• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1415-1421
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• 2015

This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetism-like mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1111-1113
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• 1998

A robust economic dispatch algorithm involving transmission losses is proposed and investigated for a possibility of on-line applications. In this paper, the penalty factors are calculated directly from transposed Jacobian of load flow analysis with advantages of superiority to B-coefficients method based on its computation time and suitability for real time application since the approach is based on a current system condition. The proposed algorithm is systematically handling the generation capacity constraints with transmission losses. Implementation of the algorithm for IEEE systems and EPRI Scenario systems shows that computation time is enough to apply on-line economic dispatch to large power system and production cost is saved compared with the crude classical economic dispatch algorithm without considering transmission losses.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.203-211
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• 1999

This paper presents a new approach on genetic on genetic algorithm to economic dispatch problem for valve point discontinuities. Proposed approach in this paper on genetic algorithms improves the performance to solve economic dispatch problem for valve point discontivuities through improved death penalty method, generation-apart elitism, atavism and sexual selection with sexual distinction. Numerical results on a test system consisting of 13 thermal units show that the proposed approach is faster, more robust and powerful than conventional genetic algorithms.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.172-174
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• 1996

This paper presents a new approach on genetic algorithms to economic dispatch problem for valve point discontinuities. Although it has been already shown that genetic algorithm was more powerful to economic dispatch problem for valve point discontinuities than other optimization algorithms, proposed approach in this paper on genetic algorithms improves the performance to solve economic dispatch problem for valve point discontinuities through combination in penalty function with death penalty, generation-apart elitism and heuristic crossover. Numerical results on an actural utility system consisted of 13 thermal units show that the proposed approach is faster and robuster than the classical genetic algorithm.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.744-751
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• 2013

In this paper, Secant method is proposed to solve multi area economic dispatch (MAED) problem. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators. Here, Secant method is applied to solve the CED problem. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.3
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• pp.7-14
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• 2002

The economic dispatch is one of the major concerns in case of buying and selling generated powers between electric powers companies. This paper describes the power system security the optimal power flow, and the security-constrained economic dispatch and shows the EDSA\`s active optimal power flow software package is well suited through a simple case study.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1508-1517
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• 2015

Optimal Power dispatch is the short-term decision of the optimal output of a number of power generation facilities, to meet the system demand, with the objective of Power dispatching at the lowest possible cost, subject to transmission lines power loss and operational constraints. The operational constraint includes power balance constraint, generator limit constraint, and emission dispatch constraint and valve point effects. In this paper, Opposition based Differential Evolution Algorithm (ODEA) has been proposed to handle the objective function and the operational constraints simultaneously. Furthermore, the valve point loading effects and transmission lines power loss are also considered for the efficient and effective Power dispatch. The ODEA has unique features such as self tuning of its control parameters, self acceleration and migration for searching. As a result, it requires very minimum executions compared with other searching strategies. The effectiveness of the algorithm has been validated through four standard test cases and compared with previous studies. The proposed method out performs the previous methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.181-188
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• 2016

This paper demonstrates that centralized economic load dispatch optimization is much more economical than independent optimization carried out by individual power generating companies. The algorithm applied here optimizes by balancing the generation power at the valve-point, then readjusting generation power by comparing incremental operating cost incurred by marginal increase in the generation power and decremental operating cost likewise incurred by marginal decrease in the generation power. Upon comparing 3 individual optimization cases of 10, 13, and 40 generators respectively with centralized optimization of 63 generators, centralized operation for economic load dispatch optimization has proven to maximize economic benefits by markedly reducing operation costs of individual optimization.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.