Pia Grahn

PHEV Utilization Model Considering Type-of-Trip and Recharging Flexibility

Electric vehicles (EVs) may soon enter the vehicle market in large numbers and change the overall fuel usage within the passenger transport sector. With increased variable consumption from EVs together with anticipated increased production from variable sources, due to renewable wind and solar power, also the balancing of the electric power system incur increased attention. This emphasizes the importance of developing models to estimate and investigate the stochasticity of personal car travel behavior and induced EV charging load. Several studies have been made in order to model the stochasticity of passenger car travel behavior but none have captured the charging behavior dependence of the type-of-trip conducted. This paper proposes a new model for plug-in-hybrid electric vehicle (PHEV) utilization and recharging price sensitivity, to determine charging load profiles based on driving patterns due to the type-of-trip and corresponding charging need. This approach makes it possible to relate the type-of-trip with the consumption level, the parking location, and the charging opportunity. The proposed model is applied in a case study using Swedish car travel data. The results show the charging load impact and variation due to the stochastic PHEV type-of-trip mobility, allowing quantification of the PHEV charging impact on the system.

The customer perspective of the electric vehicles role on the electricity market

Recent studies have investigated the impact and effects of using batteries in connected electric vehicles as ancillary services to the electricity grid. A common assumption made is that all parked cars are connected to the grid and available for recharging or discharging. However, the level of flexibility of peoples behavior is important and will affect the potential of using car batteries for regulation power. How customers will react if they are expected to recharge or discharge whenever the electric system need it, will depend on incentives and peoples willingness to adapt. This paper reviews existing research regarding electric vehicles and their interaction with the electric power system and investigate conditions for a potential use of the batteries as regulation power. The customer perspective of the electric vehicles role on the electricity market is analyzed considering participation in the control power market.

A method for evaluating the impact of electric vehicle charging on transformer hotspot temperature

The expected increasing market share of electric vehicles is a response to the combination of new technological developments, governmental financial control, and an attitude shift of residents to a more environmentally friendly lifestyle. The expected capacity required for charging, imposes changes in the load to the already existing components in the electric power grid. In order to continue managing these existing assets efficiently during this load change, it is important to evaluate the impact imposed by the battery charging.

Plug-in-vehicle mobility and charging flexibility Markov model based on driving behavior

Climate targets around the globe are enforcing new strategies for reducing climate impacts, which encourage automobile and electricity companies to consider an electrified vehicle market. Furthermore, the variable electricity production in the electric power system is increasing, with higher levels of wind and solar power. Due to the increased variability in the system, the need to meet fluctuations with flexible consumption is intensified. Electric vehicles with rechargeable batteries seem to become an increasingly common feature in the car fleet. Plugin vehicles (PIVs), may therefore become valuable as flexible consumers. If so, flexible PIV owners could, if they are flexible enough, increase the value of owning an electric vehicle. This paper introduces a new PIV Mobility and Charging Flexibility Markov Model, based on driving behavior for private cars. By using the new model, it is possible to simulate the potential flexibility in a future system with many PIVs. The results from a case study indicate a potential need for usage of the batteries as flexible loads to reduce the grid power fluctuations and load peaks.

A Bernoulli distribution model for plug-in electric vehicle charging based on time-use data for driving patterns

This paper presents a Bernoulli distribution model for plug-in electric vehicle (PEV) charging based on high resolution activity data for Swedish driving patterns. Based on the activity “driving vehicle” from a time diary study a Monte Carlo simulation is made of PEV state of charge which is then condensed down to Bernoulli distributions representing charging for each hour during weekday and weekend day. These distributions are then used as a basis for simulations of PEV charging patterns. Results regarding charging patterns for a number of different PEV parameters are shown along with a comparison with results from a different stochastic model for PEV charging. A convergence test for Monte Carlo simulations of the distributions is also provided. In addition to this we show that multiple PEV charging patterns are represented by Binomial distributions via convolution of Bernoulli distributions. Also the distribution for aggregate charging of many PEVs is shown to be normally distributed. Finally a few remarks regarding the applicability of the model are given along with a discussion on potential extensions.

PHEV utilization model considering type-of-trip and recharging flexibility

Summary form only given. Electric vehicles (EVs) may soon enter the vehicle market in large numbers and change the overall fuel usage within the passenger transport sector. With increased variable consumption from EVs together with anticipated increased production from variable sources, due to renewable wind and solar power, also the balancing of the electric power system incur increased attention. This emphasizes the importance of developing models to estimate and investigate the stochasticity of personal car travel behavior and induced EV charging load. Several studies have been made in order to model the stochasticity of passenger car travel behavior but none have captured the charging behavior dependence of the type-of-trip conducted. This paper proposes a new model for plug-in-hybrid electric vehicle (PHEV) utilization and recharging price sensitivity, to determine charging load profiles based on driving patterns due to the type-of-trip and corresponding charging need. This approach makes it possible to relate the type-of-trip with the consumption level, the parking location, and the charging opportunity. The proposed model is applied in a case study using Swedish car travel data. The results show the charging load impact and variation due to the stochastic PHEV type-of-trip mobility, allowing quantification of the PHEV charging impact on the system.