A cost-benefit analysis of V2G electric vehicles supporting peak shaving in Shanghai

Li, Xinzhou; Tan, Yonghong; Liu, Xinxin; Liao, Qiangqiang; Sun, Bo; Cao, Guangyu; Cheng, Li; Yang, Xiu; Wang, Zhiqin

doi:10.1016/j.epsr.2019.106058

Cited by 137 publications

(53 citation statements)

References 26 publications

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“…While minimising the cost function, the DP solution must also respect boundary conditions. The final battery SOC (SOC bat, f inal ) must be greater than or equal to the SOC required to complete the next trip (14). The SOC must stay between the minimum and maximum operational limits (SOC bat, min , SOC bat, max , (15)).…”

Section: Charging Optimisationmentioning

confidence: 99%

“…SOC bat, f inal ≥ SOC NextTrip (14) SOC bat,min ≤ SOC bat ≤ SOC bat,max (15) P charger,min ≤ P charger ≤ P charger,max (16) The optimal charging power profile is generated and then sent to the vehicle onboard charger controller ( Figure 13). The benefits of the DP optimisation have been discussed later in this paper.…”

Section: Charging Optimisationmentioning

confidence: 99%

“…Each BEV connected to the grid could be used as bidirectional energy storage -storing renewable energy when available and releasing that energy during peak demand [12]. The peak shaving capability and benefits enabled by V2G have been emphasised by Aziz et al [13] and Li et al [14]. A research thesis by Kaufmann [15] estimates the financial return from V2G could be circa $1275 per vehicle every year, while the NREL claimed in 2015 that circa $1825 per vehicle every year is achievable.…”

Section: Introductionmentioning

confidence: 99%

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Cloud-Based Big Data Platform for Vehicle-to-Grid (V2G)

Grée¹,

Laznikova²,

Kim³

et al. 2020

WEVJ

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Battery Electric Vehicles (BEVs) have increasingly become prevalent over the past years. BEVs can be regarded as a grid load and as a way to support the grid (energy buffering), provided this extensive battery usage does not affect the BEV’s performance. Data from both the vehicle and the grid are required for effective Vehicle-to-Grid (V2G) implementation. As such, a cloud-based big data platform is proposed in this paper to exploit these data. Additionally, this study aims to develop smart algorithms, which optimise different factors, including BEV cost of ownership and battery degradation. Dashboards are developed to provide key information to different V2G stakeholders.

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Section: Charging Optimisationmentioning

confidence: 99%

Section: Charging Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cloud-Based Big Data Platform for Vehicle-to-Grid (V2G)

Grée¹,

Laznikova²,

Kim³

et al. 2020

WEVJ

View full text Add to dashboard Cite

show abstract

“…Finally, it is an indisputable fact that EVs, storage systems, and distributed RES should be coordinated intelligently with the rest of the grid in order to maximize the use of renewable sources [19] and provide flexibility to the whole electrical system. In recent years, research interest in these topics, as well as the deployment of pilot projects, have increased [20][21][22][23][24]. Regarding the integration of EVs into electric systems, the reference literature, apart from the classical charging mode, the so-called grid-to-vehicle (G2V), distinguishes two principal operating modes.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

et al. 2020

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In the context of electric vehicle (EV) development and positive energy districts with the growing penetration of non-programmable sources, this paper provides a method to predict and manage the aggregate power flows of charging stations to optimize the self-consumption and load profiles. The prediction method analyzes each charging event belonging to the EV population, and it considers the main factors that influence a charging process, namely the EV’s characteristics, charging ratings, and driver behavior. EV’s characteristics and charging ratings are obtained from the EV model’s and charging stations’ specifications, respectively. The statistical analysis of driver behavior is performed to calculate the daily consumptions and the charging energy request. Then, a model to estimate the parking time of each vehicle is extrapolated from the real collected data of the arrival and departure times in parking lots. A case study was carried out to evaluate the proposed method. This consisted of an industrial area with renewable sources and electrical loads. The obtained results show how EV charging can negatively impact system power flows, causing load peaks and high energy demand. Therefore, a charging management system (CMS) able to operate in the smart charging mode was introduced. Finally, it was demonstrated that the proposed method provides better EV integration and improved performance.

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“…With the utilization of bidirectional converters, bidirectional power flow is, therefore, possible to have access to power from the utility grid and even to supply it as DERs with the use of power converters while EVs are plugged-in. However, by controlling the charging/discharging process of EVs that installed in an MG, peak load could be shifted, and therefore, a reliable and economical grid operation can be achieved [6], [7].…”

mentioning

confidence: 99%

Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

et al. 2020

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The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios. INDEX TERMS Vehicle-to-grid (V2G), electrical vehicles (EVs), particle swarm optimization (PSO), artificial bee colony (ABC), dynamic ED, operating cost (OC), pollutant treatment cost (PTC), carbon dioxide emission (CE). NOMENCLATURE ABBREVIATIONS PG Power Grid WT Wind Turbine MT Micro Gas Turbine The associate editor coordinating the review of this manuscript and approving it for publication was Yanbo Chen .

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A cost-benefit analysis of V2G electric vehicles supporting peak shaving in Shanghai

Cited by 137 publications

References 26 publications

Cloud-Based Big Data Platform for Vehicle-to-Grid (V2G)

Cloud-Based Big Data Platform for Vehicle-to-Grid (V2G)

Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization

Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

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