• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.9-14
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• 2018

For an efficient management of electricity market and power systems, accurate forecasts for electricity demand are essential. Since there are many factors, either known or unknown, determining the realized loads, it is difficult to forecast the demands with the past time series only. In this paper we perform a cluster analysis on electricity demand data collected from Jan. 2000 to Dec. 2017. Our purpose of clustering on electricity demand data is that each cluster is expected to consist of data whose latent variables are same or similar values. Then, if properly clustered, it is possible to develop an accurate forecasting model for each cluster separately. To validate the feasibility of this approach for building better forecasting models, we clustered data with t-SNE. To apply t-SNE to time series data effectively, we adopt the dynamic time warping as a similarity measure. From the result of experiments, we found that several clusters are well observed and each cluster can be interpreted as a mix of well-known factors such as trends, seasonality and holiday effects and other unknown factors. These findings can motivate the approaches which build forecasting models with respect to each cluster independently.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.391-395
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• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• 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.

• Journal of Energy Engineering
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• v.21 no.1
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• pp.81-85
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• 2012

Electricity is the basic building block of economic development, and constitutes one of the vital infra-structural inputs in socio-economic development. The demand for electricity has been increasing due to extensive urbanization, industrialization, and a rise in the standard of living, as is the case with residential electricity consumption. This paper attempts to estimate the consumer surplus and the economic value of the residential consumption of electricity in Seoul to assist in decision-making in electricity management. The estimated consumer surplus represents the value of the area under the demand curve, above the actual price that is paid for residential electricity consumption. The estimated annual consumer surplus and economic value for the year 2005 amount to 2,144.7 and 3,727.4 billion won, respectively. The estimates per kWh were 184.9 and 316.0 won, respectively, which imply that the consumer surplus and the economic value of residential electricity consumption significantly outweigh the average price of electricity in 2005 of 91.1 won per kWh.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1181-1186
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• 2017

The purpose of Demand Response is to reduce the cost of excessive resources and equipment by spontaneous load reductions at peak loads. Having enough power consumers participating in these schemes is key to achieving the goal. Demand Response Aggregator (DRA) is responsible for recruiting demand resources and managing them to participate in reducing the load. DRAs change the price elasticity of demand functions by providing incentives to demand response, thereby affecting price formation in the electricity market. In this paper, this process is modeled to analyze the relationship between DRA's strategic bidding and market outcomes and load reductions. It analyzes the results by applying to competition between DRAs, competition between DR and Gencos, and coexistence of DR load and non-DR load. It is noteworthy that we have found a phenomenon called the Balloon Effect.

• 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.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.403-411
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• 2005

At present, the Cournot model is one of the most commonly used theories to analyze the gaming situation in an oligopoly type market. However, several problems exist in the successful application of this model to the electricity market. The representative one is obtaining the inverse demand curve able to be induced from the relationship between market price and demand response. In the Cournot model, each player offers their generation quantity to obtain maximum profit, which is accomplished by reducing their quantity compared with available total capacity. As stated above, to obtain the probable Cournot equilibrium to reflect the real market situation, we have to induce the correct demand function first of all. Usually the correlation between price and demand appears over the long-term through statistical data analysis (for example, regression analysis) or by investigating consumer utility functions of several consumer groups classified as residential, industrial, and commercial. However, the elasticity has a tendency to change continuously according to the total market demand size or the level of market price. Therefore it should be updated as the trading period passes by. In this paper we propose a method for inducing and updating this price elasticity of demand function for more realistic market equilibrium.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1851-1863
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• 2014

Many countries are increasing their investments in smart grid technology to enhance energy efficiency, address climate change, and trigger a green energy revolution. In addition to these goals, Korea also seeks to promote national competitiveness, prepare for the growth of the renewable energy industry, and export industrialization through its strategic promotion of the smart grid. Given its inherent representativeness for Korean implementation of the smart grid and its growth potential, Jeju Island was selected by the Korean government as the site for smart grid testing in June 2009. This paper presents a new design for the electricity market and an operational scheme for testing Smart Electricity Services in the Jeju smart grid demonstration project. The Jeju smart grid test-bed electricity market is constructed on the basis of day-ahead and real-time markets to provide two-way electricity transaction environments. The experience of the test-bed market operation shows that the competitive electricity market can facilitate the smart grid deployment in Korea by allowing various demand side resources to be active market players.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• 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.