 publications

References 27 publications

Static transmission expansion planning using Heuristic and metaheuristic techniques

Static transmission expansion planning using Heuristic and metaheuristic techniques

Intelligent particle swarm optimization augmented with chaotic searching technique to integrate distant energy resources

Multi-objective transmission expansion planning considering minimization of curtailed wind energy

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