• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.113-115
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• 2004

This year korea power system had recorded highest peak load for 6 times and finally it made new peak load 51,264MW at July 29th 3:00 PM. The new peak load is increased 8.2% from the last year peak load 47,385MW and korea power system entered 50,000MW load era. The Korea Power Exchange (KPX) snapped power system data at the peak load time using state estimation function in the EMS. And authors converted the power system data at peak load to PSS/E power flow format. Using this PSS/E peak load power flow data, this paper explains demand analysis result shun capacitor operation, voltage distribution at the peak load. And the paper shows the simulation result of 2 contingency analysis using the snapped PSS/E peak load data.

• Journal of the Korean Solar Energy Society
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• v.31 no.4
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• pp.1-8
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• 2011

In this paper, inverse building modeling was applied to building perimeter zones which have different window orientation. Two test zones of east-facing and west-facing zones in ERS(Energy Resource Station) building, which is representative of small commercial building, was used to test performance of cooling load calculation and peak cooling load reduction. The dynamic thermal load model for the east and west zone was validated using measured data for the zones and then it was used to investigate the effect of peak cooling load reduction by adjustment of indoor cooling temperature set points during on-peak time period. For the east zone, the peak load can be reduced to about 60% of the peak load for conventional control even without any precooling. For the west zone, PLR is nearly independent of the start of the on-peak period until a start time of 1pm. Furthermore, PLR has a small dependence on the precooling duration. Without any precooling, the peak cooling load can be reduced to about 35% of the peak load associated with conventional control.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.22 no.3
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• pp.257-264
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• 2012

Objectives: This study's aims were to evaluate the effects of load center of gravity within an object lifted and feet placements on peak EMG amplitude acting on bilateral low back muscle groups, and to suggest adequate foot strategies with an aim to reducing low back pain incidence while lifting asymmetric load. Methods: The hypotheses that asymmetric load imposes more peak EMG amplitude on low back muscles contralateral to load center of gravity than symmetric load and maximum peak EMG amplitude out of bilateral ones can be relieved by locating one foot close to load center of gravity in front of the other were established based on biomechanics including safety margin model and previous researches. 11 male subjects were required to lift symmetrically a 15.8kg object during 2sec according to each conditions; symmetric load-parallel feet (SP), asymmetric load-parallel feet (AP), asymmetric load-one foot contralateral to load center of gravity in front of the other (AL), and asymmetric load-one foot ipsilateral to load center of gravity in front of the other (AR). Bilateral longissimus, iliocostalis, and multifidus on right and left low back area were selected as target muscles, and asymmetric load had load center of gravity 10cm deviated to the right from the center in the frontal plane. Results: Greater peak EMG amplitude in left muscle group than in right one was observed due to the effect of load center of gravity, and mean peak EMG amplitudes on both sides was not affected by load center of gravity because of EMG balancing effect. However, the difference of peak EMG amplitudes between both sides was significantly affected by it. Maximum peak EMG amplitude out of both sides and the difference of peak EMG amplitude between both sides could be reduced with keeping one foot ipsilateral to load center of gravity in front of the other while lifting asymmetric load. Conclusions: It was likely that asymmetric load lead to the elevated incidence of low back pain in comparison with symmetric load based on maximum peak EMG amplitude occurrence and greater imbalanced peak EMG amplitude between both sides. Changing feet positions according to the location of load center of gravity was suggested as one intervention able to reduce the low back pain incidence.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.1
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• pp.20-27
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• 2001

In this paper, we propose a method for estimating the peak load of pole-transformer in summer season considering the degree of cooling load possession in customer. The cooling load of customer is selected as the most reliable parameter of peak load in summer season. The proposed estimation method is restricted to the aspect of load management for pole-transformer. The main concept of proposed method is that the error of peak load estimation using load regression equation reduces with considering the degree of cooling load possession in customer. We propose an index for estimation of cooling load possession in each customer. The proposed index is defined as cooling load possession in customer (CLPC) and obtained from the increment of monthly electric energy. The membership function for deciding the uncertainty of cooling load possession in customer is used. The database of pole-transformer in Korea Electric Power Corporation (KEPCO) is used for case studies. Through the case studies, we verify that the proposed method reduces the error of peak load estimation than the conventional method in domestic.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.404-410
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• 2016

This paper proposed a program of an energy storage system(ESS) for peak shaving of high-speed railway substations The peak shaving saves cost of equipment and demand cost of the substation. To reduce the peak load, it is very important to know when the peak load appears. The past data based load profile forecasting method is easy and applicable to customers which have relatively fixed load profiles. And an optimal scheduling method of the ESS is helpful in reducing the electricity tariff and shaving the peak load efficiently. Based on these techniques, MS. NET based peak shaving program is developed. In case study, a specific daily load profile of the local substation was applied and simulated to verify performance of the proposed program.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1260-1268
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• 2012

Recently, lack of power reserve margin was observed quite often. In this paper, we studied the method to secure power source for a short time, to cut the utility power peak load, and to reduce the users electricity bills. Emergency diesel generator of an office building is to be converted into a dual-fuel engine generator which is responsible for a portion of the peak load. Compared to the conventional diesel fuel generator, the proposed dual-fuel engine is able to reduce the generation power cost by dual-fuel combustion, and it also mitigates the building's utility power peak load by charging the building's peak load. If the dead resources (a group of emergency dual-fuel engine generators), as a Virtual Power Plant, are operating in peak time, we can significantly reduce future large power development costs. We investigated the current general purpose electricity bills as well as the records of the building electric power usage, and calculated diesel engine generator renovation costs, generation fuel costs, driving conditions, and savings in electricity bills. The proposed dual-fuel engine generation method reduces 18.1% of utility power peak load, and turned out to be highly attractive investment alternative which shows more than 27% of IRR, 76 million won of NPV, and 20~53 months of payback periods. The results of this study are expected to be useful to developing the policy & strategy of the energy department.