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”VASILE ALECSANDRI” UNIVERSITY OF BACAU
FACULTY OF ENGINEERING
PROCEEDINGS OF THE 11th INTERNATIONAL CONFERENCE ON INDUSTRIAL POWER
ENGINEERING
The 11th Edition
BACĂU, ROMÂNIA
June 27 – 29, 2018
Editura Alma Mater Bacău
2018
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
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EDITORS-IN CHIEF
Roxana GRIGORE
Aneta HAZI George CULEA
TECHNICAL EDITORIAL STAFF: Sorin-Gabriel Vernica
“VASILE ALECSANDRI” UNIVERSITY OF BACAU-ROMANIA
ALMA MATER Publishing House “VASILE ALECSANDRI” UNIVERSITY OF BACAU-ROMANIA
Department of Energetics and Computer Science Calea Mărăşeşti 157, RO-600115 BACAU, ROMANIA
Tel: +40 234 542 411; Fax: +40 234 580 170
Editura Alma Mater Bacău 2018
ISSN 2069 – 9905 ISSN-L 2069 – 9905
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
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Scientific Committee
Jean-François BRUDNY (University of Artois, Bethune, France)
Vladimir BERZAN (Institute of Power Engineering of the Academy of Sciences of Moldova, Chişinău,
Republic of Moldova)
George CULEA (“Vasile Alecsandri” University of Bacau, Romania)
Mihai GAVRILAŞ (“Gh.Asachi” Technical University of Iaşi, Romania)
Mazen GHANDOUR (Lebanese University, Hadath-Beirut, Lebanon)
Roxana GRIGORE (“Vasile Alecsandri” University of Bacau, Romania)
Gheorghe HAZI (“Vasile Alecsandri” University of Bacau, Romania)
Aneta HAZI (“Vasile Alecsandri” University of Bacau, Romania)
Petru LIVINŢI (“Vasile Alecsandri” University of Bacau, Romania)
Sebastian MIRON (Research Center for Automatic Control of Nancy, France)
Florian MISOC (Kennesaw State University, USA)
Cristian NICHITA (University of Le Havre, France)
Florentin PALADI (State University of Moldavia, Chişinău, Republic of Moldova)
Valentina NICORICI (State University of Moldavia, Chişinău, Republic of Moldova)
Radu PENTIUC („Stefan cel Mare” University of Suceava, Romania)
Mihai PUIU-BERIZINŢU (“Vasile Alecsandri” University of Bacau, Romania)
Remus PUSCA (University of Artois, Bethune, France)
Raphaël ROMARY (University of Artois, Bethune, France)
Dan ROTAR (“Vasile Alecsandri” University of Bacau, Romania)
Joanny STEPHANT (University of Limoges, ENSIL, France)
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
4
CONTENT
1. GHEORGHE HAZI, ANETA HAZI, SORIN VERNICA - CONSIDERATIONS ON THE
DETERMINATION OF ENERGY LOSSES IN ENVIRONMENTAL NETWORKS 5
2. ANETA HAZI, GHEORGHE HAZI, SORIN VERNICA - IMPROVING THE OPERATING
DIAGRAM OF A SUBSTATION 14
3. BOSTAN VIOREL, BOSTAN ION, GUŢU MARIN, RABEI ION, DULGHERU VALERIU -
CFD SIMULATION OF THE MATHEMATICAL MODELS OF THE INTERACTION
BETWEEN THE BLADES AND FLUID
20
4. FLORENTIN PALADI, VLADIMIR PRIMAC - MUTUAL INFLUENCES OF THE MODERN
INFORMATION TECHNOLOGIES AND ENERGETIC INDUSTRY 25
5. MOHMED ASHGLAF, CRISTIAN NICHITA - POWER MANAGEMENT STRATEGIES
BASED ON ENERGY STORAGE TECHNOLOGIES - REVIEW FOR FUTURE
IMPLEMENTATIONS IN REAL -TIME EMULATORS
29
6. VERNICA SORIN-GABRIEL, HAZI ANETA, HAZI GHEORGHE, GRIGORE ROXANA -
PROPOSALS TO IMPROVE THE OPERATING REGIMES OF A GAS TURBINE PLANT 43
7. ROXANA GRIGORE, SORIN-GABRIEL VERNICA ,MIHAI PUIU-BERIZINȚU, SILVIU,
IFTIME - ASPECTS RELATED TO THE UTILIZATION OF GEOTHERMAL ENERGY FOR
THE PRODUCTION OF ELECTRICITY IN ROMANIA
47
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
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1
CONSIDERATIONS ON THE DETERMINATION OF ENERGY LOSSES IN
ENVIRONMENTAL NETWORKS
GHEORGHE HAZI1, ANETA HAZI1, SORIN VERNICA1
1“Vasile Alecsandri” University of Bacau, Calea Marasesti 157, Bacau, 600115, Romania
Abstract: The paper presents an analysis of the methodology for calculating the energy
losses contained in the normative NTE 013/16/00, the Energy Technical Standard for
Determination of Own Technological Consumption in Public Interest Electricity Networks.
