Optimal charging strategy for electric vehicles using symbolic–graphic combination principle

Zhang, Jialei; Pei, Yunqing; Shen, Jiaming; Wang, Laili; Ding, Tao

doi:10.1049/iet-gtd.2018.6831

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Unlike the ADMM-based algorithm, both the bucketsortbased and the consensus-based algorithms can effectively guarantee the power constraint in (6). Besides, the bucketsortbased algorithm needs much less the data size of communication than the consensus-based algorithm, which is displayed in Table 3.…”

Section: Algorithm Analysismentioning

confidence: 99%

Charging Strategy Unifying Spatial-Temporal Coordination of Electric Vehicles

et al. 2020

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This paper focuses on how to make a charging guidance for urgent charging electric vehicles (EVs) to fast charging stations. To avoid charging overload in the spatial-temporal scale and excessive waiting time for EV users, a charging guidance strategy based on the virtual service range is proposed. Considering the mutual influence among EVs' charging selections, the proposed strategy can unify the temporal shift and spatial shift of the charging load into the same time scale to control rather than other works only do the temporal shift. Specifically, the proposed strategy has two stages: the charging arrangement and the spatial-temporal shift. The charging arrangement stage is to determine whether an EV can be allowed to charge at a certain time and a given charging station while the spatial-temporal shift stage is to regulate the charging time or charging location. Besides, a bucketsort-based algorithm is proposed to solve the formulated problem in the charging arrangement stage. Finally, three scenarios have been designed to demonstrate the feasibility of the proposed strategy and the performance of the presented algorithm is compared with the other decentralized algorithms.INDEX TERMS Charging load, driving time, electric vehicle, spatial-temporal, waiting time.

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Section: Algorithm Analysismentioning

confidence: 99%

Charging Strategy Unifying Spatial-Temporal Coordination of Electric Vehicles

et al. 2020

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“…One is the safe operation of the power grid, which focuses on the centralized schedule of EV groups. They usually schedule the charging time and quantity of EVs to transfer the charging load according to the amount of renewable energy [4][5][6][7][8][9]. The other one is the economic dispatch of EVs which decides the charging behaviour of EVs to minimize the charging cost [10,11].…”

Section: Introductionmentioning

confidence: 99%

Stochastic charging scheduling of a parking lot with wind power: a state‐aggregation method based on Markov decision processes

Ming

Gao

Liu

et al. 2021

IET Generation Trans & Dist

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Optimal charging scheduling of electric vehicles with renewable energy could greatly save the electricity cost in parking lots, and also helps for environment protection. However, uncertainties of renewable energy and electric vehicles charging demand bring great challenges to obtaining the optimal charging policy. To this end, in this paper, a state aggregation based dynamic programming method is presented for such a stochastic charging scheduling problem. Specifically, first, a new Markov decision process based formulation is established for the multi-stage stochastic programming problem. Second, a novel stateaggregation method is proposed to relieve the dimension curse due to large state-action space without sacrificing the optimality of the charging policy. Besides, the consistency of the problem before and after state aggregation also could be verified by proposed theorems both on feasibility and optimality. Moreover, economic efficiency and computational efficiency are both improved in numerical testing, which shows the effectiveness of the proposed method.

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“…In recent years, power electrical devices have drawn more and more attention [1], [2]. Since the harmonics, produced by increasing power electrical devices, cause additional loss of motors and damage electrical equipments, harmonic filter plays a critical role in power systems [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Modified Adaptive Fuzzy Control for Power Quality Improvement

et al. 2020

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This study aims to present a modified adaptive fuzzy control (MAFC) for power quality improvement, whereby adaptive fuzzy control with parameter adaptive capability is employed to emulate optimal control. Different from conventional fuzzy control-based current control method without analytical tools to guarantee basic performance criteria, high-performance control system can be realized using Lyapunov-based adaptive fuzzy control. Also, in order to compensate the fuzzy approximation error (FAE) in conventional fuzzy control, a modified adaptive fuzzy control with adaptive compensator is developed. Unlike the existing approaches, adaptive compensator can estimate the optimal value of FAE with no need of upper bound of FAE. Simulation and experimental results verify that proposed control strategies reveal superior performance under different conditions.

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Optimal charging strategy for electric vehicles using symbolic–graphic combination principle

Cited by 8 publications

References 22 publications

Charging Strategy Unifying Spatial-Temporal Coordination of Electric Vehicles

Charging Strategy Unifying Spatial-Temporal Coordination of Electric Vehicles

Stochastic charging scheduling of a parking lot with wind power: a state‐aggregation method based on Markov decision processes

Experimental Validation of Modified Adaptive Fuzzy Control for Power Quality Improvement

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