Stochastic Modeling of the Charging Behavior of Electromobility

Sokorai, Peter; Fleischhacker, Andreas; Lettner, Georg; Auer, Hans

doi:10.3390/wevj9030044

Cited by 17 publications

(15 citation statements)

References 16 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [140], the authors defined a temporal stochastic process modelling charging patterns at a public EVSE with a Markov Chain comprising three states: unoccupied, charging and plugged-in but not charging. Essentially, the Markov Chains setup assumes that the current state of the process, conditionally to all past states, only depends on the previous state.…”

Section: Stochastic Processesmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Electric Vehicle Load Open Data and Models

et al. 2021

View full text Add to dashboard Cite

The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

show abstract

Section: Stochastic Processesmentioning

confidence: 99%

“…It simplifies the calculation and has been extensively studied in the literature through many applications [143]. In [140], after initializing the transition probability matrix which drives the path of the process they let the system evolve and assess the revenue made by the charging station.…”

Section: Stochastic Processesmentioning

confidence: 99%

A Review of Electric Vehicle Load Open Data and Models

et al. 2021

View full text Add to dashboard Cite

show abstract

“…By using MCMC simulation, the impact of the uncertainty in the EV load was investigated at a distribution network level. Sokorai et al built a probabilistic model for fast charging stations (CSs), defined three states (stay, charged, and plugged in but not charged), calculated an hourly charge amount, and then examined the economic impact on a station [14]. Lee et al synthesized representative fleet cycles using the transition probability of speed and acceleration data based on the actual fleet data of nine vehicles [15].…”

Section: Previous Workmentioning

confidence: 99%

“…Equations (11) and (12) show that charging and discharging are possible only when the vehicle is at home. Equation 13gives the discharge amount that is less than household demand, and (14) and (15) give lower and upper limits of battery SOC. Equation 16shows that the SOC is restored to the initial value or higher at the final time step.…”

Section: B Ev Battery Optimization Model (Planning Model)mentioning

confidence: 99%

Driving Simulator for Electric Vehicles Using the Markov Chain Monte Carlo Method and Evaluation of the Demand Response Effect in Residential Houses

Iwafune¹,

Ogimoto²,

Kobayashi³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

It is essential to secure flexible resources in power systems to increase the proportion of variable renewable energy generation systems. One flexible resource is demand response (DR) of the batteries of electric vehicles (EVs). In this study, we propose an electric vehicle driving simulator using the Markov chain Monte Carlo (MCMC) method and an EV demand response evaluation model. The former is a highly versatile EV driving simulator that can produce a random driving pattern based on actual EV fleet data, taking into account various features. The latter is a residential DR evaluation model that minimizes a household electricity bill based on the simulated fleet data and enables realistic DR operation using three processes: forecasting, planning, and operation. The contribution of this paper is to enable evaluation of realistic EV battery value in various housing and EV utilization combinations. We found that the EV battery control provides an economic benefit of US$62 (5% of the night-charging case cost) with only charge control and US$370 (31%) with charge and discharge control, as the average expected value based on our assumption of evaluation. Because 40-kWh EV batteries have a sufficiently large capacity to store surplus power from a rooftop PV, they can be operated by determining the operation schedule based on a fixed-fee structure without forecasting or planning.

show abstract

“…Considering 3.5% of total cars are EVs in 2022, the number of EVs will reach 108 000 units. Since previous studies showed that 80% to 85% of the EVs fulfill their charging requirements at homes and workplaces, the rest 15% to 20% require charging stations for charging purposes which practically must be fast charging. A total of 21 600 units represent 20% of 108 000 units distributed among 400 charging stations which results in an average of 54 units to be charged by a single station.…”

Section: Significance Of the Studymentioning

confidence: 99%

Assessment of a stand‐alone hybrid solar and wind energy‐based electric vehicle charging station with battery, hydrogen, and ammonia energy storages

Wahedi

Biçer

2019

Energy Storage

View full text Add to dashboard Cite

Since the main objective of expanding the deployment of electric vehicle (EV) usage is to reduce the dependency on carbon‐based fuels, it is essential to consider eco‐friendly and renewable sources to generate the power needed for charging those vehicles. This study suggests and analyzes a stand‐alone solar and wind energy‐driven integrated system with electro/chemical energy storage to provide independent and uninterruptable power supply for EV charging stations. Due to the intermittent nature of the utilized renewable energy sources, energy storage is a key concern to be considered in this study. Therefore, in addition to batteries, hydrogen and ammonia are considered as energy storage media, which can be converted into electricity through fuel cells on demand. The results show that with selected commercialized photovoltaic power plant covering an area of about 1500 m2, a 250 kW rated wind turbine, 650 kWh Li‐ion storage batteries, 30 m3 storage of H2 in gas form, and 5 m3 storage of NH3 in liquid form, a grid‐independent charging station sufficient for fast charging of 50 number of EVs per day can be achieved. Additional suggestions are proposed to better manage the energy storage within the charging stations based on short‐term and long‐term operations.

show abstract

Stochastic Modeling of the Charging Behavior of Electromobility

Cited by 17 publications

References 16 publications

A Review of Electric Vehicle Load Open Data and Models

A Review of Electric Vehicle Load Open Data and Models

Driving Simulator for Electric Vehicles Using the Markov Chain Monte Carlo Method and Evaluation of the Demand Response Effect in Residential Houses

Assessment of a stand‐alone hybrid solar and wind energy‐based electric vehicle charging station with battery, hydrogen, and ammonia energy storages

Contact Info

Product

Resources

About