• 전기학회논문지
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• 제66권3호
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• pp.513-521
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• 2017

Robust optimal operation of a microgrid is required since the increase of the penetration level of renewable generators in the microgrid raises uncertainty due to their intermittent power output. In this paper, an application of probabilistic optimization method to economical operation of a microgrid is studied. To simplify the treatment of the uncertainties of renewable generations and load, the new 'band of virtual equivalent load variation' is introduced considering their uncertainties. A simplified robust optimization methodology to generate the scenarios within the band of virtual equivalent load variation and to obtain the optimal solution for the worst scenario is presented based on Monte Carlo method. The microgrid to be studied here is composed of distributed generation system(DGs), battery systems and loads. The distributed generation systems include combined heat and power(CHP) and small generators such as diesel generators and the renewable energy generators such as photovoltaic(PV) systems and wind power systems. The modeling of the objective function for considering interruption cost by the penalty function is presented. Through the case study for a microgrid with uncertainties, the validity of proposed robust optimization methodology is evaluated.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1386-1396
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• 2017

In this paper, a new approach is proposed to select the optimal sitting and sizing of distributed solar photovoltaic generation (SPVG) in a radial electrical distribution systems (EDS) considering load/generation uncertainties. Here, distributed generations (DGs) allocation problem is modeled as optimization problem with network loss based objective function under various equality and inequality constrains in an uncertain environment. A boundary power flow is utilized to address the uncertainties in load/generation forecasts. This approach facilitates the consideration of random uncertainties in forecast having no statistical history. Uncertain solar irradiance is modeled by beta distribution function (BDF). The resulted optimization problem is solved by a new Dynamic Harmony Search Algorithm (DHSA). Dynamic band width (DBW) based DHSA is proposed to enhance the search space and dynamically adjust the exploitation near the optimal solution. Proposed approach is demonstrated for two standard IEEE radial distribution systems under different scenarios.

• KIEE International Transactions on Power Engineering
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• 제12A권1호
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• pp.31-35
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• 2002

This paper presents a new operation scheme of UPFC to minimize both generation costs and active power losses in a normal operation state of power system. In a normal operation, cost minimization is a matter of primary concern among operating objectives. This paper considers two kinds of costs, generation costs and transmission losses. The total generation cost of active powers can be minimized by optimal power flow, and active power losses in the transmission system can be also minimized by power flow control of UPFC incorporated with minimization of generation costs. In order to determine amounts of active power reference of each UPFC required for the cost minimization, an iterative optimization algorithm based on the power flow calculation using the decoupled UPFC model is proposed. For verification of the proposed method, intensive studies have been performed on a 3-unit 6-bus system equipped with a UPFC.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.57-63
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• 2018

The operating system for an isolated microgrid, which is completely disconnected from the central power system, aims at preventing blackouts and minimizing power generation costs of diesel generators through efficient operation of the energy storage system (ESS) that stores energy produced by renewable energy generators and diesel generators. In this paper, we predict the amount of renewable energy generation using the weather forecast and build an optimal diesel power generation plan using a genetic algorithm. In order to avoid inefficiency due to inaccurate prediction of renewable energy generation, our search algorithm imposes penalty on candidate diesel power generation plans that fail to maintain the SOC (state of charge) of ESS at an appropriate level. Simulation experiments show that our optimization method for maintaining an appropriate SOC balance can prevent the blackout better when compared with the previous method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
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• pp.202-204
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• 2003

This paper suggests an optimal parameter setting method by use of parametric protectability index, as the objective function. In this paper, a gradient based optimization method is used under the assumption that the initial values of a parameter is in a convex set including the optimal value, which is verified by a plenty of simulation studies. The proposed method is applied to a sample distribution network to shows its effectiveness.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권11호
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• pp.671-679
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• 2004

This paper presents the on-line current controller tuning method of grid-connected inverter using PSO(particle swarm optimization) technique for minimizing the harmonic current. Synchronous frame PI current regulator is commonly used in most distributed generation. However, due to the source voltage distortion, specially in weak AC power system, current may contain large harmonic components, which increase THD(total harmonic distortion) and deteriorates power quality. Therefore, some tuning method is necessary to improve response of current controller. This paper used the PSO technique to tune the current regulator and through simulation and experiments, usefulness of the tuning method has been verified. Especially in simulating the tuning process, ASM(average switching model) of inverter is used to shorten execution time.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• 한국인터넷방송통신학회논문지
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• 제17권4호
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• pp.219-224
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• 2017

경제급전 최적화 문제를 해결하는 결정론적인 알고리즘에 존재하지 않아 지금까지는 비결정론적인 휴리스틱 알고리즘들이 제안되고 있다. 이와 더불어 실시간 급전문제에 대한 연구는 거의 없는 실정이다. 본 논문은 발전정지 개념을 도입하여 실시간 급전의 최적화 문제를 풀 수 있는 알고리즘을 제안하였다. 제안된 알고리즘은 단위 발전량당 최대 비용이 소요되는 발전기는 발전을 중지시키는 기준을 적용하였다. 본 논문에서 제안한 발전정지 기준은 발전비용함수에서 밸브효과에 따른 비선형 절대치 함수를 제외한 2차 함수만을 대상으로 하였다. 경제급전 문제의 시험사례로 빈번히 활용되고 있는 데이터에 대해 제안된 알고리즘을 적용한 결과 기존 알고리즘들의 해를 크게 감소시킬 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제8권2호
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• pp.223-229
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• 2013

Restructuring and recent developments in the power system and problems arising from construction and maintenance of large power plants, increasing amount of interest in distributed generation (DG) source. Distributed generation units due to specifications, technology and location network connectivity can improve system and load point reliability indices. In this paper, the allocation and sizing of DG in distribution networks are determined using optimization. The objective function of the proposed method is to improve customer-based reliability indices at lowest cost. The placement and size of DGs are optimized using a Genetic Algorithm (GA). To evaluate the proposed algorithm, 34-bus IEEE test system, is used. The results illustrate efficiency of the proposed method.

• 전력전자학회논문지
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• 제27권4호
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• pp.297-304
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• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.