Environmental Engineering Reference
In-Depth Information
(
i.e.
travel profiles). Thus, for DNOs to successfully adapt to PHEV loads within their
system it is key to comprehend the temporal and spatial characteristics of vehicle users.
Travel survey data reveals that most of the daily driving distance of light duty
vehicles is less than 65 km in the USA [104] and less than 40 km in the UK [108]. This
data implies that if PHEV batteries are designed to cover a distance well over the mean,
there is a big potential to displace great amounts of petrol according to the driving
habits and energy management strategies of the users. Besides, complementary to
distance travel data, knowing 'when' and 'where' the vehicles are in use is another
set of key information stakeholders will need to take into account in order to provide
a satisfactory service.
Initial studies suggest GPS technology can aid DNOs in tracking and register-
ing the movements of PHEV units [105]. Data mining indeed can play a major role
assessing PHEV mobility. Otherwise, if no data log programs are implemented, there
will be a lack of knowledge to estimate the load profile variations that will take
place in local networks. This work does cover modelling the mobility of PHEVs (see
Chapter 7). However, as a first step travel data surveys are used to portray driving
habits of urban vehicles. Figure 4.17 shows how car journeys are distributed during
a weekday in an urban area, this serves us to illustrate the type of driving behaviour
utilities can expect.
Electric utilities are designed with the premise to satisfy the instantaneous
power demand that varies over time. Accordingly, as PHEV penetration grows, con-
ventional profiles that have become normal for utilities may change drastically as
USA vehicle travel profile in an urban area
10
Probability
8
6
4
2
0
2
4
6
8
10
12
14
16
18
20
22
24
Time (h)
Figure 4.17
Percent of vehicle journeys by time of day in an urban area of the USA
[104]
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