• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.1956-1963
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• 2010

Studies on the development of various energy management programs and real-time bidirectional information infrastructures have been actively conducted to promote the reduction of power demands and CO2 emissions effectively. In the conventional energy management programs, the demand response program that can transition or transfer the power use spontaneously for power prices and other signals has been largely used throughout the inside and outside of the country. For measuring the effect of such demand response program, it is necessary to exactly estimate short-term loads. In this study, the power consumption patterns in both individual and group consumers were analyzed to estimate the exact short-term loads, and the relationship between the actual power consumption and seasonal factors was also analyzed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1128-1134
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• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1278-1285
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• 2018

For saving electricity bill, energy storage system(ESS) is being installed in factories, public building and commercial building with a Time-of-Use(TOU) tariff which consists of demand charge(KRW/kW) and energy charge(KRW/kWh). However, both of peak reduction and ESS special tariff are not considered in an analysis of initial cost payback period(ICPP) on ESS. Since it is difficult to reflect base rate by an amount of uncertain peak demand reduction during mid-peak and on-peak periods in the future days. Therefore, the ICPP on ESS can be increased. Based on this background, this paper presents the advanced analysis method for the ICPP on ESS. In the proposed algorithm, the representative days of monthly electricity consumption pattern for the amount of peak reduction can be found by the k­means clustering algorithm. Moreover, the total expected energy costs of representative days are minimized by optimal daily ESS operation considering both peak reduction and the special tariff through a mixed-integer linear programming(MILP). And then, the amount of peak reduction becomes a value that the sum of the expected energy costs for 12 months is maximum. The annual benefit cost is decided by the amount of annual peak reduction. Two simulation cases are considered in this study, which one only considers the special tariff and another considers both of the special tariff and amount of peak reduction. The ICPP in the proposed method is shortened by 18 months compared to the conventional method.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.170-170
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• 2023

Providing safe and readily available water is vital to maintain public health. One of the most prevalent methods to prevent the spread of waterborne diseases is applying chlorine injection to the treated water before distribution. During the water transmission and distribution, the chlorine will experience a reduction, which can imply potential risks for human health if it falls below the minimum threshold. The ability to determine the appropriate initial intensity of chlorine at the source would be significant to prevent such problems. This study proposes two methods that integrate hydraulic and water quality modeling to determine the suitable intensity of chlorine to be injected into the source water to maintain the minimum chlorine concentration (e.g., 0.2 mg/l) at each demand node. The water quality modeling employs the first-order decay to estimate the rate of chlorine reduction in the water. The first method utilizes a backtracking algorithm to trace the path of water from the demand node to the source during each time step, which helps to accurately determine the travel time through each pipe and node and facilitate the computation of time-dependent chlorine decay in the water delivery process. However, as a backtracking algorithm is computationally intensive, this study also explores an alternative approach using a water age. This approach estimates the elapsed time of water delivery from the source to the demand node and calculate the time-dependent reduction of chlorine in the water. Finally, this study compares the outcomes of two approaches and determines the suitable and effective method for calculating the chlorine intensity at the source to maintain the minimum chlorine level at demand nodes.

• Earthquakes and Structures
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• v.14 no.3
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• pp.203-214
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• 2018

Predictive demand and collapse fragility functions are two essential components of the probabilistic seismic demand analysis that are commonly developed based on statistics with enormous, costly and time consuming data gathering. Although this approach might be justified for research purposes, it is not appealing for practical applications because of its computational cost. Thus, in this paper, Bayesian regression-based demand and collapse models are proposed to eliminate the need of time-consuming analyses. The demand model developed in the form of linear equation predicts overall maximum inter-story drift of the lowto mid-rise regular steel moment resisting frames (SMRFs), while the collapse model mathematically expressed by lognormal cumulative distribution function provides collapse occurrence probability for a given spectral acceleration at the fundamental period of the structure. Next, as an application, the proposed demand and collapse functions are implemented in a seismic fragility analysis to develop fragility and consequently seismic demand curves of three example buildings. The accuracy provided by utilization of the proposed models, with considering computation reduction, are compared with those directly obtained from Incremental Dynamic analysis, which is a computer-intensive procedure.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.44-48
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• 2015

The smart grid is a next-generation power grid to create a new value-added information technology. Power providers and consumers exchange information in real-time bi-directional, and optimize energy efficiency with using the smart grid. This paper describes the concept of demand response of the communication system used in the protocol, implementation of demand response systems with demand response scenarios for power reduction through the air conditioning control.

• Food Science and Industry
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• v.50 no.4
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• pp.2-11
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• 2017

The external factors of global flavor market include the world economy, population growth, urbanization, consumer spending, raw material availability, pricing, and regulatory issues. And the internal factors as product pricing and technology development may lie the flavor house's competitiveness and cost considerations. In developing countries, rising preference for packaged food and beverages and increasing personal expenditures will drive demand for flavor market. Increasing consumer demand for natural products, driven in part by health concerns and a desire for transparency in labeling will impact the demand. The increasing demand for salt and sugar reduction will boost demand for flavors to maintain the overall taste. The use of quality and innovative flavors in the beverages and the multifunctional flavors has a positive influence on the global market. The global flavor industry has the presence of several drivers and positive trends, with its future expected to be promising.

• Advances in aircraft and spacecraft science
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• v.10 no.2
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• pp.107-125
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• 2023

Drag reduction is significant research in aircraft design due to its effect on the cost of operation and carbon footprint reduction. Aircraft currently use conventional solid winglets to reduce the induced drag, adding extra structural weight. Fluidic on-demand winglets can effectively reduce drag for low-speed flight regimes without adding any extra weight. These utilize the spanwise airflow from the wingtips using hydraulic actuators to create jets that negate tip vortices. This study develops a computational model to investigate fluidic on-demand winglets. The well-validated computational model is applied to investigate the effect of injection velocity and angle on the aerodynamic coefficients of a rectangular wing. Further, the turbulence parameters such as turbulent kinetic energy (TKE) and turbulent dissipation rate are studied in detail at various velocity injections and at an angle of 30°. The results show that the increase in injection velocity shifted the vortex core away from the wing tip and the increase in injection angle shifted the vortex core in the vertical direction. Further, it was found that a 30° injection is efficient among all injection velocities and highly efficient at a velocity ratio of 3. This technology can be adopted in any aircraft, effectively working at various angles of attack. The culmination of this study is that the implementation of fluidic winglets leads to a significant reduction in drag at low speeds for low aspect ratio wings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1339-1346
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• 2011

This paper is developed to demand management scenario of energy consumption efficiency improvement, electricity generation efficiency improvement, network efficiency improvement, change of distribution ratio, movement of energy source, change of heating system, put of CHP to quantitatively assess to impact on energy use of demand management at the national level. This scenario can be applied Energy System Management model was developed based on Energy Balance Flow. In addition, effect analysis through built demand management scenario was quantitatively evaluated integrated demand management effectiveness of energy cost saving, CO2 emission reduction and energy savings of national level by calculating to primary energy source usage change in terms of integration demand management effect more often than not a single energy source separated electricity, heat and gas.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).