A Reliability Assessment Approach for Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

Hou, Kai; Xu, Xiandong; Jia, Hongjie; Yu, Xiaodan; Jiang, Tao; Zhang, Kai; Shu, Baipo

doi:10.1109/tsg.2016.2545113

Cited by 76 publications

(44 citation statements)

References 23 publications

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“…Some studies take into consideration queuing time for the location problems of charging stations (for example, in [18][19][20][21][22]); various studies revealed that queuing time strongly affects the adoption of EVs [23,24]. Fast-charging stations have been proposed to address this issue; however, the installation of this type of charging station has a negative impact on the distribution network, such as high voltage [19,25] and overload distribution system during peak hours [26,27]. User behavior was considered in [28], while in [29] a resource planning scheme was investigated.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Model for the Temporary Locations of Mobile Charging Stations

et al. 2020

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A possible solution with which to alleviate the range anxiety of electric vehicle (EV) drivers could be a mobile charging station which moves in different places to charge EVs, having a charging time of even half an hour. A problem that arises is the impossibility of charging in any location due to heavy traffic or limited space constraints. This paper proposes a new operational mode for the mobile charging station through temporarily stationing it at different places for certain amounts of time. A mathematical model, in the form of an optimization problem, is built by modeling the mobile charging station as a queuing process, the goal of the problem being to place a minimum number of temporary service centers (which may have one or more mobile charging stations) to minimize operating costs and the charger capacity of the mobile charging station so that the service offered is efficient. The temporary locations obtained are in areas with no or few fixed charging stations, making the mobile station infrastructure complementary to the fixed charging station infrastructure. The temporary location operational mode, compared to current moving operational mode, is more efficient, having a small miss ratio, short mean response time and short mean queuing time.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimization Model for the Temporary Locations of Mobile Charging Stations

et al. 2020

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“…Reliability issues of power networks due to extensive integration of EVs are well addressed through various significant approaches in studies. [28][29][30] However, detailed stochastic aspects are not covered along with unbalancing issues. Charging scenarios with significant EV characteristics are considered with a stochastic method for analyzing reliability parameters of power networks.…”

Section: Review Of Ev Studiesmentioning

confidence: 99%

“…A probabilistic approach is employed by utilizing Institute of Electrical and Electronics Engineers (IEEE) test system; nevertheless, stochastic aspects for EV charging pattern are limited to evaluate reliability issues of grids. [30] Bidirectional charging and discharging scenarios are realized in the study by Liu et al [31] through power control methodology. However, the moment of charging is not defined for the development of EV charging model.…”

Section: Review Of Ev Studiesmentioning

confidence: 99%

“…The work by Richardson et al estimated daily EV mileage with its power consumption based on the predefined distribution curve. Reliability issues of power networks due to extensive integration of EVs are well addressed through various significant approaches in studies . However, detailed stochastic aspects are not covered along with unbalancing issues.…”

Section: Introductionmentioning

confidence: 99%

“…The study concluded that peak load factor is substantially increased by applying level 2 power charging. Unbalancing issues resulted from the massive single‐phase charging of EVs are not considered for assessment of realistic EV charging model in the aforementioned studies . Moreover, problems associated with added EV charging demand with location are not analyzed in terms of customers with voltage issues.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles

et al. 2019

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Large‐scale integration of electric vehicles (EVs) into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Similarly, electric load forecasting is an operational process permitting the utilities to manage demand issues for optimal energy utilization. Unbalanced voltages prevent the effective and reliable operation of RDNs. This study implements a novel framework to examine risks associated with RDNs by applying a residential forecasting model with a stochastic model of EVs charging pattern. Diversified EV loads require a stochastic approach to predict EVs charging demand; consequently, a probabilistic model is developed to account for several realistic aspects comprising charging time, battery capacity, driving mileage, state‐of‐charge, travelling frequency, charging power, and time‐of‐use mechanism under peak and off‐peak charging strategies. Peak‐day forecast of various households is obtained in summer and winter by implementing an optimum nonlinear auto‐regressive neural‐network (NN) with time‐varying external input vectors (NARX). Outputs of the EV stochastic model and residential forecasting model obtained from Monte‐Carlo simulations and the NARX‐NN model, respectively, are utilized to evaluate power quality parameters of RDNs. Performance specifications of RDNs including voltage unbalance factor (VUF) and voltage behavior are assessed in context to EV charging scenarios with various charging power levels under different penetration levels.

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Coordinated optimizations of urban traffic and power distribution systems

Zhang

Yang

et al. 2021

Int Trans Electr Energ Syst

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Summary This paper presents a bi‐objective and bi‐hierarchical coordinated optimization model for integrated urban traffic system (UTS) and power distribution system (PDS). The two objectives target the least traffic congestion in UTS and the lowest power losses in PDS. In the upper hierarchy, the two objectives are co‐optimized with traffic light settings as optimized variables. In the lower hierarchy, a vehicle re‐routing process includes a dynamic vehicle assignment and a dynamic microscopic simulation for all individual vehicles. The two challenging issues, that is, the coordination between the microscopic simulation and the relatively macroscopic modeling and the spatiotemporal optimizations in both UTS and PDS, are fully resolved. The coordinated optimization model is solved using the NSGA‐II algorithm which is suitable for multiple objective problems. The effectiveness of the proposed model and algorithms is verified using an example of integrated UTS and PDS.

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A Reliability Assessment Approach for Integrated Transportation and Electrical Power Systems Incorporating Electric Vehicles

Cited by 76 publications

References 23 publications

An Optimization Model for the Temporary Locations of Mobile Charging Stations

An Optimization Model for the Temporary Locations of Mobile Charging Stations

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles

Coordinated optimizations of urban traffic and power distribution systems

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