Sikai Huang

The impact of domestic Plug-in Hybrid Electric Vehicles on power distribution system loads

The market for Plug-in Hybrid Electric Vehicle (PHEVs) is expected to grow significantly over the next few years and a number of new products are soon to come onto the market, such as the Toyota Prius plug-in version, [1]. The charging demand of wide-scale use of PHEVs may have a significant impact on domestic electricity loads and could risk of overloading the power system if appropriate charging strategies not applied to prevent this. A Monte Carlo Simulation (MCS) model of domestic PHEV use and availability has been developed based on probabilistic characterisations obtained from UKTUS and quantifies charging demand of PHEVs as a function of time of day. The MCS model has been developed in order to simulate the impact on the electricity distribution system. This article also discusses the potential for responsive battery charging load from PHEVs.

Using Electric Vehicle Fleet as Responsive Demand for Power System Frequency Support

Based on conventional car driving patterns, it has been recognized that typically a car remains stationary for 95% of the time, only being driven on the road for the remaining 5% of that time on average. This gives the system operator opportunities to utilize future EV charging as system reserve demand in order to provide system control, such as frequency response. In this paper, a next generation power system with high penetration of renewables represents the future power grid in Great Britain. Three charging schemes for EVs are presented in order to illustrate their potential as responsive demand for power system control. A case study was undertaken for the year 2020. System frequency stability tests have been performed with shedding EV fleet charging.

Plug-in electric vehicles as demand response to absorb local wind generation in power distribution network

It has been forecast that by 2020, the penetration of renewable generation in the UK energy mix will reach approximately 15%, predominantly from wind generation, and that the number of electric vehicles (EVs) deployed is also expected to exceed 1 million. Over the same period it is also forecast that the security of supply of the UK power system will be affected due to the increasing imbalance due to increased demand (from EVs) and uncontrolled supply (i.e. from wind). This paper studies the use of applying smart EV charging strategies to help the power system cope with high penetrations of local renewable generation. Key to this work is the recognition that domestic vehicles are parked for typically 95% of the time, hence these EVs can be utilised as a ready form of responsive demand.

Plug-in electric vehicles as demand response to local surplus wind generation

It has been forecast that by 2020, the penetration of renewable generation in the UK energy mix will reach approximately 15%, predominantly from wind generation, and that the number of electric vehicles deployed is also expected to exceed one million. Over the same period it is also forecast that the security of supply of the UK power system will be affected by the increasing imbalance due to increased demand (from EVs) and uncontrolled supply (i.e., from wind). This paper studies the use of applying smart EV charging strategies to help the power system cope with high penetrations of local renewable generation. Key to this work is the recognition that domestic vehicles are parked for typically 95% of the time, hence these EVs can be utilised as a ready form of responsive demand.

Monte Carlo modelling for domestic car use patterns in united kingdom

For the purposes of quantifying the potential impact of widespread electric vehicles charging on the UKs power distribution system, it is essential to obtain relevant statistical data on domestic vehicle usage. Since electric vehicle ownership is presently very limited, these data will inevitably be for conventional internal combustion engine vehicles, and in particular privately owned vehicles. This should not be an issue since the limited journey distances that will dealt with in this work could as easily be undertaken by an electric vehicle as a conventional vehicle. Particular attention is paid to the United Kingdom 2000 Time Use Survey as it contains detailed and valuable statistical information about household car use. This database has been analyzed to obtain detailed car use statistics, such as departure and arrival time, individual journey time, etc. This statistical information is then used to build up two Monte Carlo simulation models in order to reproduce weekday car driving patterns based on these probability distributions. The Monte Carlo methodology is a well-known technique for solving uncertainty problems. In this paper, key statistics of domestic car use are presented together with two different Monte Carlo simulation approaches the simulation results that have been analyzed to verify the results being consistent with the statistics extracted from the TUS data.