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Mobility-Aware Vehicle-to-Grid Control Algorithm in Microgrids

Authors
Ko, HaneulPack, SangheonLeung, Victor C. M.
Issue Date
7월-2018
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Vehicle-to-grid (V2G); electric vehicle (EV); microgrid; Markov decision process (MDP); reinforcement learning (RL)
Citation
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.19, no.7, pp.2165 - 2174
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume
19
Number
7
Start Page
2165
End Page
2174
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/74474
DOI
10.1109/TITS.2018.2816935
ISSN
1524-9050
Abstract
In a vehicle-to-grid (V2G) system, electric vehicles (EVs) can be efficiently used as power consumers and suppliers to achieve microgrid (MG) autonomy. Since EVs can act as energy transporters among different regions (i.e., MGs), it is an important issue to decide where and when EVs are charged or discharged to achieve the optimal performance in a V2G system. In this paper, we propose a mobility-aware V2G control algorithm (MACA) that considers the mobility of EVs, states of charge of EVs, and the estimated/actual demands of MGs and then determines charging and discharging schedules for EVs. To optimize the performance of MACA, the Markov decision process problem is formulated and the optimal policy on charging and discharging is obtained by a value iteration algorithm. Since the mobility of EVs and the estimated/actual demand profiles of MGs may not be easily obtained, a reinforcement learning approach is also introduced. Evaluation results demonstrate that MACA with the optimal and learning-based policies can effectively achieve MG autonomy and provide higher satisfaction on the charging.
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Pack, Sang heon
공과대학 (전기전자공학부)
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