• Journal of Information Technology Services
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• v.21 no.6
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• pp.117-125
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• 2022

In respond to climate change caused by global environmental problems, countries around the world are actively promoting the advancement of new electricity industries. The new energy business is being applied to energy storage systems (ESS), electric vehicle charging business, and power demand response using cutting edge technologies. In 2022, the Korean government is also establishing a policy stance to foster new energy industries and making efforts to improve its responsiveness to power demand response with the innovative technologies. In Korea, attempts to commercialize energy power are also being made in the private and public sectors to control energy power in houses, buildings, and industries. For example, private companies, local governments, and central government are making all-out efforts to develop new energy industry models through joint investment. There are forms such as establishing energy-independent facilities by region, establishing an electric vehicle charging system, controlling urban lighting systems with Information technologies, and managing demand between power suppliers and power consumers. This study examined the business model applied with energy storage system, electric vehicle charging business, smart lighting, and power demand response based on information communication technology to examine the site where smart energy system was introduced. According to this study, company missions and government tasks are suggested to apply new energy business technologies as economical energy solutions that meet the purpose of use by region, industry, and company.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.292-294
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• 1998

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

• Advances in Energy Research
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• v.7 no.2
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• pp.85-100
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• 2020

Distributed energy resources (DERs) are essential for coping with growing multiple energy demands. A microgrid (MG) is a small-scale version of the power system which makes possible the integration of DERs as well as achieving maximum demand-side management utilization. Hence, this study focuses on the analysis of optimal power dispatch considering economic aspects in a multi-carrier microgrid (MCMG) with price-responsive loads. This paper proposes a novel time-based demand-side management in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. In conventional studies, energy consumption is optimized from the perspective of each infrastructure user without considering the interactions. Here, the interaction of energy system infrastructures is considered in the presence of energy storage systems (ESSs), small-scale energy resources (SSERs), and responsive loads. Simulations are performed using GAMS (General Algebraic modeling system) to model MCMG, which are connected to the electricity, natural gas, and district heat networks for supplying multiple energy demands. Results show that the simultaneous operation of various energy carriers, as well as utilization of price-responsive loads, lead to better MCMG performance and decrease operating costs for smart distribution grids. This model is examined on a typical MCMG, and the effectiveness of the proposed model is proven.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.8
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• pp.13-22
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• 2018

The purpose of this study was to consider the energy generation of the building as well as the energy demand of the building in terms of zero energy building design. The reason why the zero energy building viewpoint should be discussed is that direction of the building, heat transfer rate of the building, and the S/V ratio of the building are variables related to energy demand and solar panels installed on the building roof and building envelope are variables related to energy generation. This study proceeded as follows; Firstly, the simulation model of large office and elementary school has the same mutual volume and total floor area, and the each floor area and number of floors are adjusted so that the S/V ratio is different. To the next, the energy demand and energy generation of the simulation model were derived based on the meteorological data of Seoul, Daejeon, Busan. Finally, energy demand, energy generation, and final energy demand were compared with heat transfer rate, S/V ratio, building type, region, and orientation. The results of this study is that consideration of solar power generation in terms of energy generation should be taken into consideration at the same time in consideration of the heat transfer rate, the shape, the region and the direction of the zero energy building design.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.

• Journal of Energy Engineering
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• v.18 no.2
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• pp.114-124
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• 2009

The Ministry of Knowledge and Economy (MKE) establishes the Basic Plan for Long-Term Electricity Supply and Demand(BPE) biannually, a governmental plan for the stable electricity supply. This study investigated the effects of the electric demand forecast on the energy mix. A simplified simulation model was developed, which replaces the WASP program developed by the KPX and verified by comparing both results. Three different electric demand scenarios were devised based upon the 2005 electric demand forecast: Proper, 5 % higher, and 5% lower. The simplified model calculates the energy mix for each scenario of the year 2005. Then it calculates the energy mix for the proper electric demand forecast of the year 2007 using the energy mixes of the three scenarios as the initial conditions, so that it reveals the effect of electric demand forecast of the previous BPE on the energy mix of the next BPE. As the proper electric demand forecasts of the year 2005 and 2007 are the same, there is no change in the previous and the next BPEs. However when the electric demand forecasts were 5% higher in the previous BPE and proper in the next BPE, some of the planned power plant construction in the previous BPE had to be canceled. Similarly, when the electric demand forecasts were 5% lower in the previous BPE and proper in the next BPE, power plant construction should be urgently increased to meet the increased electric demand. As expected the LNG power plants were affected as their construction periods are shorter than coal fired or nuclear power plants. This study concludes that the electric demand forecast is very important and that it has the risk of long term energy mix.

• Korean Journal of Chemical Engineering
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• v.36 no.1
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• pp.12-20
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• 2019

An energy storage system consisting of a battery and a power-to-methanol (PtM) unit was investigated to develop an energy storage system for renewable energy systems. A nonlinear programming model was established to optimize the energy storage system. The optimal installation capacities of the battery and power-to-methanol units were determined to minimize the cost of the energy system. The cost from a renewable energy system was assessed for four configurations, with or without energy storage units, of the battery and the power-to-methanol unit. The proposed model was applied to the modified electricity supply and demand based on published data. The results show that value-adding units, such as PtM, need be included to build a stable renewable energy system. This work will significantly contribute to the advancement of electricity supply and demand management and to the establishment of a nationwide policy for renewable energy storage.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.10
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• pp.553-558
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• 2009

Energy demand characteristics of hotel, hospital, and office building are compared to provide guidelines for combining building in community energy system design. The annual, monthly, and daily energy demand patterns for electricity, heating, hot water and cooling are qualitatively compared and important features are delineated based on the energy demand models. Key statistical values such as the mean, the maximum are also provided. Important features of the hourly demand patterns are summarized for weekdays and weekends. Substantial variations in both magnitudes and patterns are observed among the 3 building types and smart grouping or combination of building type and size is essential for a successive energy supply.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

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