Environmental Engineering Reference
In-Depth Information
residential area and one in each of the other two zones. To determine which power
node is the nearest to a plugged PHEV, only the geographical distance is considered,
so it is possible that a vehicle parked at an office is in fact closest to a node in the
residential zone. Consequently, there is no strict division between the urban zones
from the perspective of the DNO, yet the load of each node can be characterised
individually.
7.3.1.4 Electricity load profiles and network characteristics
Besides the demand for electricity to charge electric vehicles, the network needs to
supply load profiles of residential and commercial buildings. These profiles compli-
ment the energy requirements of the urban area. The load curves used in this case
study are based on the United Kingdom Generic Distribution System data [216],
commonly used to simulate different types of customers. All load profiles employed
are in half-hourly intervals typical of a UK winter period. As shown in Figure 7.2,
the 11 kV network has a radial topology and consists of four nodes. It is assumed
the system operates under balanced conditions and is represented by its positive
sequence network. Furthermore, the load in each node is considered to be a three-
phase balanced load. Electricity is provided from a high-voltage transmission source
and stepped down in the south of the city where node 1 is located. Nodes 2 and 3
represent the central residential area, while node 4 is in the north-east of town. The
base voltage and power used is 11 kV and 1 MVA respectively.
7.3.2 Case studies and energy system parameters
The case study explores three power scenarios. The first does not consider PHEVs,
hence serving as a benchmark of how the urban energy system performs without
them. The second scenario takes the current generation portfolio in the UK to depict
how optimal PHEV charging would look within the present context of prices and
emissions. The third scenario attempts to look at a future in which wind power has a
meaningful presence ( e.g. 20% of the fuel mix). The three scenarios consider price
and CO 2 emission variations during the day based on how the fuel mix is composed.
This data serves to incentivise charging as the objective function is expressed in
monetary terms, similar to the work presented in Reference 242, see Figures 7.3 and
7.4 which detail weekday data.
Once the ABM simulation is performed, all three scenarios employ the same
travel journeys and static loads; the spot prices and carbon emissions signals are the
only changing variables, therefore giving relevance to how price signals can influence
flexible demand. The simulation is done for a period of 72 h, from Thursday morning
up to Sunday morning, during which agent activities and demand profiles are suited
according to the day of the week. A key assumption from the simulation is that
all batteries have to be fully charged by 6 a.m. on weekdays and by 8 a.m. on the
weekend. Table 7.1 illustrates the energy system parameters used in the case study.
The values of all the variables depicted in the tables, unless specified, are in per unit
(PU) terms.
 
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