• 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.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.476-483
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• 2008

This paper is aimed at presenting techniques of hybrid differential evolution for solving various kinds of Economic Dispatch(ED) problems such as those including prohibited zones, emission dispatch, multiple fuels, and multiple areas. The results obtained for typical problems are compared with those obtained by other techniques such as Particle Swarm Optimization(PSO) and Classical Evolutionary Programming(CEP) techniques. The comparison of the results proves that hybrid differential evolution is quite favorable for solving ED problems with no restrictions on the shapes of the input-output functions of the generator.

• 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.

• Journal of Energy Engineering
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• v.6 no.2
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• pp.129-136
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• 1997

This paper presents a new economic load dispatch considering atmospheric emissions such as NOx and SO$_2$caused by the operation of fossil-fueled thermal generation in power systems. The proposed method is described for scheduling their output of thermal power units so as to comply with total emission constraint, area emission constraint and the both of those constraints. Also, by using a trade-off curve, representing all dispatch alternatives and conflict between the emission and the fuel cost, the sensitivity analysis of the emission and the fuel cost is applied to this algorithm. By the way, this proposed method is analyzed how dispatch changes as a function of the total environmental cost, and as a function of the relative weighting of individual environmental insults, e.g, NOx and SO$_2$. By applying the proposed method to the system, the usefulness of this method is verified.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.574-577
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• 1995

The proposed method is described for scheduling their output of thermal power units so as to comply with total emission constraint, area emission constraint and the both of those constraints. Also, by using a trade-off curve, representing all dispatch alternatives and conflict between the emission and the fuel cost, the sensitivity analysis of the emission and the fuel cost is applied to this algorithm. By the way, this proposed method is analyzed how dispatch changes as a function of the total environmental cost, and as a function of the relative weighting of individual environmental insults, e.g, NOx and $SO_{2}$. By applying the proposed method to the system, the usefulness of this method is verified.

• Journal of Energy Engineering
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• v.16 no.1 s.49
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• pp.40-45
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• 2007

Many countries have been preparing the exercise of UNFCCC (United Nations Framework Convention on Climate Change). If UNFCCC is enforced, considerable changes in generation sector are expected due to the imposed greenhouse gas emission. This paper proposes dispatch scheduling algorithms which incorporate the emission constraints and emission trading. Numerical examples are provided to demonstrate the availability these algorithms.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.406-410
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• 2001

This paper develops an efficient evolutionary programming based algorithm for solving the environmentally constrained economic dispatch problem in thermal power system. The proposed algorithm can deal with the power balance constraints and the emission constraints which are equality and inequality constraints, respectively. Numerical results show that the proposed algorithm can provide superior solutions within reasonable time through its application to a test system.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.341-343
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• 2000

Traditionally electric power system are operated in such a way that the total fuel cost is minimized regardless of accounting for tie-lines transmission constraint and emissions produced. But tie-lines transmission and emissions constraint are very important issues in the operation and planning of electric power system. This paper presents the Two-Phase Neural Network(TPNN) to solve the Economic Load Dispatch (ELD) problem with tie-lines transmission and emissions constraint considering transmission losses. The transmission losses are obtained from the B-coefficient which approximate the system losses as s quadratic function of the real power generation. By applying the proposed algorithm to the test system, the usefulness of this algorithm is verified.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.