Environmental Engineering Reference
In-Depth Information
PHEV load flexbility in node 4
4
MaxSOC
SOC
EVLF
3.5
3.0
2.5
2
1.5
1
0.5
0
6
12
18
24
6
12
18
24
6
12
18
24
Time (h)
Figure 7.8
Battery data snapshots at node 4 describe how commercial areas might
be impacted by PHEVs
make a quick trip to home or shops and as a consequence small spikes are visible
in other nodes. Another pattern worth noting in these graphs is that people return
home later on Friday and especially Saturday night compared to Thursday, showing
the high level of detail in the results that are obtained from the activities. Some travel
variations between days are caused by randomness in activities, while others have to
do with deviations in departure times. Overall, for this and the rest of the nodes, load
flexibility of vehicles increases as the day progresses after travelling activities have
been made.
Figures 7.6 and 7.7 describe how networks would see PHEV units plugged-
in at the residential nodes. Mostly battery capacity would be available during the
evening and early morning hours and to a lesser extent during the afternoon. Mean-
while, Figure 7.8 represents battery presence at node 4, clearly receiving a smaller
amount of PHEVs than other nodes which can be explained by the fact that it is
a less populated area and not such a busy destination. Nevertheless, due to lower
PHEV penetration it is interesting to see it has a less significant profile than the other
nodes and this could have consequences on the charging infrastructure that would be
installed in such an area, for example.
All vehicle journey results are the outcome of the defined profiles and activities
of the agents, whereas other similar PHEV studies as indicated in section 7.2, often
resort to composing this data as input without having the spatial elements clearly
represented. By adding agents and giving diversity to their profiles, it is possible to
begin simulating and depicting reliable forecasts of the behaviour of drivers and hence
the load flexibility this new type of mobile user offers to the grid. The total battery
energy used, plug-in factors and load flexibility from the ABM simulation provide
sufficient input data, so the TCOPF can optimise PHEV charging.
Search WWH ::
Custom Search