• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.551-569
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• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• KEPCO Journal on Electric Power and Energy
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• 제6권3호
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• pp.315-319
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• 2020

The modern transportation and mobility sector is expected to encounter high penetration of Electric Vehicles (EVs) because EVs contribute to reducing the harmful emissions from fossil fuel-powered vehicles. With the prospective growth of EVs, sufficient and convenient facilities for fast charging are crucial toward satisfying the EVs' quick charging demand during their trip. Therefore, the Fast Electric Vehicle Charging Stations (FECS) will be a similar role to gas stations. In this paper, we study a charging scheduling problem for the FECS with solar photovoltaic (PV) and an Energy Storage System (ESS). We formulate an optimization problem that minimizes the operational costs of FECS. There are two cost and one revenue terms that are buying cost from main grid power, ESS degradation cost, and revenue from the charging fee of the EVs. Simulation results show that the proposed scheduling algorithm reduces the daily operational cost by effectively using solar PV and ESS.

• 전기학회논문지
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• 제65권4호
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• pp.547-554
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• 2016

Analyze the customer daily load patterns, be used to determine the optimal charging and discharging schedule which can minimize the electrical charges through the battery energy storage system(BESS) installed in consumers is an object of this paper. BESS, which analyzes the load characteristics of customer and reduce the peak load, is essential for optimal charging and discharging scheduling to save electricity charges. This thesis proposes optimal charging and discharging scheduling method, using particle swarm optimization (PSO) and penalty function method, of BESS for reducing energy charge. Since PSO is a global optimization algorithm, best charging and discharging scheduling can be found effectively. In addition, penalty function method was combined with PSO in order to handle many constraint conditions. After analysing the load patterns of target BESS, PSO based on penalty function method was applied to get optimal charging and discharging schedule.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.810-819
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• 2016

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.

• 한국산업융합학회 논문집
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• 제26권6_1호
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• pp.985-991
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• 2023

This study proposes a solution to the voltage drop in electric vehicle chargers, due to the parasitic resistance and inductance of power cables when the chargers are separated by large distances. A method using multi-level electric vehicle chargers that can output power in stages, without installing an additional energy supply source such as a reactive power compensator or an energy storage system, is proposed. The voltage drop over the power cables, to optimize the charging scheduling, is derived. The obtained voltage drop equation is used to formulate the constraints of the optimization process. To validate the effectiveness of the obtained results, an optimal charging scheduling is performed for each period in a case study based on the assumed charging demands of three connected chargers. From the calculations, the proposed method was found to generate an annual profit of $20,800 for a $12,500 increase in installation costs.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.60-61
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• 2019

Drones are opening new horizon as a major Internet-of-Things (IoT) player which is a network of objects. Drone needs to charge itself during providing services from the charging stations. If there are lots of drones and one charging station, then it is a critical situation to decide which drone should get charged first and make order of priorities for drones to get charged sequentially. In this paper, we propose an efficient scheduling algorithm for drone power charging (ESADPC), in which charging station would have a scheduler to decide which drone can get charged earlier among many other drones. Simulation results have showed that our algorithm reduces the deadline miss ration and turnaround time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.4988-5012
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• 2019

As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs). Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제8권12호
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• pp.277-286
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• 2019

무인항공기는 사물인터넷 분야에서 중요한 발명중의 하나이며 많은 응용에서 사용되고 있다. 특히 소형 무인항공기(드론)는 배터리로 동작을 하기 때문에 서비스 도중 또는 서비스간에 충전소에서 배터리 충전과 배터리 교체가 필요하다. 배터리 충전소가 제한된 상황에서 먼저 충전할 드론을 스케쥴링하고 충전된 배터리를 할당하는 문제는 중요하다. 본 논문에서는 효율적인 드론의 배터리 충전 스케쥴링 알고리즘을 제안하였다. 드론을 위한 배터리 충전 스케쥴링 알고리즘의 기본 아이디어는 실시간처리 환경에서 마감시간을 만족하기 위하여 마감시간을 고려함(EDF)과 동시에 대기시간을 줄이기 위해서 충전시간을 동시에 고려(SJF)하였다. 즉, 마감시간이 짧을수록 그리고 충전시간이 짧을수록 높은 우선순위를 부여하여 마감시간 준수율을 높이고 평균 대기시간을 줄임으로서 결과적으로 마감시간 준수율향상과 대기시간 단축이라는 두가지 측면을 동시에 만족할 수 있는 기법을 고려하였다. 이미 충전된 배터리의 할당에서는 충전시간이 길수록 높은 우선순위를 배정하므로서 평균 대기시간을 줄일 수 있다. 시뮬레이션 결과 제안 알고리즘을 이용하여 마감시간 준수율과 평균 대기시간 측면에서 기존 알고리즘(EDF와 SJF)과 비교하여 좋은 성능을 보임을 확인하였다. 시뮬레이션 결과를 바탕으로시스템 파라메터와 사용자 요구사항에 따라 배터리 충전 스케쥴링과 배터리 할당 알고리즘을 합리적으로 선택할 수 있다.

• KEPCO Journal on Electric Power and Energy
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• 제6권1호
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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.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1999년도 추계학술대회 논문집
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• pp.100-107
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• 1999

무인 운반차량 시스템 (AGV System) 의 이용도가 날로 증가함에 따라 시스템의 최적화 및 운영 방법에 관한 많은 연구가 진행되고 있다. 이에 본 연구에서는 AGV System에서 사용하는 Routing 및 Scheduling 정책들을 연구하고 이를 개선할 수 있는 새로운 방안을 모색한 후, 컴퓨터 모델링 기법을 이용한 보다 객관적인 시뮬레이션을 수행하여 최적의 AGV System과 그에 적합한 운영 정책을 제시하는데 그 목적이 있다. 따라서 본 논문은 크게 AGV Routing 과 Scheduling에 관한 연구로 나누어진다. AGV Routing은 AGV의 이동경로를 설정하는 것으로서 충돌 방지 (Collision Avoidance)와 최단경로 탐색 (Minimal Cost Path Find) 이라는 두 개의 주요 알고리즘으로 이루어진다. AGV Scheduling 은 장비의 공정시간과 AGV의 Loading/Unloading, Charging 시간으로 인해 불규칙한 Event 가 일어났을 경우 AGV 각각의 Jop을 알맞게 선정해주는 정책을 말하며 일반적으로 AGV Selection Rule, Charging Rule이 여기에 속한다. 본 연구에서는 이러한 알고리즘들이 반영된 AGV System을 컴퓨터 모델로 구축하여, 기존 시스템에서 사용되고 있는 여러 운영 정책들의 문제점들을 분석하였으며, Multiple AGV System을 최적화 시키는 운영 정책이 보다 객관적으로 제시되었다.