Economic Power Generation Using Linear Programming

Fanshel, S.; Lynes, Eugene Stewart

doi:10.1109/tpas.1964.4766011

Cited by 31 publications

(6 citation statements)

References 8 publications

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“…Classical techniques, such as lambda iteration [1], quadratic programming [2], linear programming [3], and Lagrangian relaxation [4], offer simple approaches to obtain favourable solutions in different situations. These methods have the advantages of quick calculations and can theoretically demonstrate optimality in certain algorithms.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

Pawani,

Singh

2024

Eng. Res. Express

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The study focuses on a hypothesis-based critical analysis of proposed modifications to the slime mould algorithm. The modifications being investigated are opposition learning and wavelet mutation. These modifications help the slime mould algorithm to avoid local optima for better exploration of the search space. The goal is to assess the effectiveness of these modifications in improving the performance of slime mould algorithm. Both, the basic slime mould algorithm, and the proposed variant eventually achieve the desired convergence. However, to compare the efficiency of the two algorithms, the study introduces a measurement index called the "swiftness" of the algorithm. This index quantifies the speed at which an algorithm reaches convergence. It is calculated as the area under the convergence curve of each algorithm. The rationale behind this approach is that visual inspection alone may not be sufficient to discriminate between the algorithms based on the convergence curve. The hybrid approach, incorporating opposition learning and wavelet mutation, is evaluated statistically using the CEC 2008 benchmark function. Additionally, the study examines small and medium single-objective power dispatch optimization problems that adhere to non-convex system limitations. By assessing the performance of the modified slime mould algorithm on these different problem domains, the researchers aim to provide a comprehensive analysis of its effectiveness.

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Section: Introductionmentioning

confidence: 99%

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

Pawani,

Singh

2024

Eng. Res. Express

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“…Reduction of fossil fuels resources and high fuel prices encourage utility to operate the system at minimum cost as well as satisfied all the system and operational constraints. Commonly, the cost function of thermal generator is modelled as quadratic function that can be solve efficiently by most of the conventional methods such lambda iteration method [1], linear programming [2], quadratic programming [3] and other methodsthat can be found in [4] [5, 6].…”

Section: Introductionmentioning

confidence: 99%

Gbest Artificial Bee Colony for Non-convex Optimal Economic Dispatch in Power Generation

Abdullah

Manan

Jamian

et al. 2018

IJEECS

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Non-convex Optimal Economic Dispatch (OED) problem is a complex optimization problem in power system operation that must be optimized economically to meet the power demand and system constraints. The non-convex OED is due to the generator characteristic such as prohibited operation zones, valve point effects (VPE) or multiple fuel options. This paper proposes a Gbest Artificial Bee Colony (GABC) algorithm based on global best particle (gbest) guided of Particle Swarm Optimization (PSO) in Artificial bee colony (ABC) algorithm for solving non-convex OED with VPE. In order to investigate the effectiveness and performance of GABC algorithm, the IEEE 14-bus 5 unit generators and IEEE 30-bus 6 unit generators test systems are considered. The comparison of optimal solution, convergence characteristic and robustness are also highlighted to reveal the advantages of GABC. Moreover, the optimal results obtained by proposed GABC are compared with other reported results of meta-heuristic algorithms. It found that the GABC capable to obtain lowest cost as compared to others. Thus, it has great potential to be implemented in different types of power system optimization problem.

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“…However, they suffer from various limitations. In Linear programming [1] method, generator fuel cost characteristics are approximated as piecewise linear. This is one of the major disadvantages of this method even though it is fast and reliable.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Oppositional Symbiotic Organisms Search Algorithm for Different Economic Load Dispatch Problems

2018

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In this paper, an effective meta-heuristic technique called Quasi-Oppositional Symbiotic Organisms Search is applied for solving non-convex economic dispatch problems. Symbiotic Organisms Search is a soft computing technique, inspired by organisms in the ecosystem. This technique is implemented for improving the solution quality in minimum time. In order to improve convergence rate, quasi-reflected numbers are used here instead of pseudo-random numbers. Different equality and inequality constraints such as transmission loss, load demand, prohibited operating zone, generator operating limits and boundary of ramp rate are considered here. Presence of multiple fuels and valve point are also considered in some cases. This algorithm is applied to four different test systems. Simulation results are compared with many recently developed optimization techniques to show the superiority and consistency of this method. Simulation results also show that the computational efficiency of this algorithm is much better than the other meta-heuristic methods available in the literature.

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Economic Power Generation Using Linear Programming

Cited by 31 publications

References 8 publications

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

Gbest Artificial Bee Colony for Non-convex Optimal Economic Dispatch in Power Generation

Quasi-Oppositional Symbiotic Organisms Search Algorithm for Different Economic Load Dispatch Problems

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