A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account

Yağcıtekin, Bünyamin; Uzunoğlu, Mehmet

doi:10.1016/j.apenergy.2015.09.040

Cited by 92 publications

(30 citation statements)

References 37 publications

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“…The result of arrival and departure time distribution is shown in Figure 6. The origin-destination pattern (OD) is assumed to follow a normal distribution, as shown in Equation (17). The average distance is 18.9 km according to the report in [32]: The origin-destination pattern (OD) is assumed to follow a normal distribution, as shown in Equation (17).…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits

Gao

2017

Energies

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Many electric vehicles' (EVs) charging strategies were proposed to optimize the operations of the power grid, while few focus on users' benefits from the viewpoint of EV users. However, low participation is always a problem of those strategies since EV users also need a charging strategy to serve their needs and interests. This paper proposes a method focusing on EV users' benefits that reduce the cost of battery capacity degradation, electricity cost, and waiting time for different situations. A cost model of battery capacity degradation under different state of charge (SOC) ranges is developed based on experimental data to estimate the cost of battery degradation. The simulation results show that the appropriate planning of the SOC range reduces 80% of the cost of battery degradation, and the queuing theory also reduces over 60% of the waiting time in the busy situations. Those works can also become a premise of charging management to increase the participation. The proposed strategy focusing on EV users' benefits would not give negative impacts on the power grid, and the grid load is also optimized by an artificial fish swarm algorithm (AFSA) in the solution space of the charging time restricted by EV users' benefits.

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Section: Monte Carlo Methodsmentioning

confidence: 99%

Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits

Gao

2017

Energies

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“…[20] proposed a centralized strategy to optimize the battery swapping behaviors of EVs. In [21], a centralized double-layer smart charging management algorithm was designed which not only optimizes the charging power of EVs but also navigates the EVs to proper charging stations. In [22], the authors developed an optimization technique to coordinate V2G power to deal with the intermittency in renewable power generation.…”

Section: Introductionmentioning

confidence: 99%

“…Coordinated EV charging strategies in previous published papers can mainly be divided into three categories: centralized charging strategies [18][19][20][21][22][23], decentralized charging strategies [24][25][26][27][28][29][30], hierarchical charging strategies [31,32].…”

Section: Introductionmentioning

confidence: 99%

“…[23] proposed a joint power scheduling strategy for EVs and wind generation utilities participating in the day-ahead energy, balancing, and regulation markets. In [18][19][20][21][22][23], centralized controllers are used to determine the charging schedules for all EVs. Centralized control strategies can guarantee optimal controlling results.…”

Section: Introductionmentioning

confidence: 99%

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Pricing mechanisms design for guiding electric vehicle charging to fill load valley

et al. 2016

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“…In [7], a multi-objective scheduling of EVs in a smart distribution system is proposed, so as to minimize the total operation cost and emissions. In [8], to improve the security and economics of the grid operation, a model concerning with optimal power flow, statistic characteristics of EVs, Temporal domain [6] (1) power losses (2) the cost of purchased energy [7] (1) the total operational costs and emissions [8] (1) power loss (2) adjustment frequency for power grid control equipment (3) the smoothness for the power daily load curve (4) EV owners' degree of satisfaction [9] (1) peak-valley difference Spatial domain [10] (1) system charging time (2) system charging capacity (3) dispatching charging load [11] (1) generation cost (2) network losses [12] (1) the utilization of existing networks Temporal and Spatial domain [13] (1) First level: the discharging cost of EV, charging station corresponding transformer loading (2) Second level: network Losses [14] (1) transmission system: fuel cost; the PM 2.5 emission of a thermal unit; the start-up and shut-down cost of thermal unit; charging cost; wind curtailment cost (2) distribution system: network Losses…”

Section: Introductionmentioning

confidence: 99%

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

Yang

Chen

et al. 2016

Energies

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Abstract:With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.

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A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account

Cited by 92 publications

References 37 publications

Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits

Optimal Planning of Charging for Plug-In Electric Vehicles Focusing on Users’ Benefits

Pricing mechanisms design for guiding electric vehicle charging to fill load valley

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

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