Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale

Yang, Yuqing; Zhang, Weige; Niu, Liyong; Jiang, Jiuchun

doi:10.3390/en8021256

Cited by 16 publications

(10 citation statements)

References 9 publications

(8 reference statements)

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“…The PSO algorithm is based on swarm intelligence, and features advantages such as fast convergence, relatively few parameters, and suitability for dynamic environments [25][26][27]35,36]. The steps of PSO are described as follows:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Fang¹,

Ke²,

Chung³

2017

Energies

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Abstract:The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO-GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO-GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Fang¹,

Ke²,

Chung³

2017

Energies

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“…In terms of the positive performance of the optimization solution in [15], both of the multi-objective optimization problems derived from the two strategies above could be tackled by fuzzification multi-objective optimization and an improved particle swarm optimization (IPSO) algorithm. More specifically, the detailed description is illustrated as follows.…”

Section: Solution Techniquementioning

confidence: 99%

“…The fuzzy mathematics method, with the membership function, has been utilized to perform the objective function fuzzification to transfer the multi-objective optimization to a single-objective issue [15,16].…”

Section: Fuzzy Multi-objective Optimizationmentioning

confidence: 99%

Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

et al. 2015

Self Cite

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Abstract:With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs) is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS) has to be involved to mitigate the bad effects of DERs' integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs) considered as the auxiliary technique are also introduced to attenuate the DERs' influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs' operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO) algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration.

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“…Under the current fast-charging technology, EV charging and CS operation influence each other. If some guiding measures are taken, they can effectively adjust interactions between EVs and CSs [13][14][15][16][17]. Beyond this, however, the parallel output characteristics and charging strategy of charging facilities can also affect the EV charging and CS operation.…”

Section: Introductionmentioning

confidence: 99%

“…The inter-station EV-guided charging strategy means that EVs can charge different CSs by using the guiding method to reduce user waiting time and improve the utilisation rate of charging infrastructure and reduce the impact on the power grid. The guided method is sub-divided into the reservation guided method [13][14][15][16] and electricity price-guided method [17]. A reservation-guided method means that the user interacts with the platform that will then specify a CS for the user, which lays more emphasis on how to offer suitable charging options for EV users, without considering either the effect of electricity price on user choice or management of user charging process.…”

Section: Introductionmentioning

confidence: 99%

Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations

Chen¹,

Huang²,

Chen³

et al. 2016

Energies

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Abstract:To solve the problem, because of which conventional quick-charging strategies (CQCS) cannot meet the requirements of quick-charging for multiple types of electric vehicles (EV) on highways where vehicle inflow is excessive, this paper proposed a new quick-charging strategy (NQCS) for EVs: on the premise of not affecting those EVs being charged, the remaining power of the quick-charging pile with multiple power output interfaces is used to provide a synchronous charging service for EVs waiting in the queue. To verify the effectiveness of this strategy, a power distribution model of charging pile and a queuing model of charging station (CS) were constructed. In addition, based on an actual highway service area where vehicle inflow is excessive during the simulation period (0:00-24:00), charging situations of CQCS and NQCS were respectively simulated in a charging station (CS), with different number of chargers, by basic queuing algorithm and an improved queuing algorithm. The simulation results showed that when the relative EV inflow is excessive, compared to CQCS, NQCS not only can reduce user waiting time, charging time, and stay time, but also can improve the utilisation rate of charging infrastructure and service capacity of CS and reduce the queue length of CS. At the same time, NQCS can reduce the impact on the power grid. In addition, in NQCS, the on-demand power distribution method is more efficient than the average power distribution method. Therefore, NQCS is more suitable for quick-charging for multiple types of EVs on highways where vehicle inflow is excessive.

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Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale

Cited by 16 publications

References 9 publications

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration

Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations

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