• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
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• pp.156-158
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• 2005

This paper presents an Optimal Power Flow (OPF) algorithm using Interior Point Method (IPM) to swiftly and precisely perform the five minute dispatch. This newly suggested methodology is based on Affine Sealing Interior Point Method (AS IPM), which is favorable for large-scale problems involving many constraints. It is also eligible for OPF problems in order to improve the calculation speed and the preciseness of its resultant solutions. Big-M Method is also used to improve the solution stability. Finally, this paper provides a relevant case study to confirm the efficiency of the proposed methodology.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.98-100
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• 2003

This paper presents the method for computing the power economic dispatch with an optimal power flow (OPF) computation algorithm, considering the power factor limits constraint. Efficient reactive power planning enhances economic operation as well as system security. Accordingly, an adequate level of power factor limits for the load buses should be evaluated for economic operation. The power factor limits are included and described into the OPF's objective function. The proposed method is applied to IEEE 26 buses system.

• 대한전기학회논문지
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• 제33권10호
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• pp.410-422
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• 1984

This paper presents a new practical method for optimal active and reactive power control for the economic operation in electrical power system, and the programs are developed for digital computer solution. The major features and techniques of this paper are as follows: 1) The method is presented for finding the equivalent active power balance equation applying the sparse Jacobian matrix of power flow equation instead of using B constant as active power balance equation considering transmission loss, and thus for determining directly optimal active power allocation berween generator unitw satisfying the equality and inequality constraints. 2) The method is proposed for solving directly the optimum economim dispatch problem without using gradient method and penalty function for both active and reactive power control. As a result, the computing time are reduced and convergence characteristic is remarkably improved. 3) Unlike most of conventional methods which adopt the transmission loss as a objective function for reactive power control, the total fuel cost of themal power plant is adopted as objective function for both active and reactive power control. consequently, more reasonable and economic profit can be achieved.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.15-17
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• 1994

The economic operation in power systems has long been in keen interests for power system engineers. The classical equal incremental fuel cost rule is still the basis for it, even though more elaborate tools such as optimal power flow have been developed already. The classical method requires usually many iterations, while the optimal power flow shows often some difficulties. This paper suggests a single step solution based on the classical method revisited. The concept is shown graphically. Three sample systems are compared. The proposed approach has shown a single step solution regardless system sizes, while the conventional methods require many iterations.

• 한국지능시스템학회논문지
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• 제24권1호
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• pp.40-45
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• 2014

본 논문은 풍력에너지가 전력계통에 투입되었을 때 전압안정도 마진을 보장할 수 있도록 최적화 기법을 적용한 발전력 재분배 알고리즘에 대하여 제안하고자 한다. 풍력발전기는 대부분 유도기로 구성되기 때문에 일반적으로 풍력발전기가 기존 전력계통에 투입되는 경우 우리는 전압안정도와 같은 문제를 고려해야 한다. 풍력발전기가 계통에 투입되는 비중이 증가하는 경우 전압안정도와 같은 계통의 전반적인 안정도 문제에 영향을 미치게 된다. 본 논문에서는 'Area 1'과 'Area 2'로 구성되어 있는 2-area 계통을 고려하고자 한다. 'Area 1'내의 발전기의 발전비용이 'Area 2'지역보다 상대적으로 비싸고 'Area 1'내의 부하량이 'Area 2'내의 부하량보다 더 많다고 가정하면, 계통운영자는 경제성 관점에서 보면 값비싼 'Area 1' 내의 발전기의 출력은 감소시키고, 'Area 2 지역내의 발전기 출력을 증가시키려고 할 것이다. 반면에 안정도 관점에서 보면 오히려 'Area 1' 지역내의 발전기 출력을 증가시켜야 하기 때문에 trade-off 점이 존재한다. 전력계통 해석은 비선형성이 매우 강하기 때문에 안정도 문제를 포함한 최적화 문제를 수치적으로 해결하기에 어려움이 많다. 따라서 본 논문에서는 시뮬레이션 기반의 최적화 알고리즘인 유전알고리즘을 적용하여 본 문제를 해결하고자 한다. 시뮬레이션을 위해서 New England 계통을 이용하였고, 알고리즘은 Python 2.5로 구현하였다.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of Electrical Engineering and Technology
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• 제4권4호
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• pp.457-466
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• 2009

This paper presents efficient parallel genetic algorithm (EPGA) based decomposed network for optimal power flow with various kinds of objective functions such as those including prohibited zones, multiple fuels, and multiple areas. Two coordinated sub problems are proposed: the first sub problem is an active power dispatch (APD) based parallel GA; a global database generated containing the best partitioned network: the second subproblem is an optimal setting of control variables such as generators voltages, tap position of tap changing transformers, and the dynamic reactive power of SVC Controllers installed at a critical buses. The proposed approach tested on IEEE 6-bus, IEEE 30-bus and to 15 generating units and compared with global optimization methods (GA, DE, FGA, PSO, MDE, ICA-PSO). The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.202-206
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• 1989

A new algorithm is suggested to solve the optimal reactive power control(optimal VAR control) problem. An efficient computer program based on the latest achievements in the sparse matrix/vector techniques has been developed for this purpose. The model minimizes the real power losses in the system. The constraints include the reactive power limits of the generators, limits on the bus voltages and the operating limits of control variables- the transformer tap positions, generator terminal voltages and switchable reactive power sources. The method developed herein employs linearized sensitivity relationships of power systems to establish both the objective function for minimizing the system losses and the system performance sensitivities relating dependent and control variables. The algorithm consists of two modules, i.e. the Q-V module for reactive power-voltage control, Load flow module for computational error adjustments. In particular, the acceleration factor technique is introduced to enhance the convergence property in Q-module, The combined use of the afore-mentioned two modules ensures more effective and efficient solutions for optimal reactive power dispatch problems. Results of the application of the method to the sample system and other worst-case system demonstrated that the algorithm suggested herein is compared favourably with conventional ones in terms of computation accuracy and convergence characteristics.

• 대한전기학회논문지
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• 제39권3호
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• pp.232-239
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• 1990

A new algorithm is suggested to solve the optimal reactive power and voltage control (optimal VAR control) problem. The model minimizes the real power losses in the system. The constraints include the reactive power limits of the generators, limits on the bus voltages and the operating limits of control variables-the transformer tap positions generator terminal voltages and switchable reactive power sources. The method presented herein, using a newly developed Jacobian decomposition method, employs linearized sensitivity relationships of power systems to establish both the objective function for minimizing the system losses and the system performance sensitivities relating dependent and control variables. The algorithm consists of two modules, i.e. the Q-V module for reactive power-voltage control, and load flow module for computational error adjustments. In particular the acceleration factor technique is introduced to enhance the convergence property in Q-V module. The combined use of the afore-mentioned two modules ensures more effective and efficient solutions for optimal reactive power dispatch problems. Results of the application of the method to a sample system and other worst-case systems demonstrated that the algorithm suggested herein is compared favourably with conventional ones in terms of computation accuracy and convergence characteristics.

• 전기학회논문지
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• 제56권10호
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.