• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.52-54
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• 2000

Almost traditional ELD(Economic Load Dispatch) is hard to apply to power system directly and also OPF(Optimal Power Flow) is not easy to solve the problem. This paper deals with the practical application of ELD with considering power equation.

• 대한전기학회논문지
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• 제41권1호
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• pp.1-8
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• 1992

Hopfield neural network has been applied to the problem of economic load dispatch(ELD) of electric power. The optimum values of neuron potentials are represented in terms of large numbers. The differential synchronous transition mode is used in this simulation. Through case studies, we have shown the possibility of the application of neural network to ELD. In case of including the transmission losses, the proposed method has an advantage that the problem can be solved simply with one neural network, without calculating incremental fuel costs and incremental losses required by traditional method.

• 한국인터넷방송통신학회논문지
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• 제16권2호
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• pp.181-188
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• 2016

본 논문은 경제급전 최적화 문제에 개별 발전회사별로 독립적으로 경제급전을 수행하는 방법에 비해 중앙에서 통합하여 경제급전 최적화를 수행하는 경우가 보다 경제적임을 보였다. 이 경우에 적용된 알고리즘으로 밸브지점으로 발전량을 조절하는 균형방법을 수행한 후 발전량을 감소시킬 때의 비용 감소분과 증가시킬 때의 비용 증가분 차이로 발전량을 상호 교환하는 방법으로 최적화를 수행하였다. 10대, 13대과 40대-발전기를 독립적으로 운영하는 경우와 통합된 63대-발전기를 경제급전하는 경우를 비교한 결과 통합운영 방법이 독립적 경제급전에 비해 발전비용을 획기적으로 감소시켜 경제적인 이익을 극대화 시킬 수 있음을 보였다.

• 대한전기학회논문지:전력기술부문A
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• 제50권11호
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• pp.520-527
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• 2001

This paper suggests a new methodology of evolutionary computations - an Adaptive Evolutionary Algorithm (AEA) for solving the Economic Load Dispatch (ELD) problem which has piecewise quadratic cost functions and prohibited operating zones with many local minima. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and the population by ES are adaptively modulated according to the fitness. Case studies illustrate the superiority of the proposed methods to existing conventional methods in power generation cost and computation time. The results demonstrate that the AEA can be applied successfully in the solution of ELD with piecewise quadratic cost functions and prohibited operating zones

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.388-394
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• 1999

This paper is concerned with an application of Parallel Genetic Algorithms(PGA) to optimal econmic load dispatch(ELD) in power systems. The ELD problem is to minimize the total generation fuel cost of power outputs for all generating units while satisfying load balancing constraints. Genetic Algorithms(GA) is a good candidate for effective parallelization because of their inherent principle of evolving in parallel a population of individuals. Each individual of a population evaluates the fitness function without data exchanges between individuals. In application of the parallel processing to GA, it is possible to use Single Instruction stream, Multiple Data stream(SIMD), a kind of parallel system. The architecture of SIMD system need not data communications between processors assigned. The proposed ELD problem with C code is implemented by SIMSCRIPT language for parallel processing which is a powerfrul, free-from and versatile computer simulation programming language. The proposed algorithms has been tested for 38 units system and has been compared with Sequential Quadratic programming(SQP).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.133-136
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• 1996

In the paper, an artificial neural network (ANN) approach based on Lagrange multiplier method (Lagrangian ANN) is used to solve an economic load dispatch (ELD) problem. Traditionally ELD problem has one convex cost function as its objective function and nonlinear constraints such as power balance and maximum-minimum limits of real power. In this study, modification is given to the Lagrangian ANN proposed by Gong et all[5] to guarantee the convergence to the optimal solution. Simulation results demonstrate the effectiveness of the proposed method applied to the ELD problem.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.832-834
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• 1996

This paper presents the research on the application of the Hopfield Neural Network to the Economic Load Dispatch problem. The ELD problem has convex cost functions as the objective functions, power balance equation and real power lower/upper limits as the constraints. So we have shown that the possibility of the application of the Hopfield Neural Network to the ELD problem. Through the case study, the simulation results are very close to the numerical method and the dynamic programming method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.391-393
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• 2000

This paper presents an new algorithm for the economic load dispatch considering the reliability level constraints of composite power system under deregulated electricity market. It is the traditional ELD problem that generation powers have been dispatched In order to minimize total fuel cost subjected to constraints which sum of powers generated must equal the received load and no violating lower and upper limit constraints on generation. Under deregulated electricity market, however, generation powers of a pool have to be reallocated newly in order to satisfy the reliability differentiated level required at a load point because of a reliability differentiated electricity service which is a part of the priority service. In this study, new economic load dispatch algorithm for reallocating the generation powers of a pool in order to satisfy the reliability differentiated level under deregulated competitive electricity market is proposed. The uncertainties of not only generators but also transmission lines are considered fer the reliability evaluation. The characteristics and effectiveness of this methodology are illustrated by the case studies on MRBTS and IEEE-RTS.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.15-26
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• 2014

This paper proposes a Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called 'rbest' is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

• 조명전기설비학회논문지
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• 제28권8호
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• pp.47-53
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• 2014

This paper presents an Differential Search(DS) Algorithm for solving the economic load dispatch(ELD) problems with Valve-Point loading constraints. DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate. Numerical results on a test system consisting of 13 units show that the proposed approach is faster, more robust and powerful than conventional algorithms. Case studies show the simulation results are better than Lagrange method, the Hopfield neural networks and GA.