Environmental Engineering Reference
In-Depth Information
provided by the ABM simulation. Term (7.9) states that all nodal battery storage sys-
tems must have at all times a SOC equal to or greater than 0 and (7.10) specifies
refuelling batteries based on ABM predicted travel behaviour.
The TCOPF problem is programmed, executed and solved by performing a multi-
period non-linear optimisation using the gPROMS software [215]. Once the problem
is solved a summary report is provided, describing the following results:
Time consumed during the optimisation process;
●
Final value of the objective function;
●
Values for all variables during each time interval.
●
7.3
ABM-TCOPF case study for charging of PHEVs
7.3.1 Input data and assumptions
A case study of a small urban area has been devised to showcase the interoperability
between the ABM and TCOPF models. The aim of the case study is to demon-
strate that through ABM the load flexibility of PHEVs on electrical networks can be
estimated, thanks to its temporal and spatial features. This data then serves as a reli-
able forecast to support decision-making by distribution network operators (DNOs),
energy service providers and customers to sufficiently charge PHEVs in optimal times
irrespective of their location in the network. The case study is determined by many
factors; among these are:
Driver profiles (
i.e.
types of agents);
●
PHEV features;
●
City layout;
●
Static electricity demands (
e.g.
residential and commercial loads);
●
Distribution network characteristics.
●
7.3.1.1 Driver profiles
The model currently considers three types of PHEV drivers:
1.
People with a job, who may or may not have kids of school-going age;
2.
People without a job, but who have kids of school-going age;
3.
Pensioners and other people who do not have to go to work and/or to a school.
There are 14 agents who are loaded from a GIS file, 10 of which are of the first
category, 2 in the second and 2 in the third. Based on this distribution 250 agents
are generated, each with a different home and work address (if applicable) and a
certain number of children. In the model, 80% agents with a job work in an office
and the remaining 20% in a leisure centre or a shop. Additionally, approximately
10% of the PHEV owners have to work on Saturday. It is assumed that drivers do not
travel outside the city, thus they do not come close to consuming a full battery. This
fact ensures there is no 'range anxiety' in drivers given the city layout and vehicle
characteristics.
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