• Journal of Electrical Engineering and Technology
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• 제10권4호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.848-849
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• 2007

This paper presents an efficient approach for solving the economic dispatch (ED) problems with valve-point effects using differential evolution (DE). A DE, one of the evolutionary algorithms (EAs), is a novel optimization method capable of handling nonlinear, non-differentiable, and nonconvex functions. And an efficient constraints treatment method (CTM) is applied to handle the equality and inequality constraints. The resultant DE-CTM algorithm is very effective in solving the ED problems with nonconvex cost functions. To verify the superiority of the proposed method, a sample ED problem with valve-point effects is tested and its results are compared with those of previous works. The simulation results clearly show that the proposed DE-CTM algorithm outperforms other state-of-the-art algorithms in solving ED problems with valve-point effects

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.648-649
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• 2011

This Paper presents an efficient approach for solving the economic dispatch (ED) problems with valve-point effects using an combination of particle swarm optimization and harmony search. To reduce a premature convergence effect of PSO algorithm, We proposed PSO-HS algorithm considering evolutionary using harmony search algorithm. To prove the ability of the PSO-HS in solving nonlinear optimization problems, ED problems with non-convex solution spaces are solved with three different approach(PSO, HS, combination of PSO and HS)

• 대한전기학회:학술대회논문집
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• 대한전기학회 2015년도 제46회 하계학술대회
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• pp.523-524
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• 2015

신재생 에너지는 발전 시 연료가 들지 않는다는 특징과 친환경적인 이유로 각광 받고 있다. 이러한 특징으로 인해 에너지 독립 계통을 구성하는 데 주요 역할을 한다. 풍력, 태양광, 에너지 저장 장치 등이 분산 전원으로서 계통을 구성하게 되는데, 낮은 효율과 높은 초기 설치비용으로 인해 디젤 발전기나 마이크로 터빈 등 소형 발전기를 예비 전력으로서 부착하는 경우가 대부분이다. 이러한 분산 전원들은 가격이 제각각이며 기존의 화석연료 발전기처럼 비용 함수 모델링이 어려워 경제적 분석이 어려웠다. 이 논문은 이러한 신재생 에너지를 사용한 독립 마이크로그리드를 경제적 관점에서 분석하는 것이 목적이다. 따라서 계통을 분석하는 가장 보편적인 방법인 최적 조류 계산(OPF, Optimal Power Flow)방법과 가격을 최소화하는 점을 찾는 경제급전(ED, Economic Dispatch)방법을 사용하였다. 두 가지 방법의 결과를 비교하여 발전 비용을 줄일 수 있는지에 대해 조사하였다. 7-Bus 가상 계통을 만들어 사용하였고, OPF와 ED의 발전 비용은 PowerFactory$^{(R)}$ 프로그램을 통해 시뮬레이션 하여 데이터를 얻었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.850-851
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• 2007

This paper presents an efficient approach for solving the economic dispatch (ED) problems with prohibited operating zones using an improved particle swarm optimization (PSO). Although the PSO-based approaches have several advantages suitable to the heavily constrained nonconvex optimization problems, they still have the drawbacks such as local optimal trapping due to the premature convergence (i.e., exploration problem) and insufficient capability to find nearly-by extreme points (i.e., exploitation problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. The proposed method is applied to ED problem with prohibited operating zones. Also, the results are compared with those of the state-of-the-art methods.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1778-1790
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• 2018

Based on the requirements and characteristics of multi-zone autonomous decision-making in modern power system, fully distributed computing methods are needed to optimize the economic dispatch (ED) problem coordination of multi-regional power system on the basis of constructing decomposition and interaction mechanism. In this paper, four fully distributed methods based on alternating direction method of multipliers (ADMM) are used for solving the ED problem in distributed manner. By duplicating variables, the 2-block classical ADMM can be directly used to solve ED problem fully distributed. The second method is employing ADMM to solve the dual problem of ED in fully distributed manner. N-block methods based on ADMM including Alternating Direction Method with Gaussian back substitution (ADM_G) and Exchange ADMM (E_ADMM) are employed also. These two methods all can solve ED problem in distributed manner. However, the former one cannot be carried out in parallel. In this paper, four fully distributed methods solve the ED problem in distributed collaborative manner. And we also discussed the difference of four algorithms from the aspects of algorithm convergence, calculation speed and parameter change. Some simulation results are reported to test the performance of these distributed algorithms in serial and parallel.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1417-1426
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• 2017

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.198-200
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• 2006

This paper presents a development of an educational simulator of particle swarm optimization (PSO) and application for solving the test functions and economic dispatch (ED) problems with nonsmooth cost functions. A particle swarm optimization is one of the most powerful methods for solving global optimization problems. It is a population-based search algorithm and searches in parallel using a group of particles similar to other AI-based heuristic optimization techniques. In developed simulator, lecturers and students can select the functions for simulation and set the parameters that have an influence on PSO performance. To improve searching capability for ED problems, a crossover operation is proposed to the position update of each individual (CR-PSO). To verify the feasibility of CR-PSO method, numerical studies have been performed for two different sample systems. The proposed CR-PSO method outperforms other algorithms in solving ED problems.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1329-1339
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• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• 전기학회논문지
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• 제68권1호
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• pp.8-12
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• 2019

Combined Cycle Unit(CC) generates the primary power from the Gas Turbine(GT) and supplies the remaining heat of the GT to the Steam Turbine(ST) to generate the secondary power from the ST. It plays a major role in terms of energy efficiency and Load Frequency Control(LFC). Incremental Heat Rate(IHR) curves of economic dispatch(ED) of CC is applied differently by GT/ST combination. But It is practically difficult because of performance test by all combinations. This paper suggests a reasonable method for estimating IHR curves for partial combinations(1:1~(N-1):1) using IHR curves when operating with GT alone(1:0) and with all(N:1) combinations of CC.