• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.940-945
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• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.755-765
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• 2015

A thermostatically controlled load (TCL) can be one of the most appropriate resources for demand response (DR) in a smart grid environment. DR capability can be effectively implemented in a TCL with various intelligent control methods. However, because traditional on-off control is still a commonly used method in a TCL, it is useful to develop a method for adding DR capability to the TCL with an existing on-off controller. As a specific realization of supervisory control for implementing DR capability in the TCL, two methods are proposed - a method involving the changing of a set point and a method involving the paralleling of an identified system without delay. The proposed methods are analyzed through the simulations with an electric heater for different power consumption levels in the on-state. Considerable cost benefit can be achieved with the proposed methods when compared with the case without DR. In addition, the observations suggest that a medium power consumption level, instead of the maximum power, in the on-state should be used for consistently obtaining the cost benefit without severe temperature deviation from the specified temperature range for DR.

• Korean Management Science Review
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• v.26 no.1
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1454-1470
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• 2014

This paper propose a new power conditioner topology with intelligent power management controller that integrates multiple renewable energy sources such as solar energy, wind energy and fuel cell energy with battery backup to make best use of their operating characteristics and obtain better reliability than that could be obtained by single renewable energy based power supply. The proposed embedded controller is programmed for maintaining a constant voltage at PCC, maximum power point tracking for solar PV panel and WTG and power flow control by regulating the reference currents of the controller on instantaneous basis based on the power delivered by the sources and load demand. Instantaneous variation in reference currents of the controller enhances the controller response as it accommodates the effect of continuously varying solar insolation and wind speed in the power management. The power conditioner uses a battery bank with embedded controller based online SOC estimation and battery charging system to suitably sink or source the input power based on the load demand. The simulation results of the proposed power management system for a standalone solar/WTG/fuel cell fed hybrid power supply with real time solar radiation and wind velocity data collected from solar centre, KEC for a sporadically varying load demand is presented in this paper and the results are encouraging in reliability and stability perspective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.11-22
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• 2000

The main objective of this research is to analyze an order point and an order quantity of a distribution center and each branch to attain a target service level in multi-level inventory distribution system. In case of product item, we use the item with low volume of average monthly demand. Under the continuous review method, the distribution center places a particular order quantity to an outside supplier whenever the level of inventory reaches an order point, and receives the order quantity after elapsing a certain lead time. Also, each branch places an order quantity to the distribution center whenever the level of inventory reaches an order point, and receives the quantity after elapsing a particular lead time. When an out of stock condition occurs, we assume that the item is backordered. For considering more realistic situations, we use generic type of probability distribution of lead times. In the variable lead time model, the actually achieved service level is estimated as the expected service level. Therefore, this study focuses on the analysis of deciding the optimal order point and order quantity to achieve a target service level at each depot as a expected service level, while the system-wide inventory level is minimized. In addition, we analyze the order level as a maximum level of inventory to suggest more efficient way to develop the low demand item model.

• Korean Journal of Organic Agriculture
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• v.30 no.2
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• pp.151-170
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• 2022

The demand for commercial kimchi has increased continuously for the past 20 years due to the increase in eating out demand. Although Korean kimchi industry has expanded significantly, it is still small and a large portion of domestic demand is dependent on Chinese kimchi. Chinese kimchi imports has markedly increased over the last 20 years. However, kimchi imports from China in 2021 significantly reduced due to the recently released video showing a naked man making Kimchi. Korean government has decided to apply HACCP to all imported Kimchi from October 2021 in order to improve the safety of imported kimchi. This study analyzed the effect of changes in the amount of kimchi imports due to the introduction of HACCP on the kimchi industry by using a dynamic partial equilibrium model. According to the analysis result, if imports decreased by 20% compared to the Baseline, domestic kimchi production increased from 1.8% to a maximum of 4.8%, but kimchi consumption decreased from 3.1% to 5.2%. In particular, consumption away from home decreased from 3.3% to 5.7%. It is expected that the results of this study would be used as useful data in the decision-making process of market participants and policy makers related to the kimchi industry.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• International Journal of Human Ecology
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• v.4 no.2
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• pp.77-87
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• 2003

This paper tried to construct statistical and econometric models on the basis of economic theory in order to discuss the issue of statistical efficiency and unbiasedness including the sample selection bias correcting problem. Comparative analytical tool were one stage Tobit of Maximum Likelihood estimation and Heckman's two-step Tobit of Ordinary Least Squares. The results showed that the adequacy of model for the analysis on demand and choice, we believe that there is no big difference in explanatory variables between the first selection model and the second linear probability model. Since the Lambda, the self- selectivity correction factor, in the Type II Tobit is not statistically significant, there is no self-selectivity in the Type II Tobit model, indicating that Type I Tobit model would give us better explanation in the demand for and choice which is less complicated statistical method rather than type II model.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.349-357
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• 2002

This research is basically deals with an inventory distribution system with several regional sales branches. Under the continuous review policy, each sales branch places an order to its supplier whenever on hand plus on order inventory falls on the order point, and the order quantity is received after elapsing a certain lead time. This research first shows the method how to apply the product with low lever of demand into the continuous review policy. For the application, we use an order level as the maximum level of inventory during an order cycle. Also we analyze the lost sales case as a customer behavior. Further we use variable demands and variable lead times for more realistic situation. Based on the above circumstances, the research mainly discusses those methods to decide the optimal order level, order point, and order quantity for each sales branch which guarantees the system wide goal level of service, while keeping the minimum level of the system wide total inventory.

• Journal of Power Electronics
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• v.9 no.6
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• pp.919-928
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• 2009

Microgrids (MGs) are typically comprised of distributed generators (DGs) including renewable energy sources (RESs), storage devices and controllable loads, which can operate in either interconnected or isolated mode from the main distribution grid. This paper introduces a novel dynamic programming (DP) approach to MG optimization which takes into consideration the coordination of energy supply in terms of heat and electricity. The DP method has been applied successfully to several cases in power system operations. In this paper, a special emphasis is placed on the uncontrollability of RESs, the constraints of DGs, and the application of demand response (DR) programs such as directed load control (DLC), interruptible/curtaillable (I/C) service, and/or demand-side bidding (DSB) in the deregulated market. Finally, in order to illustrate the optimization results, this approach is applied to a couple of examples of MGs in a certain configuration. The results also show the maximum profit that can be achieved.