• Smart Media Journal
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• v.9 no.1
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• pp.67-74
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• 2020

As the demand of electric vehicles has been increased recently, their related industries are developing. In particular, the market of electric vehicles charging infrastructure is expanding rapidly and users have much demand in convenience of electric vehicles charging site. Because an charging site using electric pole of KEPCO can make use of that installed nearby public parking lots, there are many commercial construction and convenient facilities near around it. In this situation, users can do shopping or their personal business during charging. In this paper, we proposed the design of charging platform for electric vehicles to support LBS for users to do shopping or personal business conveniently.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2220-2226
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• 2018

Electric vehicles (EVs) are significant resources for demand response (DR). Thus, it is essential for EV aggregators to quantitatively evaluate their capability for DR. In this paper, a concept of dynamic equivalent battery (DEB) is proposed as a metric for evaluating the DR performance using EVs. The DEB is the available virtual battery for DR. The capacity of DEB is determined from stochastic calculation while satisfying the charging requirements of each EV, and it varies also with time. Further, a new indicator based on the DEB and time-varying electricity prices, named as value of DEB (VoDEB), is introduced to quantify the value of DEB coupled with the electricity prices. The effectiveness of the DEB and the VoDEB as metrics for the DR performance of EVs is verified with the simulations, where the difference of charging cost reduction between direct charging and optimized bidding methods is used to express the DR performance. The simulation results show that the proposed metrics accord well with the DR performance of an EV fleet. Thus, an EV aggregator may utilize the proposed concepts of DEB and VoDEB for designing an incentive scheme to EV users, who participate in a DR program.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.196-200
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• 2013

In this paper, the design of PRT vehicle power supply system is discussed. Since there is no power feeding line facilities in PRT system under development, the PRT vehicle must have its own energy storage device on board. For the energy storage device, ultra-capacitor bank is applied due to its fast charging capability and long life time. Charging the Ultra-capacitor bank is performed by wireless inductive power transfer system. The capacitor bank is charged up in less than 10 seconds when the vehicle is traveling by passenger stations. In this paper the design of the ultra-capacitor bank and the wireless inductive power transfer system for the PRT vehicle are discussed. Tests are conducted for the both system and the result shows the efficiency of the wireless inductive power transfer system is higher than 80%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1455-1462
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• 2018

As the interests in eco-friendly energy has increased, the interests in Electric Vehicles(EVs) are increasing as well. Moreover, due to the government's economic support for EVs, penetration level of it has rapidly increased. These sharp increases, however, induce various problems in distribution system, such as voltage/frequency variations, peak demand increasement, demand control, etc. To minimize these possible matters, lots of research have conducted. Nevertheless, most of it assumed extremely important factors, such as numbers and charging patterns of EVs. It inevitably results in errors in their research, and thus make it difficult to prevent the possible matters from EVs. In this paper, therefore, we use actual EVs charging data from KEPCO, and analysis and deduction of it were conducted. The simulations were carried out for four aspect(season, region, purpose).

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.51-59
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• 2019

In this paper, we analyzed the relationship between charging operation system and electricity charges connected with charging infrastructure among data of many demonstration projects focused on electric vehicles recently. At this point in time, due to the rapid increase in demand for the electric charging infrastructure that will take place in the future, we can prepare for an upcoming era in the sense of forecasting the demand value. At the same time, demonstrating and modeling optimized system modeling centering on sites is a prerequisite. The modeling based on the existing small - scale simulation and the design of the operating system are based on the data linkage analysis. In this paper, we implemented a new optimized system modeling and introduced it as a standard format to analyze time - dependent time - divisional data for each vehicle and user in each point and node. In order to verify the efficiency of the optimization based on the data linkage analysis for the actual implemented electric car charging infrastructure and operation system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.21-28
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• 2013

The paper proposes an optimal sizing method of a customer's battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.8
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• pp.343-352
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• 2017

IEC62541, known as OPC-UA, is a standard communication protocol for Smart Grid (SG) and Smart Factory application platform. It was accepted as an IEC standard (IEC62541) in 2011 by IEC TC57, and is extending range of application as collaborating with other standrads. The government's policies to popularize EVs ("Workplace Charging Challenge"), the number of Electric vehicle which try to be charging in the factory is expected to increase. In this situation, indiscreet and uncontrolled EV charging can lead to some problems, such as excess of the peak demand capacity. Therefore, EVs, which is charging in SFs, must be monitoring and controlling to avoid and reduce peak demand. However, the standards for EVs charging differ from the standards for SFs. In other words, to increase the ease of use for drivers, and reduce risk for enterprise, we have needs of study to develop the protocols or to provide interoperability, for EVs charging in SFs. This paper deals with a EV charging management platform installing in a smart factory. And this platform can be easily integrated as part of SF management software. The main goal of this paper is to implement EV management system based on IEC61851 and IEC62541.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.81-91
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• 2012

The world has been replacing fast fossil fuels vehicles with electric vehicles(EVs) to cope with climate change. The government set a goal which EVs will be substitute at least 10% of the domestic small vehicles with EVs until 2020, and will try to build electric charging infrastructures in apartments with the revision the law of 'the housing construction standards'. In apartments the EVs charging infrastructure and parking space is, essential to accomplish the goal. But the studies on EVs demand are few. In this study, we predicted that the demand for EVs using time-series analysis of statistical data, survey results for apartments residents in the metropolitan area. As a result, the ratio of the EVs appeared to be 6~21% for the total vehicles in a rental apartments for the years 2020, 21~39% in apartments for sales. For the EVs, the maximum power required for 1,000 households in rental apartment is predicted to be about 4200 kwh on a daily basis, while the maximum power in the apartment for sales is predicted to be 7800kwh.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Journal of Energy Engineering
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• v.29 no.1
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• pp.44-51
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• 2020

The importance of ESS has been emphasized due to stabilization of power demand due to deterioration of network reliability and expansion of renewable energy sources. ESS (Energy Storage System) stores the remaining power and uses it when necessary to meet the power demand, and build the ESS system mainly in conjunction with solar and wind power. In this paper, we propose a home PV Charging Module using the Master-Slave method which is effective for low insolation. After designing the module, Fast MPPT algorithm is applied to generate the maximum output from the nonlinear output characteristics of the PV modules. The average power value for the input of PV Charging Module was 296.90 W and the output power was 289.60 W, which averaged 97.54%.