• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.9
• /
• pp.1577-1582
• /
• 2016

Now we are facing severe environmental issues such as global warming. Due to these, the concerns about eco-friendly energy have been increased. Kyoto protocol and Copenhagen climate change conference are circumstantial evidence of it. With these trends, the interests for the Electric Vehicles(EVs) which do not emit any harmful gases have gradually been raised. Unfortunately, however, massive connection of EVs to the power system could cause negative impacts such as voltage variations, frequency variations and increase of demand power. To prevent the mentioned issues, KEPCO adopts Time-of-Use(ToU) price for EVs charging. Nevertheless, it is important to verify the propriety of the charging system. In this paper, therefore, we used pre-introduced price elasticity concept to predict possible Demand Response(DR) on charging of EVs. And analyzed possible demand power increase according to various price elasticities. Simulation results show that given ToU based charging system would not enough to control the increase of demand power by EVs on the power system. It is concluded, therefore, additional methods and/or algorithms are required.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.4
• /
• pp.587-594
• /
• 2018

The purpose of this study is to propose a power demand volatility evaluation system based on LSTM and not to verify the accuracy of the demand module which is a core module, but to recognize the sudden change of power pattern by using deeplearning in the actual power demand monitoring system. Then we confirm the availability of the module. Also, we tried to provide a visualized report so that the manager can determine the fluctuation of the power usage patten by applying it as a module to the web based system. It is confirmed that the power consumption data shows a certain pattern in the case of government offices and hospitals as a result of implementation of the volatility evaluation system. On the other hand, in areas with relatively low power consumption, such as residential facilities, it was not appropriate to evaluate the volatility.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.13 no.4
• /
• pp.7-18
• /
• 2017

Electrical energy is one of the most important energy sources in modern society. Therefore, it is very important to control the supply and demand of electric power. However, the power consumption data needed to predict power demand may include the information about the private behavior of an individual, the analysis of which may raise privacy issues. In this paper, we propose a secure power demand forecasting method where regression analyses on power consumption data are conducted in a trusted execution environment provided by Intel SGX, keeping the power usage pattern of users private. We performed experiments using various regression equations and selected an equation which has the least error rate. We show that the average error rate of the proposed method is lower than those of the previous forecasting methods with privacy protection functionality.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.9
• /
• pp.47-54
• /
• 2009

These clays, because of world economy recession, exports decreased rapidly and manufacturing industry growth fell into negative. Industrial power consumption has been reduced about 7[%] that forms 53[%] of total load demand in Korea. And also, daily load pattern has been changed in several ways because of power consumption decrease influenced by domestic demand recession and heating power load decreased by the rise in temperature. This research analyzes, by analyzing maximum load demand, average load demand, load pattern based on relative factor, and load sensitiveness in accordance with temperature, that maximum load demand is more sensitive to atmospheric temperature than GDP growth rate and average load demand tends to be reduced according to GDP growth rate. I suppose KPX could operate the network system economically and safely by forecasting load demand in winter and summer seasons based on the results.

• Architectural research
• /
• v.18 no.2
• /
• pp.59-64
• /
• 2016

Since the massive power outages that hit across the nation in September 2011, a growing imbalance between energy supply and demand has led to a severe backup power shortage. To overcome the energy crisis which is annually repeated, a policy change for deriving energy supply from renewable energy sources and a demand reduction strategy has become essential. Buildings account for 18% of total energy consumption and have great potential for energy efficiency improvements; it is an area considered to be a highly effective target for reducing energy demand by improving buildings' energy efficiency. In this regard, retrofitting buildings to promoting environmental conservation and energy reduction through the reuse of existing buildings can be very effective and essential for reducing maintenance costs and increasing economic output through energy savings. In this study, we compared the energy reduction efficiency of national power energy consumption by unit production volume based on thermal power generation, renewable energy power generation, and initial and operating costs for a building retrofit. The unit production was found to be 13,181GWh/trillion won for bituminous coal-fired power generation, and 5,395GWh/trillion won for LNG power generation, implying that LNG power generation seemed to be disadvantageous in terms of unit production compared to bituminous coal-fired power generation, which was attributable to a difference in unit production price. The unit production from green retrofitting increased to 38,121GWh/trillion won due to the reduced energy consumption and benefits of greenhouse gas reduction costs. Renewable energy producing no greenhouse gas emissions during power generation and showed the highest unit production of 75,638GWh/trillion won, about 5.74 times more effective than bituminous coal-fired power generation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.12
• /
• pp.2221-2224
• /
• 2011

During an emergency due to a shortage of power, a load aggregator (LA) can use the demand response operation system to deploy demand response resources (DRRs) through various demand response (DR) programs. This paper introduces the demand response operation system for a load aggregator to manage various demand response resources in a smart grid environment.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.9
• /
• pp.1658-1662
• /
• 2010

This paper presents an advanced energy saving algorithm in building. It is important to aggregate a various demand side resource which is surely controllable at the peak power time to reduce the energy cost. Previous demand side algorithm appropriate for building is based on peak power. In this paper, we develop the new energy saving algorithm to reduce the quantity of power consumption. The simulation results show that the proposed tem is very effective.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.453-463
• /
• 2013

The probabilistic nature of renewable energy, especially wind energy, increases the needs for new forms of planning and operating with electrical power. This paper presents a novel approach for determining the short-term generation schedule for optimal operations of wind energy-integrated power systems. The proposed probabilistic security-constrained optimal power flow (P-SCOPF) considers dispatch, network, and security constraints in pre- and post-contingency states. The method considers two sources of uncertainty: power demand and wind speed. The power demand is assumed to follow a normal distribution, while the correlated wind speed is modeled by the Weibull distribution. A Monte Carlo simulation is used to choose input variables of power demand and wind speed from their probability distribution functions. Then, P-SCOPF can be applied to the input variables. This approach was tested on a modified IEEE 30-bus system with two wind farms. The results show that the proposed approach provides information on power system economics, security, and environmental parameters to enable better decision-making by system operators.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.1
• /
• pp.129-136
• /
• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.228-231
• /
• 2015

Recently, the electric power consumption concentrated in specific seasons and time causes the unstable power supply. To resolve this problem, smart grid has emerged as an alternative to consume the power, efficiently. Smart grid that combines ICT with the existing electrical grid includes demand response as a core technology. Demand response enable the power consumption effectively by offering a variety of informations, such as power consumption, charges expected, etc., for consumers who voluntarily participate in the electricity markets. In this paper, we design the automatic demand response systems based on SEP 2.0 for the efficient power control of home appliances in the smart grid environments.