• Current Photovoltaic Research
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• v.11 no.4
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• pp.124-133
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• 2023

In order to deploy the large-scale energy storage (ES) service in the various industry, it is very important to develop a business model with high technological and economic feasibility through detailed valuation of cost and expected benefits. In relation to this, this paper established an optimal scheduling plan for electric vehicle charging stations connected with photovoltaic (PV) and ES technologies in Korea using the distributed energy resource valuation tool and analyzed the feasibility of the project. In addition, the impact of incentives such as REC (Renewable Energy Certificate) to be given to electric vehicle charging stations in accordance with the relevant laws to be revised in the future was analyzed. As a results, the methodology presented in this paper are expected to be used in various ways to analyze the feasibility of various business models linked to renewable energy and ES technologies as well as the electric vehicle market.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.1
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• pp.59-64
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• 2020

Scheduling of electric vehicles and optimizing for charging waiting time have been critical. Meanwhile, it is challengeable to exploit the fluctuating data from electric vehicles in real-time. We introduce an optimal routing algorithm and a simulator with electric vehicles obeying the Poisson distribution of the observed information about time, space and energy-demand. Electric vehicle routing is updated in every cycle even it is already set. Also, we suggest an electric vehicle routing algorithm for minimizing total trip time, considering a threshold of the waiting time. Total trip time and charging waiting time are decreased 34.3% and 86.4% respectively, compared to the previous algorithm. It can be applied to the information service of charging stations and utilized as a reservation service.

• Journal of Drive and Control
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• v.20 no.1
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• pp.60-62
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• 2023

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.9-21
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• 2018

In South Korea, Jeju Island has a role as a test bed for electric vehicles (EVs). All conventional cars on the island are supposed to be replaced with EVs by 2030. Accordingly, how to effectively set up EV charging stations (EVCSs) that can charge EVs is an urgent research issue. In this paper, we present a case study on planning the locations of EVCS for Jeju Island, South Korea. The objective is to determine where EVCSs to be installed so as to balance the load of EVCSs while satisfying demands. For a public service with EVCSs by some government or non-profit organization, load balancing between EVCS locations may be one of major measures to evaluate or publicize the associated service network. Nevertheless, this measure has not been receiving much attention in the related literature. Thus, we consider the measure as a constraint and an objective in a mixed integer programming model. The model also considers the maximum allowed distance that drivers would detour to recharge their EV instead of using the shortest path to their destination. To solve the problem effectively, we develop a heuristic algorithm. With the proposed heuristic algorithm, a variety of numerical analysis is conducted to identify effects of the maximum allowed detour distance and the tightness of budget for installing EVCSs. From the analysis, we discuss the effects and draw practical implications.

• Journal of the Korean Society of Safety
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• v.35 no.6
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• pp.25-31
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• 2020

Boiling Liquid Expanding Vapor Explosion(BLEVE) can cause not only economic damage to the plant but also serious casualties. LPG accidents account for 89.6 percent of all accidents caused by gas leaks in Korea over the past nine years, while casualties from accidents also account for 73 percent of all accidents, according to statistics from the Korea Gas Safety Corporation. In addition, a potential explosion and a fire accident from one LPG storage tank may affect the nearby storage tanks, causing secondary and tertiary damage (domino effect). The safety distance standards for LPG used by LPG workplaces, charging stations, and homes in Korea have become stricter following the explosion of LPG charging stations in Bucheon. The safety distance regulation is divided into regulations based on the distance damage and the risk including frequency. This study suggests two approaches to optimizing the safety distance based on the just consequence and risk including frequencies. Using the Phast 7.2 Risk Assessment software by DNV GL, the explosion overpressure and heat radiation were derived according to the distance caused by BLEVE in the worst-case scenario, and accident and damage probability were derived by considering the probit function and domino effect. In addition, the safety distance between LPG tanks or LPG charging stations was derived to minimize damage effects by utilizing these measures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.669-674
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• 2017

Due to the depletion of fossil fuels and the increase in environmental pollution, electric vehicles are attracting attention as next-generation transportation and are becoming popular all over the world. As the interest in electric vehicles and the penetration rate increase, studies on the charging infrastructure with vehicle-to-grid (V2G) technology and information technology are actively under way. In particular, communication with the grid network is the most important factor for stable charging and load management of electric vehicles. However, with the existing centralized infrastructure, there are problems when control-message requests increase and the charging infrastructure cannot efficiently operate due to slow response speed. In this paper, we propose a new charging infrastructure using mobile edge computing (MEC) that mitigates congestion and provides low latency by applying distributed cloud computing technology to wireless base stations. Through a performance evaluation, we confirm that the proposed charging infrastructure (with low latency) can cope with peak conditions more efficiently than the existing charging infrastructure.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1234-1242
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• 2013

To verify the effectiveness of the proposed system, the charges in power demand are analyzed for an AC and DC distribution system for the existing V2G concept and electric vehicle charging stations connected to a Battery Energy Storage System. In addition, since many power-converter-based chargers are operated simultaneously in an EV charging station, the change in the system harmonics when several EV chargers are connected at a single point is analyzed through simulations.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.83-90
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• 2020

This study conducted a risk assessment using the HyKoRAM program created by international joint research. Risk assessment was conducted based on accident scenarios and worst-case scenarios that could occur in the facility, reflecting design specifications of major facilities and components such as compressors, storage tanks, and hydrogen pipes in the hydrogen charging station, and environmental conditions around the demonstration complex. By identifying potential risks of hydrogen charging stations, we are going to derive the worst leakage, fire, explosion, and accident scenarios that can occur in hydrogen storage tanks, treatment facilities, storage facilities, and analyze the possibility of accidents and the effects of damage on human bodies and surrounding facilities to review safety.