• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.6
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• pp.551-561
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• 1991

The probabilistic generation simulation plays a key role in power system expansion and operational planning especially for the calculation of expected energy, loss of load probaility and unserved energy expected. However, it is crucial to develop a probabilistic generation simulation algorithm which gives sufficiently precise results within a reasonable computation time. In a previous paper, we have proposed an efficent method using Fast Hartley Transform in convolution process for considering the thermal and nuclear units. In this paper, a method considering the scheduling of pumped-storage plants and hydro plants with energy constraint is proposed. The method also adopts FHT techniques. We improve the model to include multi-state and multi-block generation. The method has been applied for a real size model system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.8
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• pp.418-428
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are newly defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.640-643
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• 1987

A probabilistic production costing model based on the economic load dispatch has been developed. Objective function is composed of fuel cost which is a function of generation output and the failure cost. Coefficients of the failure cost is determined from the known equivalent generation cost. The model is compared with other existing methodolgies and the excellent results are obtained.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.29-30
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• 2008

This paper defines a concept of "protectability" for the performance evaluation of distance relay considering its sensitivity and selectivity. The paper starts from the probabilistic modeling of the errors, and based on this model, a detailed explanation of protectability calculation for each zone of the distance relay is presented. An effect of the Weighting Rate and the Measurement Deviation on the protectability evaluation is also given. By considering this effect, the optimization of relay setting can be realized. The proposed method is applied to a typical model system to show its effectiveness.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.350-358
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• 2017

Recently, owing to increased interest in low-carbon energy supplies, renewable energy sources such as photovoltaics and wind turbines in distribution networks have received considerable attention for generating clean and unlimited energy. The presence of energy storage systems (ESSs) in the promising field of active distribution networks (ADNs) would have direct impact on power system problems such as encountered in probabilistic distributed generation (DG) model studies. Hence, the optimal procedure is offered herein, in which the simultaneous placement of an ESS, photovoltaic-based DG, and wind turbine-based DG in an ADN is taken into account. The main goal of this paper is to maximize the net present value of the loss reduction benefit by considering the price of electricity for each load state. The proposed framework consists of a scenario tree method for covering the existing uncertainties in the distribution network's load demand as well as DG. The collected results verify the considerable effect of concurrent installation of probabilistic DG models and an ESS in defining the optimum site of DG and the ESS and they demonstrate that the optimum operation of an ESS in the ADN is consequently related to the highest value of the loss reduction benefit in long-term planning as well. The results obtained are encouraging.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2211-2220
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• 2015

This paper analyzes multiple active management (AM) techniques of active distribution network (ADN), and proposes an optimal coordination model of intermittent distributed generation (IDG) accommodation considering the timing characteristic of load and IDG. The objective of the model is to maximize the daily amount of IDG accommodation under the uncertainties of IDG and load. Various active management techniques such as IDG curtailment, on-load tap changer (OLTC) tap adjusting, voltage regulator (VR) tap adjusting, shunt capacitors compensation and so on are fully considered. Genetic algorithm and Primal-Dual Interior Point Method (PDIPM) is used for the model solving. Point estimate method is used to simulate the uncertainties. Different scenarios are selected for the IDG accommodation capability investigation under different active management schemes. Finally a modified IEEE 123 case is used to testify the proposed accommodation model, the results show that the active management can largely increase the IDG accommodation and penetration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1444-1452
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• 2009

Probabilistic distribution functions based data generation method is very effective. Probabilistic distribution functions are defined under the assumption that daily routine contexts are mainly depended on a time-based schedule. However, daily life contexts are frequently determined by previous contexts because contexts have consistency and/or sequential flows. In order to refect previous contexts effect, a situation propagation network is proposed in this paper. As proposed situation propagation network make parameters of related probabilistic distribution functions update, generated contexts can be more realistic and natural. Through the simulation study, proposed context-data generation model generated general outworker's data about 11 daily contexts at home. Generated data are evaluated with respect to reduction of ambiguity and confliction using newly defined indexes of ambiguity and confliction of sequential contexts. In conclusion, in case of combining situation propagation network with probabilistic distribution functions, ambiguity and confliction of data can be reduced 6.45% and 4.60% respectively.