Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem

Shayeghi, Hossein; Mahdavi, Meisam; Bagheri, Amir

doi:10.1016/j.enconman.2009.08.030

Cited by 58 publications

(29 citation statements)

References 27 publications

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“…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]. TNEP is done using PSO in [13,14]. Hybridized methods are quoted in [15][16][17].…”

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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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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“…Next step is finding the initial local best and global best . Changing the velocity of the particle ⃗ 1 and ⃗ 0 according to (2) to determine the velocity of the particle, ⃗ as in (1). After that generate the random variable in the range (0,1).…”

Section: Simulationmentioning

confidence: 99%

“…To avoid line overloading, net transmission capacity (NTC) has to meet reliability criteria while delivering electric power to the load. The construction of transmission line which needs a high budged becoming an issue in transmission expansion [1]. Transmission expansion planning (TEP) aims to minimize the line construction which directly relates to investment cost.…”

Section: Introductionmentioning

confidence: 99%

Novel binary PSO algorithm based optimization of transmission expansion planning considering power losses

Astuty

Haryono

2016

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract.Transmission expansion planning (TEP) is one of the issue that have to be faced caused by addition of large scale power generation into the existing power system. Optimization need to be conducted to get optimal solution technically and economically. Several mathematic methods have been applied to provide optimal allocation of new transmission line such us genetic algorithm, particle swarm optimization and tabu search. This paper proposed novel binary particle swarm optimization (NBPSO) to determine which transmission line should be added to the existing power system. There are two scenerios in this simulation. First, considering transmission power losses and the second is regardless transmission power losses. NBPSO method successfully obtain optimal solution in short computation time. Compare to the first scenario, the number of new line in second scenario which regardless power losses is less but produces high power losses that cause the cost becoming extremely expensive. IntroductionThe additional of high scale power generating capacity increases over the year to fulfill the electricity demand. Most of generating plants are located far from the load center. Transmission network is an important infrastructure that must be considered in generation expansion planning. To avoid line overloading, net transmission capacity (NTC) has to meet reliability criteria while delivering electric power to the load. The construction of transmission line which needs a high budged becoming an issue in transmission expansion [1]. Transmission expansion planning (TEP) aims to minimize the line construction which directly relates to investment cost. It determines which line must be added to existing power system by considering technical, economic and reliability aspect.There are a lot of mathematical methods that have been used to do TEP optimization. Genetic algorithm has been used in several researches in binary and decimal codification model [2]. Discrete PSO with decimal codification also has been applied in STEP optimization. The precision and convergence speed of discrete PSO are better and faster than genetic algorithm. In addition to discrete PSO, traditional PSO with real number for continuous model also have been used to find the optimum transmission line considering line loading [4]. The other methods such as discrete evolutionary particle swarm optimization, simulated annealing algorithm, tabu search, and hybrid algorithm successfully provide optimum solution [5]-[8]. Ordinal optimization and mixed integer linear programming also have been applied to solve TEP problem [9]- [11].Most of previous researches conducted TEP study to obtain optimal cost of transmission line investment regardless power losses in power system. In this research, the optimization of TEP considers investment cost, power losses and line loading transmission. Novel binary PSO algorithm is applied to solve TEP problem in this research.

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“…In the recent period, Lattore et al [13] [32][33][34], disjunctive mixed integer programming [35], branch and bound algorithm [36], implicit enumeration [37,38], Benders decomposition [12, 39,40], maximum flow [12], hierarchical decomposition [41], sensitivity analysis [42,43], genetic algorithm (GA) [44][45][46][47][48][49], object-oriented programming [50], game-theory [51][52][53][54], simulated annealing [55,56], expert systems [57,58], fuzzy set [49,59,60], greedy randomised adaptive search [61], non-convex optimisation [62], tabu search [63], ant-colony [64], data-mining [65], particle swarm optimisation (PSO) [66][67][68][69], harmony search [70,71], artificial neural network (ANN) [72], game theory [73], and robust optimisation techniques [74][75]…”

Section: Grid Developmentmentioning

confidence: 99%

Time‐horizons in the planning and operation of transmission networks: an overview

Khuntia

Tuinema

Rueda

et al. 2016

IET Generation, Transmission & Distribution

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In the planning and operation of power systems, actions are taken in different processes and time-horizons. The purpose of these actions is to secure a high reliability level. Although the three main processes (grid development, asset management, and system operation) are described in literature, there has been no explicit study on the time-horizons (long-term, mid-term, and short-term) and actual time-scale (decades, years, months, etc.) that these processes focus on. This study aims at making a review of the various activities performed by transmission system operators while reviewing the concept of each time-horizon and methodologies developed in literature. As decisions taken in different time-horizons can influence each other, the interactions and overlapping are discussed.

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Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem

Cited by 58 publications

References 27 publications

Hybrid GA and PSO Approach for Transmission Expansion Planning

Hybrid GA and PSO Approach for Transmission Expansion Planning

Novel binary PSO algorithm based optimization of transmission expansion planning considering power losses

Time‐horizons in the planning and operation of transmission networks: an overview

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