• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.402-405
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• 1991

This paper represents a new method for computing reliability indices by using Large Deviation method which is one of the probabilistic production cost simulations. The reliability measures are based on the models used for the loads and for the generating unit failure states. In computing these measures it has been tacitly assumed that the values of all parameters in the models are precisely known. In fact, however, some of these values must often be chosen with a considerable degree of uncertainty involved. This is particularly true for the forecast peak loads in the load model, where there is an inherent uncertainty in the method of forecasting, which are frequently based on insufficient statistics. In this paper, the effect of load forecasting uncertainty on the LOLP(Loss of Load Probability), is investigated. By applying the Large Deviation method to the IEEE Rilability Test System, it is verified that the proposed method is generally very accurate and very fast for computing system reliability indices.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.8
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.87-91
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• 1990

Regional electricity demand forecasting is among the most important step for lone-term investment and power supply planning. This study presents a regional electricity forecasting model for Korean power system. The model consists of three submodels, regional economy, regional electricity energy demand, and regional peak load submodels. A case study is presented.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.61-64
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• 2001

The electric power load during the peak time in summer is strongly affected by cooling load. which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data approached to the actual data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.27-35
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• 2019

There is a growing interest in various microgrid solutions that supply electricity 24 hours a day to off-grid areas where are not connected with the main grid, and Korea has many positive effects by constructing overseas microgrids as a country operating the emission trading scheme. Since it is not clear how to obtain load curves that is one of the inputs of the HOMER used to design a microgrid optimization plan, or it is necessary to examine whether electricity is supplied to the peak load level of the areas where have not received the electricity benefits from the viewpoint of the demand management, a methodology should be developed to know the load composition ratio and the shape of the daily load curve. In this paper, the relative coefficient and average load information for each load group obtained from the survey are used besides peak load and total average load. A mathematical model is proposed to derive the load composition ratio in the form of a Quadratic Programming and the load forecasting is performed using simple linear regression with future indicators. The effectiveness of the proposed method is confirmed for the Philippine island region supported by Korea Energy Agency and the Asian Development Bank.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1073-1075
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• 1999

This Paper Presents the process of load management for distribution Pole transformer at KEPCO. The purpose of this process is to establish reasonable peak load forecasting and prevention of Pole transformer damages caused by overload through the investigation of correlation coefficient for recent load characteristics. In this Paper, we newly proposed more reliable correlation coefficient using improved method and verified its reliability in various ways.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.901-922
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• 2007

To see how the electricity demand for air-conditioning responds to weather condition and what kind of weather condition works better in forecasting maximum daily electricity demand, four different regression models, which are linear, exponential, power and S-curve, are adopted. The regression outcome turns out that the electricity demand for air-conditioning is inclined to rely on the exponential model. Another major discovery of this study is that the electricity demand for air-conditioning responds more sensitively to the weather condition year after year along with the higher non-air-conditioning electricity demand. In addition, it has also been found that the discomfort index explains the electricity demand for air-conditioning better than the highest temperature.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.187-192
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• 2019

Hybrid power storage system with emergency power function for demand management and power outage minimizes the investment cost in the building of buildings and factories requiring emergency power generation facilities, We propose a new business model by developing technology that can secure economical efficiency by reducing power cost at all times. Normally, system power is supplied to load through STS (Static Transfer Switch), and PCS is connected to system in parallel to perform demand management. In order to efficiently operate the electric power through demand forecasting, the EMS issues a charge / discharge command to the ESS as a PMS (Power Management System), and the PMS transmits the command to the PCS controller to operate the system. During the power outage, the STS is rapidly disengaged from the system, and the PCS becomes an independent power supply and can supply constant voltage / constant frequency power to the load side. Therefore, it is possible to secure reliability through verification of actual system linkage and independent operation performance of hybrid ESS, By enabling low-carbon green growth technology to operate in conjunction with an efficient grid, it is possible to improve irregular power quality and contribute to peak load by generating renewable energy through ESS linkage. In addition, the ESS is replacing the frequency follow-up reserve, which is currently under the charge of coal-fired power generation, and thus it is anticipated that the operation cost of the LNG generator with high fuel cost can be reduced.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.312-321
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• 2018

The Korean DR market proposes suppression of peak demand under reliability crisis caused a natural disaster or unexpected power plant accidents as well as saving power plant construction costs and expanding amount of reserve as utility's perspective. End-user is notified a DR event signal DR execution before one hour, and executes DR based on requested amount of load reduction. This paper proposes a DR energy management algorithm that can be scheduled the optimal operations of chiller system and ESS in the next day considering the TOU tariff and DR scheme. In this DR algorithm is divided into two scheduling's; day-ahead operation scheduling with temperature forecasting error and operation rescheduling on DR operation. In day-ahead operation scheduling, the operations of DR resources are scheduled based on the finite number of ambient temperature scenarios, which have been generated based on the historical ambient temperature data. As well as, the uncertainties in DR event including requested amount of load reduction and specified DR duration are also considered as scenarios. Also, operation rescheduling on DR operation day is proposed to ensure thermal comfort and the benefit of a COB owner. The proposed method minimizes the expected energy cost by a mixed integer linear programming (MILP).