• 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 Electrical Engineering and Technology
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• v.10 no.1
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• pp.64-74
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• 2015

Because of the burgeoning demand of the energy, the countries are finding sustainable solutions for these emerging challenges. Demand Side Management is playing a significant role in managing the demand with an aim to support the electrical grid during the peak hours. However, advancement in controls and communication technologies, the aggregators are appearing as a third party entity in implementing demand response program. In this paper, a detailed mathematical framework is discussed in which the aggregator acts as an energy service provider between the utility and the consumers, and facilitate the consumers to actively participate in demand side management by introducing the new concept of demand reduction bidding (DRB) under constrained direct load control. Paper also presented an algorithm for the proposed framework and demonstrated the efficacy of the algorithm by considering few case studies and concluded with simulation results and discussions.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.79-85
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• 2016

This paper presents an advanced energy demand management for buildings. It is important to aggregate a various demand side resource which is controllable on demand response environment. Previous demand side algorithm for building is mostly restricted on single building. In this paper, we suggest energy demand management algorithm for many buildings. And, this paper shows the procedure to apply suggested demand management algorithm.

• 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.

• Environmental and Resource Economics Review
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• v.24 no.1
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• pp.225-249
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• 2015

There are growing interests in the introduction of consumer's selective electricity tariff systems in order to enhance demand response in electricity market in Korea. Real time pricing (RTP) and Time of Use (TOU) are typical examples of demand response system through which electricity price is linked to real time demand. This paper adopts an agent-based model to analyze the effects of such demand system on the counsumers' electricity costs. The result shows that real time pricing system is effective to reduce electricity costs of consumers by providing more flexible tariff system, depending on each consumer's demand pattern. This finding could be used as a basis for supporting smart grid system in the presence of responsive demand environment.

• Proceedings of the KIEE Conference
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• summer
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• pp.162-164
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• 2004

Electrical power peak demand of Republic of Korea is annually growing and the peak demand has occurred in the summer. It is difficult that we handle with constructing power plants and increasing generation capacity to cope with a suddenly increased demand due to the cost problem, difficulty to find the new plant site, and the spread of the NIMBY. The alternative of the above problem is to efficiently manage demand of electrical power. Accordingly, load shedding of a section of demand side management is investigated. First we surveyed a trend of research in the domestic and overseas, for load curtailment and demand response program. After reviewing several demand response programs, the future research direction for load shedding in emergency and normal operation is introduced.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.291-297
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• 2010

A system called demand response programs (DRP) is being introduced among various countries owing to the lack of new generation capacity and the higher fuel generation cost. It is a program which provides for the end-users to select their consumption of electricity by recognizing the value of their consumption in real time. That is, Demand Response can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity or other signals. It is expected that the effects of DRP are preventing price spike, improving supply reliability and social welfare and increasing option of customers. Considering the customer's thermal comfort zone, this paper determines the most profitable combination of optimal incentives and amounts of load reduction for a retailer to maximize profits according to predicted outdoor temperatures while implementing DRP.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.417-422
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• 2004

It is the challenging task to predict seismic demand for structural design. In current seismic design provisions such as UBC, NEHRP, ATC 3-06, the seismic demand is calculated using the response spectrum with response modification factor (R). This paper investigates variability of seismic demand according to selecting the earthquake ground motion groups. Different Earthquake sets used by Miranda, Riddell and Seed selected were used in this study. Earthquake sets selected by authors include 62 sets of near field ground motion and 19 sets one pulse ground motion. Linear Elastic Response Spectrum (LERS), the variation of performance points of calculated by Capacity Spectrum Method (CSM) were considered with respect to the different sets of earthquake ground motions.

• Proceedings of the KIEE Conference
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• summer
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• pp.663-665
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• 2004

There are some demand response program which is Direct Load Control and so on in Korea. These are used to manage lack of power stability or shift peak time for shading load. It is very important not only using stability power system but controling and scheduling power system on the whole. Interruptible loads are essential resources to solve lack of energy and limit of constructing generator On recently days, Demand Response Program's reliability is recognized as ancillary or reserve service in many country. This paper presents a necessity to apply demand resource to our ancillary program. For this reason, it is introduce overseas ancillary program using load resource.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.