Environmental Engineering Reference
In-Depth Information
Table 6.5
Techno-economical DER parameters
Boiler efficiency
η
b
=
0
.
80
micro-CHP penetration
%
chp
2
=
0
.
27, %
chp
3
=
0
.
33, %
chp
3
=
0
.
30
micro-CHP efficiency
η
el
=
0
.
38,
η
th
=
0
.
55,
η
ts
=
0
.
90
P
chp
G
,
max
=
1kW
el
,
T
chp
G
,
max
micro-CHP capacity per unit
=
1
.
44 kW
th
TSOC
store
max
Thermal store capacity per unit
=
7kW
th
P
chp
F
=
10
P
chp
G
+
0
.
10
P
chp
2
G
micro-CHP operation
%
phev
2
=
0
.
27, %
phev
3
=
0
.
33, %
phev
3
=
0
.
30
PHEV penetration
PHEV charging efficiency
η
G
2
V
=
η
V
2
G
=
0
.
95,
η
V
2
R
=
1
P
phev
D
,
max
=
P
phev
G
,
max
PHEV capacity per unit
=
3
.
12 kW
el
Battery capacity per unit
EVSOC
store
max
=
24 kW
el
P
phev
F
20
P
phev
G
0
.
20
P
phev
2
G
PHEV operation
=
+
Table 6.6
Constraints of the energy system
0
.
94
≤
V
α
≤
1
.
06, 0
.
00
≤
P
chp
Gα
Electric nodes,
α
=
1, 2, 3, 4
≤
0
.
27
Tap-changer
0
.
95
≤|
t
|
1
≤
1
.
05
0
.
90
≤
p
α
≤
1
.
10, 0
.
00
≤
T
chp
Gα
Natural gas nodes,
α
=
1, 2, 3, 4
≤
0
.
388
Compressor
1
.
00
≤
r
1
≤
1
.
80
TSOC
store
max
,
α
Thermal storage capacity,
α
=
2, 3, 4
=
1
.
89
EC
store
α
=
1
.
89,
ED
store
α
Thermal storage energy,
α
=
2, 3, 4
=
1
.
70
EVSOC
store
max
,
α
Battery capacity,
α
=
2, 3, 4
=
6
.
48
V
2
R
store
α
Driving V2R energy,
α
=
2, 3, 4
=
2
.
592
PHEV utility factor,
α
=
2, 3, 4
UF
phev
=
0
.
4
PHEV ATR factor,
α
=
2, 3, 4
ATR
phev
=
0
.
1
G
2
V
store
α
=
3
.
41,
V
2
G
store
α
G2V and V2G energy,
α
=
2, 3, 4
=
0
.
616
Weight factors
λ
cm
=
0
.
5,
ω
=
0
.
5
6.2
Techno-economical results
6.2.1 Overview
Although the problems proposed for the case studies consist of over 11,000 variables,
this does not represent an issue for the TCOPF solver. Due to the effectiveness of
the gPROMS
TM
software a solution is reached after a few iterations. As a consequence
of managing so many variables the outputs from the time coordinated optimisation
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