Dynamic transmission planning using a constrained genetic algorithm

Youssef, Hala

doi:10.1016/s0142-0615(01)00003-5

Cited by 24 publications

(4 citation statements)

References 26 publications

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“…The improvement of computing technology with increasingly faster processors along with the option of solving the problem in a distributed computing environment has made possible to handle a bigger number of parameters and variables and even formulate the TEP as a multi-period optimization problem (Youssef, 2001;Braga & Saraiva, 2005). However, jointly with the above mentioned increasing competition brought by the deregulation, relevant aspects such as: the development of new small-scale generation technologies (Distributed Generation, DG), the improvement of power electronic devices (e.g.…”

Section: The Emerging New Tep Problemmentioning

confidence: 99%

Flexibility Value of Distributed Generation in Transmission Expansion Planning

Vasquez¹,

Olsina²

2010

Distributed Generation

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Section: The Emerging New Tep Problemmentioning

confidence: 99%

Flexibility Value of Distributed Generation in Transmission Expansion Planning

Vasquez¹,

Olsina²

2010

Distributed Generation

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“…The limitations of the classical approaches have attracted the application of different types of heuristic optimization techniques, which basically are designed to handle non-convex problems, since they have mechanisms to escape the local optimum [9], providing in addition a better computation performance as compared to conventional optimization techniques, at the expense of lower accuracy [7]. Recently developed approaches include genetic algorithms (GA) [10], simulated annealing [11], evolutionary particle swarm optimization (EPSO) [12], as well as hybrid techniques (i.e. joint solution of static and dynamic CTEP by using a combination of meta-heuristics and classical dynamic programming) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of heuristic optimization methods for optimal dynamic transmission expansion planning

Gavela

Rueda

Vargas

et al. 2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY Modern heuristic optimization algorithms have gained considerable attention in different research fields due to their conceptual simplicity and their ability to deal with different complex optimization problems. Motivated by promising results from the application of these emerging tools on other power system optimization problems, this paper presents a comparative study on the capabilities of three heuristic optimization algorithms for solving the problem of optimal dynamic centralized transmission expansion planning (CTEP). Among the studied algorithms are: Evolutionary particle swarm optimization, differential evolution, and a newly developed tool named mean–variance mapping optimization. Considering a predefined indicative generation expansion plan, which is usually contemplated in regulations for the power system planning in most Latin American countries, modified versions of the well‐known Garver's system and the IEEE‐118 bus multi‐area system are used to evaluate the performance of each algorithm for solving the dynamic CTEP with a medium‐term planning horizon of 10 years. Copyright © 2013 John Wiley & Sons, Ltd.

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“…In most cases, expansion costs and network losses are in conflict with each other, so the component losses in TNEP would be a multi-criteria decision making (MCDM) function. Accordingly, the network planner can construct the lines with lower losses and higher cost or construct the lines with lower costs but higher losses (Shayeghi et al, 2008;Youseef, 2001). Another factor affecting TNEP is uncertainty over Garver's famous offering of innovative ideas in 1970, at the same time as oil-crisis planners were being considered.…”

Section: Introductionmentioning

confidence: 99%

Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

Ameli¹,

Shivaie²,

Moslehpour³

2012

IJIEC

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Transmission Network Expansion Planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI) tools such as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANNs) are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs) and Harmony Search Algorithm (HSA) was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver's 6-bus network.

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Dynamic transmission planning using a constrained genetic algorithm

Cited by 24 publications

References 26 publications

Flexibility Value of Distributed Generation in Transmission Expansion Planning

Flexibility Value of Distributed Generation in Transmission Expansion Planning

Performance comparison of heuristic optimization methods for optimal dynamic transmission expansion planning

Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

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