Optimising DERs in energy service networks: a case study - Modelling Distributed Energy Resources in Energy Service Networks - page 110

Table 6.1 Average heat to electricity dwelling

demand ratio in a UK winter weekday

Time Heat to electricity ratio

12.00 a.m.-5.30 a.m. 1.17

6.00 a.m.-11.30 a.m. 5.01

12.00 p.m.-5.30 p.m. 2.87

6.00 p.m.-11.30 p.m. 3.95

Daily average 3.25

of 38% and a 55% thermal power recovery rate, hence an EUF chp of 0.93 and an
EHR chp of 0.69 that provide a very high power generating efficiency for a small-scale
unit. Within a single micro-CHP unit the electric power capacity is 1 kW el , while the
thermal power capacity is 1.44 kW th . In addition, the hot water storage capacity of
the system is 200 litres, which is equivalent to 7 kWh th , and is modelled with a 90%
efficiency performance.
The plug-in vehicle technical characteristics modelled correspond to the Nissan
Leaf model [219]. This unit has a battery capacity of 24 kWh that allows the user
to travel over 160 km in an all-electric mode, well over the daily average distance
travelled by urban vehicles [104,108]. Hence, it is assumed PHEVs only employ the
equivalent of 64 km per day, following the driving patterns described in Figure 6.3.
Concerning the charging rate of these mobile agents in a residential environment, a
3.12 kW capacity at a 95% efficiency was adopted. Additionally, for simplicity the
simulation considers that the PHEVs that are not on the road are parked and plugged
to the grid. This condition allows the PHEVs to provide a relatively small capacity
for V2G services, conceding to the grid a 10% of their battery capacity, an amount
equivalent to 2.4 kWh, which they can comfortably discharge without risking their
travelling priorities.

6.1.2 Description of case studies and energy system parameters
Once the features and assumptions of the networks, load data and DER technologies
are determined, various scenarios can be simulated with the purpose of evaluat-
ing different TCOPF formulations. Case 1 is the base case scenario where neither
PHEV nor micro-CHP technologies are present in the networks. As a result, it is
assumed boilers with an 80% efficiency satisfy the heat demand of customers. By
performing this reference load flow it allows us to quantify the status quo conditions of
the infrastructures and thus serves to compare and assess the influence other operating
strategies can have on key techno-economical parameters. Subsequently, according
to the objective function proposed and other constraints, the multiple impacts of
embedded technologies on the optimal operation of energy service networks can be
identified.
The flexibility of the TCOPF program allows us to assess many case studies.
However, aside from the reference case ( i.e. case 1), for illustrative purposes only six