Modelling electric vehicle mobility in energy service networks - Modelling Distributed Energy Resources in Energy Service Networks - page 145

Chapter 7
Modelling electric vehicle mobility in energy
service networks 1
Salvador Acha and Koen H. van Dam
The ability to determine optimal charging profiles of electric vehicles (EVs) is
paramount in developing an efficient and reliable smart-grid. However, as explained
in Chapter 4, so far the level of analysis proposed to address this issue lacks
combined spatial and temporal elements, thus making mobility a key challenge to
address for a proper representation of this phenomenon in power system analysis -
particularly at low voltage levels. For the case study presented in Chapter 6, plug-in
hybrid vehicle (PHEV) mobility data was taken from travel surveys and broad assum-
ptions were made regarding location of PHEVs among the energy service networks.
Current modelling capabilities, however, offer adequate alternatives to try and realis-
tically simulate vehicle journeys in transport networks, so temporal as well as spatial
data can be used as input for powerflow studies.
This chapter details the principles applied to represent optimal charging of
PHEVs by employing an agent-based model that simulates the travelling patterns
of vehicles on a road network. The output data from the temporal and spatial move-
ment of vehicles is used as a reliable forecast so the time-coordinated optimal power
flow (TCOPF) program can devise optimal charging scenarios of PHEVs in a local
electrical network. The effectiveness of the model is illustrated by presenting a multi-
day case study in an urban area. Results show a high level of detail and variability in
PHEV charging when a present-day carbon fuel mix is compared to one with lower
carbon intensity.
For sake of simplicity, natural gas infrastructure and Combined heat and power
(CHP) technologies included in the model previously are not incorporated in this
chapter; the case study solely focuses on linking transport and power sectors and
allows us to begin pondering the synergies between both sectors. First, an overview
of existing modelling methods and a brief literature review are presented, before
showing how two models - one an agent-based model of EV users and the other a
power-flow model - can work together to address the challenge of mobile loads in a
novel way.
1 This chapter is based on Reference 224.