Solving the Economic Dispatch Problem by Using Tabu Search Algorithm

Naama, Bakhta; Bouzeboudja, Hamid; Allali, Ahmed

doi:10.1016/j.egypro.2013.07.080

Cited by 38 publications

(14 citation statements)

References 4 publications

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“…To obtain the realistic and transparent EDP solution in this paper both MFO and VPLE model are considered. Mathematical modeling for this consideration is combination of both cost functions given in (6) and (8). Consider…”

Section: Edp Considering Both Vple and Mfomentioning

confidence: 99%

“…Since the operating cost of generating unit is inflated an optimal EDP can lead to prominent cost saving with reducing the atmospheric emission of thermal power plant [4]. In the energy crises age, a number of engineers and scientists are taking interest in the optimized solution of this industrial problem [5] by keeping in view the constraints like generator ramp limit, power balance, and valve point loading effect (VPLE) and prohibited operating zones [6,7]. However the facts of EDP with multiple fueling option (MFO) and MFO combined with VPLE conditions is not well addressed simultaneously, which are the motivations for taking this study with metaheuristic computing approach.…”

Section: Introductionmentioning

confidence: 99%

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Performance of Various Metaheuristic Techniques for Economic Dispatch Problem with Valve Point Loading Effects and Multiple Fueling Options

Ahmed

Rao

Shah

et al. 2014

Advances in Electrical Engineering

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The paper presents the metaheuristic approaches based on pattern search and simulated annealing hybridized with sequential quadratic programming, a powerful nonlinear adaptive technique for estimation of the finest combination of generated power in a given system at lowest operating cost while sustaining the operating condition of system efficiently. The fuel cost is minimized by satisfying the nonlinear operating conditions of thermal units mainly based on generation capacity constraints, generator ramp limit, power balance constraints, and valve point loading effect and by keeping in view the prohibited operating zones, respectively. About the optimization, a comparative study is made for pattern search (PS) and simulated annealing (SA), as a viable global search technique and sequential quadratic programming, an efficient local optimizer and their hybrid versions. The applicability, stability, and reliability of the designed approaches are validated through comprehensive statistical analysis based on Monte Carlo simulations.

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Section: Edp Considering Both Vple and Mfomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance of Various Metaheuristic Techniques for Economic Dispatch Problem with Valve Point Loading Effects and Multiple Fueling Options

Ahmed

Rao

Shah

et al. 2014

Advances in Electrical Engineering

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“…Han et al [20] have proposed two new solution methods for Dynamic Economic Dispatch. Global optimization techniques such as Genetic Algorithm (GA) [21], Tabu Search (TS) [22], Gravitational Search Algorithm [23], Bio-geography based Optimization [24] and Simulated Annealing (SA) [25] have also been used for this purpose. However, these techniques have some drawbacks regarding their ability to converge toward the final solution which has limited their usage.…”

Section: Introductionmentioning

confidence: 99%

A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm

Sen

Mathur

2016

International Journal of Electrical Power & Energy Systems

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“…These methods in terms of the isotropy and the response quality, these methods do not have high acceptability. The most common intelligent methods applied for solving this issue are taboo search [6][7], neural network [8], fuzzy logic [9], genetic algorithm [10][11][12][13], particle swarm optimization [14], and artificial bee colony algorithm [15][16], and teaching-learning based optimization [17].…”

Section: Introductionmentioning

confidence: 99%

A Modified ANT Colony Algorithm for Solving the Unit Commitment Problem

Zand¹,

Bigdeli²,

Azizian³

2016

AEIJ

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Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the optimization of the generative units to minimize the full action cost regarding problem constraints. In this article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from local minimums by performing flexible memory system. The efficiency of proposed method in two power system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed method with results of the past well-known methods is a proof for suitability of performing the introduced algorithm in economic input and output of generative units.

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Solving the Economic Dispatch Problem by Using Tabu Search Algorithm

Cited by 38 publications

References 4 publications

Performance of Various Metaheuristic Techniques for Economic Dispatch Problem with Valve Point Loading Effects and Multiple Fueling Options

Performance of Various Metaheuristic Techniques for Economic Dispatch Problem with Valve Point Loading Effects and Multiple Fueling Options

A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm

A Modified ANT Colony Algorithm for Solving the Unit Commitment Problem

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