Reliability-Based Metrics to Quantify the Maximum Permissible Load Demand of Electric Vehicles

Kamruzzaman, Md.; Benidris, Mohammed

doi:10.1109/tia.2019.2914877

Cited by 24 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [22] assumed that the BEVs could be discharged to supply power to the home in case of power outage in the islanded mode. The authors of [23] proposed two metrics to study the impact of BEVs on power system reliability. The first index was used to determine the maximum load of BEVs which the power system can accommodate without upgrading it or affecting its reliability.…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment under High Penetration of EVs including V2G Strategy

et al. 2022

View full text Add to dashboard Cite

With the increase in the penetration of battery electric vehicles (BEVs) all over the world, utilities should start considering their increased demand as part of their electric demand. Generally, the literature lacks works that consider the impact of transportation electrification on the reliability of the power system. Thus, this paper proposes a new mechanism for reliability assessment including BEVs, with both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. Three charging strategies: uncontrolled, controlled unidirectional, and controlled bidirectional are considered in this paper to model the interactions between the transportation and electric power systems. A dynamic stochastic consumption model for a fleet of BEVs is developed to be used in the reliability assessment for the distribution networks. This dynamic model takes into consideration the variability and uncertainty of different trip purposes, starting and ending trip times, as well as the corresponding battery consumption in weather conditions. Furthermore, it is composed of two sequential submodels: travel behavior and battery depletion. The first submodel considers trip-related information while the second considers battery-depleted energy. Simulation results on a benchmark test system show the negative impacts of uncontrolled charging on the power system’s reliability. However, they also show that controlled charging can significantly reduce or mitigate these impacts.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliability Assessment under High Penetration of EVs including V2G Strategy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…On the other hand, the optimal allocation of EVCS can bring multiple advantages for grid like reduction of active power losses and voltage drop. Also, if the problem is modeled correctly, it may cause improving the reliability and increasing economic benefits for DISCO 27‐29 . In References 21 and 30, a multiobjective optimization problem is solved with the aim of minimizing power loss, voltage deviation, and cost to optimally allocate the EVCS in candidate buses.…”

Section: Introductionmentioning

confidence: 99%

Optimal resiliency‐oriented charging station allocation for electric vehicles considering uncertainties

Akhgarzarandy

Wang

Farzinfar

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper presents a resiliency‐oriented multi‐objective approach for optimal allocation of electric vehicles' (EV) charging station (EVCS) within distribution network. In addition, the effect of implementation of the demand response programs (DRP) is considered while allocating EVCSs. For this purpose, an appropriate modeling for considering various uncertainties such as consumption load, energy price, and initial state of EV charge are firstly presented. Moreover, the uncertainty and dynamic risk of wind speed during extreme weather conditions, as an important parameter that affects lines outage and grid resiliency, are accurately modeled. In the proposed methodology of this paper, the benefits obtained by ENS reduction and power loss is optimally shared with the EVCS owner to incentivize the investors to cooperate with DISCO and to invest in the construction of EVCS. The formulated problem is solved by utilization of an NSGA‐II‐based method embedded by scenario‐based approach and Monte‐Carlo method. The proposed formulation is applied to a standard network (IEEE 9 buses), and related Pareto‐front solutions for an economic‐resilient EVCS allocation, while maximizing benefits of Distribution Company Operator (DISCO) and EVCS owner are calculated. The results show that, in case of allocation two EVCSs and applying DRP program, total profits of DISCO is increased by 36.70% and 40.07%, respectively, compared with traditional grid that there is no EVCS or DRP implementation. Moreover, applying DRP, with its positive effect on peak load and power losses, reduces the EVCS capacities.

show abstract

“…In [29] a charging and discharging strategy based on the market price is presented, however, in this paper effect of charging/discharging program on load profile and EV behavior is not considered in the proposed strategy. In [30,31] EV charging load model is obtained system with the aim of improving the reliability of the system, but the generation model of EVs in the discharge mode has not been studied. Although the methods presented in the above papers take a comprehensive look at the issue of EVCS modelling and placement, it should be noted that considering electrical factors, such as reducing losses or peak shaving during peak hours, which leads to higher operating profit of DSO, does not necessarily encourage investors to construct EVCS.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

et al. 2020

View full text Add to dashboard Cite

Modeling and allocation of the Electric vehicles Charging Stations (EVCS) within the distribution network, as per the growing use of Electric vehicles (EVs) is a challenging task. In this paper, to manage the EVCS, first, with the aim of peak shaving, valley filling, and flattening the load curve of the network, the optimal planning of EV's charging/discharging is devised. In this regard, after modeling involving random variables, a novel hybrid method, based on the Multi Objective Particle Swarm Optimization (MOPSO) optimization algorithm and sequential Monte Carlo simulation is presented. The purpose of the presented optimal charge/discharge schedule is to control the rate and time of charging/ discharging of EVs. In the proposed model, various battery operation strategies, including Uncontrolled Charging Mode (UCM), Controlled Charging Mode (CCM), and Smart Charge/Discharge Mode (SCDM) are also considered. In the next step, in order to implement the charge/discharge schedule of the EVCS profitably, a new formulation is presented for the allocation of two EVCSs (administrative and residential EVCS). In the proposed formulation, various objective functions such as power loss reduction, reducing power purchases from the upstream network, reducing voltage deviation in buses, improving reliability is addressed. Moreover, to incentivize the owner to construct the EVCS with adopted charging/discharging schedule, the optimal profits sharing between Distribution System Operator (DSO) and EVCS owner is also performed. The proposed formulation is applied to a standard network (IEEE 69 buses) and encouraging results are achieved.

show abstract

Reliability-Based Metrics to Quantify the Maximum Permissible Load Demand of Electric Vehicles

Cited by 24 publications

References 28 publications

Reliability Assessment under High Penetration of EVs including V2G Strategy

Reliability Assessment under High Penetration of EVs including V2G Strategy

Optimal resiliency‐oriented charging station allocation for electric vehicles considering uncertainties

Optimal Allocation of Electric Vehicle Charging Stations With Adopted Smart Charging/Discharging Schedule

Contact Info

Product

Resources

About