• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.559-567
• /
• 2018

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

• Proceedings of the KAIS Fall Conference
• /
• 2002.11a
• /
• pp.141-144
• /
• 2002

Recently, the operation of power distribution systems has become more difficult because the peak demand load is increasing continuously and the daily load factor is getting worse and worse. Also, the consideration of deregulation and global environment in electric power industry is required. In order to overcome these problems, a study on the planning and operation in distribution systems of dispersed generating sources such as fuel cell systems, photovoltaic systems and wind power systems has been performed energetically. This study presents a method for determining an optimal operation strategy of dispersed co-generating sources, especially fuel cell systems, in the case of both only electric power supply and thermal supply as well as electric power supply. In other words, the optimal operation of these sources can be determined easily by the principle of equal incremental fuel cost and the thermal merits is evaluated quantitatively through Kuhn-Tucker's optimal conditions. In order to select the optimal locations of those sources, an priority method using the comparison of total cost at the peak load time interval is also presented. The validity of the proposed algorithms is demonstrated using a model system.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.50 no.6
• /
• pp.265-274
• /
• 2001

Recently, the operation of power systems has become more difficult because the peak demand load is increasing continuously and the daily load factor is getting worse and worse. Also, the consideration of deregulation and global environment in electric power industry is required. In order to overcome those problems, a study on the planning and operation in power systems of dispersed generating sources such as fuel cell systems, photovoltaic systems and wind power systems, has been performed energetically. This paper presents a method for determining an optimal operation strategy of dispersed co-generating sources, especially fuel cell generation systems, considering thermal supply as well as electric power supply. In other words, the optimal operation of those sources can be determined easily by the principle of equal incremental fuel cost and the thermal merit of those sources can be also evaluated quantitatively through Kuhn-Tucker's optimal conditions. In additions, an priority method using the comparison of total cost at the peak load time interval is presented in order ot select the optimal locations of those sources. The validity of the proposed algorithms is demonstrated using a model system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.2
• /
• pp.92-99
• /
• 2014

The paper is related to smart maximum power system based on program logic. Especially, this system compares the total demand power with the target power by using the signal from the digital kilo watt meter. Based on the power information by the maximum power control equipment the consumed future power is anticipated. In addition, through consumed future power the controllable target power is set, and it applies on the maximum power control equipment. User or manager would control the load efficiently through the simple programming which could control load based on the control sequence and relay. To begin with the conventional maximum power control algorithm is surveyed, and the smart maximum power control system based on program logic is used, and the new algorithm from full load to proportion shut down is proposed by using PLC program. the validity of the proposed control scheme is investigated by both simulation results.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.33 no.4
• /
• pp.278-287
• /
• 2009

Plug-in hybrid electric vehicle (PHEV) with capability of being recharged from the power-grid will reduce oil consumption. Also, the PHEV will affect the utility operations by adding additional electricity demand for charging. In this research, the power-grid impact by demand of PHEV charging is presented and the optimal charging control strategy for utility operators is proposed with simulated data. The penetration of PHEV is assumed to be 50% in the circumstances of Korean passenger car market and Korean power-grid market limitedly. To obtain smooth load shape and utilize the surplus electricity in power-grid at midnight and dawn, the peak of charging demand should be controlled to be located before 4:00 a.m., and the time slot which can supply the electricity power to PHEV should be allowed between 1:00 a.m.$\sim$7:00 a.m.

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

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.27 no.3
• /
• pp.95-99
• /
• 2013

The maximum power analysis simulator took advantage of the facilities and power consumption reduction simulator test scenario development and testing of improvement in the scenario. As a maximum demand power controller, Maximum power analysis simulator performs control and disperasion of maximum demand power by calculating base power, load forecast, and present power which are based on signal of watt-hour meter to keep the electricity under the target. In addition, various algorithms to select appropriate control methode on each of the light installations through the peak demand power is configured to management. The simulation shows the success of control power for the specified target controlled by five sequential lighting installations.

• KDI Journal of Economic Policy
• /
• v.41 no.1
• /
• pp.43-58
• /
• 2019

Since 2009, electricity consumption has developed a unique seasonal pattern in South Korea. Winter loads have sharply increased, and they eventually exceeded summer peaks. This trend reversal distinguishes these load patterns from those in the USA and the EU, where annual peaks are observed during the summer months. Using Levene's test, we show statistical evidence of a rise in temperature but a decrease in variance over time regardless of the season. Despite the overall increase in the temperature, regardless of the season there should be another cause of the increased demand for electricity in winter. With the present study using data from 1991 to 2012, we provide empirical evidence that relatively low electricity prices regulated by the government have contributed significantly to the rapid upward change in electricity consumption, specifically during the winter months in the commercial sector in Korea.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.267-278
• /
• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.12
• /
• pp.2335-2341
• /
• 2009

High technique growth of modem times and high industrial facility in consequence of buildings demand for electric power of an extensive scale with stability supply and maintenance of high quality. But, power system always have risk of network contingency. When power system break out disturbance, it circumstantially happen like uncontrolled loss of load developing from of cascading. Severely which would be raised wide area blackout, plan to prevent, which make stability through a little of load shedding and multi-level UnderVoltageLoadShdding should work. This paper presents target, sensitivity of bus voltage have choose appropriating load shedding location and load shedding decision making logic with considering rate of change of voltage have studied multi-level under voltage load shedding scheme. Calculation of rate of change of voltage applied method of least square. As a result, we are studied an dynamic analysis of 2008 summer peak data. We have been known that network analysis is a little development and developing UnderVoltageLoadShedding scheme.