Environmental Engineering Reference
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so, thus making clear that if the solver forecasts it will be cheaper to charge at a later
moment of the day it will do so.
Overall, since weekday travelling activities for Thursday and Friday are similar
the simulations are somewhat alike, leading us to believe that other working days
of the week would produce similar PHEV charging profiles. As long as penetration
of EVs remains low, most charging should be done past the evening peak demand
utilities usually register in local networks. On the other hand, due to the quite distinct
travelling behaviour of agents on Saturday the PHEV charging demands are different
than for the previous days, leading us to believe that weekend load flexibility of
mobile loads may be slightly more challenging to forecast. Moreover, more detailed
models of low voltage networks could provide further knowledge as to how best
to manage feeders and develop advanced charging algorithms as PHEV penetration
increases.
As the detailed results suggest, the variations on input data and modelling assump-
tions can make outputs vary significantly. Indeed, each case study needs to be carefully
formulated. Nevertheless, the case study fulfils its core objective which is the ability
to formulate, model and calculate the complex problem of PHEV mobility and opti-
mal charging via the TCOPF modelling framework. Combining different modelling
principles has shown the insight integrated modelling brings to the forefront of power
system studies. The level of analysis shown in the case study is just the tip of the ice-
berg on assessing mobile loads in electrical networks, hence providing basic patterns
of future load demand. In-depth knowledge for energy stakeholders is possible via
modelling and with further research it is indeed feasible to cover the basic elements
to seamlessly integrate PHEVs to electrical networks for a better management of
future power systems.
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