• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.99-127
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• 2007

When the margin between available capacity and demand is thin in a liberalized electricity market, prices rise steeply and system reliability is threatened. The principal response to these circumstances is often an assumption that price spikes and electricity shortages are the result of a failure to build sufficient new supplying facilities. It is, of course, often the case that additional investments in generation and network facilities would improve reliability, and such investments are often needed. But focusing on additional generation and transmission facilities for restoring balance to the grid overlooks the essential fact that reliability is a function of the relationship between supply and demand, imposing unnecessary costs on electric system. When the relationship is out of balance, the search for solutions must consider not only investments supply-side resources but also cost-effective demand-side resources such as accelerated load management, efficiency measures, and price-responsive load programs. Integrating demand resources into electricity markets can add enormous value to the electric system, widening the capacity margin, lowering costs and enhancing system reliability at the same time. This paper studies several challenges now facing electricity markets: demand-side management-especially, economic effects of demand response, potential reliability problems, market and system operation, CBP market improvements and so on. The paper concludes with a series of policy recommendations in five areas: (i) The Effects of efficient improvement to incorporate demand responses and demand-side resources into modem electricity markets, (ii) Fosteing price based demand response and (iii) improving incentive based demand response, (iv) strengthen demand response analysis and valuation, (v) integrating demand response into resource planning and adopting enabling technologies.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1601-1607
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• 2016

Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.570-575
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• 2006

This paper develops methodology in order to consider CHP(Combined Heat and Power) capacity in the Basic Plan of Long Term Electricity Supply & Demand. We develop generating cost of CHP considering electric and heat. Also we develop mixed load duration curve which includes the electric load and heat load and then apply CHP capacity to SCM(Screening Curve Method) considering CHP feature. Accordingly, it decide the optimal CHP capacity in the Basic Plan of Long Term Electricity Supply & Demand. Also, We perform the sensitivity analysis according to cost variation.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.804-806
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• 2005

In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity markets. Conventional forced outage rates of generators are conceptually combined with maintenance outage of generators and, consequently, effective outage rates of generators are new iy defined in order to properly address the probabilistic characteristic of generation in electricity markets. Then, original market demands are classified into several distinct demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the original demand. We have found that generators have different effective outage rates values at each classified demand cluster, depending on the market situation. From this, therefore, it can be seen that electricity markets can also be classified into several groups which show similar patterns and that the fundamental characteristics of power systems can be more efficiently analyzed in electricity markets perspectives, for this classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.2
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• pp.18-26
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• 2012

Though electricity consumption amount in industry has been increased gradually, corresponding power supply show symptoms of marginal point. Importance of demand-side management from large-industries has also been raised. This paper deals with induction motor, which is one of representative examples of heavy electricity consumption utilities, to analyze potential technical capability, economic feasibility from consumers' viewpoint and demand-side management feasibility from nation-wide perspective. Nation-wide economic feasibility analysis was done through California test, which has been used as demand-side management evaluation model. This paper also describes limitation of existing high efficiency induction motor in terms of contribution to demand-side management and utilizes premium motor to calculate demand-side management contribution level and economic feasibility evaluation. Likewise, this paper emphasizes the efficiency improvement of induction motor and analyzes how much premium motor related technologies can contribute to demand-side management.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.67-86
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• 2018

This paper attempted to analyze impacts of transition in electricity generation system on a national economy and environmental level in Korea using a recursive computable general equilibrium(CGE) model. In particular, the paper presented a hybrid model combining the top-down CGE model with the bottom-up model which describes the structure of electricity production in detail. The impacts were analyzed by two policy scenarios base on the basic plan for electricity supply and demand proposed by the Korean government. As a result, the paper specifically showed that there exists a trade-off relationship in the policy-making between economic efficiency and environmental level. The paper also suggested that the transition in electricity generation system should be done more gradually and carefully.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.59-68
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• 2014

The Fukushima nuclear accident in 2011 had an extensive impact on the national electricity plans. This paper outlines alternative electricity scenarios that meet the goals of nuclear phase-out and greenhouse gas (GHG) emission reduction. This paper also analyzes the results of each scenario in respect to the electricity mix, GHG emissions, costs and employment effects. The Long-range Energy Alternatives Planning system (LEAP) model was used to simulate the annual electricity demand and supply system from 2011 to 2030. The reference year was 2009. Scenarios are reference (where existing plans are continued), A1, A2, B1, B2, and C2 (where the levels of demand management and nuclear phase-out are different). The share of renewable energy in the electricity mix in 2030 for each scenario will be increased from about 1% in 2009 to 8% in the reference scenario and from 11% to 31% in five alternative scenarios. Total cumulative cost increases up to 14% more than the reference scenario by replacing nuclear power plants with renewable energy in alternative scenarios could be affordable. Deploying enough renewable energy to meet such targets requires a roadmap for electricity price realization, expansion of research, development and deployment for renewable energy technologies, establishment of an organization dedicated to renewable energy, and ambitious targets for renewable energy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.3
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• pp.187-197
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• 2004

This paper presents a window-based load management system (LMS) developed as a decision-making tool in the competitive electricity market The developed LMS can help the users to monitor system load patterns, analyze their past energy consumption and schedule for the future energy consumption. The LMS can also provide the effective information on real-time energy/cost monitoring, consumed energy/cost analysis, demand schedule and cost-savings. Therefore. this LMS can be used to plan the optimal demand schedule and consumption strategy.