The analysis is based on calculations made for a 20 kV network in Bacau County. It is
taken account of the requirements imposed by the distribution operator in the zone,
DELGAZ GRID SA.
Keywords: power system losses, medium voltage, calculation methodology
1. INTRODUCTION
By ANRE Order no. 26 / 22.06.2016 approved the Energy Technical Standard for Determination of Own
Technological Consumption in Public Interest Electricity Networks, NTE 013/16/00 [2]. The normative is aimed
at establishing the methods of determination and analysis of the own technological consumption (OTC) in the
public electricity networks.
The methods presented are the following:
a) The statistical method, which consists in determining the predicted OTC based on statistical data recorded in
previous periods, using linear regression relations.
b) Loss method on network elements consisting of predicted OTC calculation and technical OTC based on
loadings of network elements in different operating regimes and their technical characteristics.
c) The average network element method, which consists in calculating the predicted OTC and the technical OTC
achieved in a network or network zone based on the OTC calculated in a network element considered to be its
average element.
d) The electricity balance method, which consists of the OTC forecast and OTC based on the electricity balance,
the difference between the electricity input and the electricity output from the balance outline.
e) The efficiency method, which consists of the OTC forecasting and realization for a category of network
elements based on the transported electricity and its operating efficiency, determined statistically in the previous
periods.
Also, DELGAZ GRID, the Moldovan distribution operator, has developed a methodology for the determination
of OTC in public-interest electrical networks for the supply of domestic consumers [3]. This methodology
defines the way of calculating the own technological consumption for the distribution installations related to the
electricity supply of domestic consumers and represents a particularization of the method b) presented above.
In the present paper the authors make a critical analysis of the presented methodologies, especially variant b), as
compared to the variants applicable prior to the occurrence of NTE 013.
2. PRESENTATION OF THE NETWORK ANALIZED
The analysis was carried out on a 20 kV grid in the Dărmăneşti zone, overhead electric line LEA 20 kV Poiana
Uzului. The diagram of the network is shown in Figure 1.
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
6
250K
VA
100K
VA
LE
A 2
0kV
20 k
V
DC 35
2 12
1120
A2Y
SY
135
35
PT
A 3
PT
A 1
ST
AT
IA 1
10/2
0/6
kV
DA
RM
AN
ES
TI
PO
IAN
A U
ZU
LU
I
L1
L2
12
B A
MO
24
MO
24
3
AL
95+
120
250m
1 2
63K
VAD
CP
TA
7S
AL
AT
RU
C
6
99m35
1
PT
CZ
10
630K
VA
1
PT
CZ
8S
AL
AT
RU
C
A2Y
SY
150
5m
22m
A2Y
SY
150
SA
LA
TR
UC
22
95
4.8
61m
912
75K
VA
PT
A 5
TV
SA
LA
TR
UC
20
15m
1
35
100K
VA
SA
LA
TR
UC
PT
A 9
GA
TE
R
78
330m
22
1
100K
VA
DC
SA
LA
TR
UC
PT
A 6
35
63K
VA
SA
LA
TR
UC
1
2
400K
VA
AL
120 SA
LA
TR
UC
PT
CZ
4
28
30
30m
68m
1
SA
LA
TR
UC
35
SA
LA
TR
UC
2
22m
45m
38
43
49
MO
24
PT
A 1
1
319
42
MO
24
L2
L1
C.H
.E.
PO
IAN
A U
ZU
LU
I
6,3
MV
A
24,2
/23,1
/22/2
0,9
/19,8
Usc
=7,2
5 %
6kV
6,3
kV
6/0
,1kV
3,4
MV
A0,7
MV
A
20/0
,1kV
50
18m
1.0
03m
4 13
MO
24
3
50
3.0
00m
PT
CZ
15k
9k
7k
8k
160m
35
98m
3
100K
VA
SA
LA
TR
UC
150
PT
A 1
2
160K
VA
8m
3A
SS
29
SS
48
SS
5S
S38
SS
41
1
40
100K
VA
PT
A 2
SA
LA
TR
UC
MO
24
9b
1
PT
A 1
3S
AL
AT
RU
C
250K
VA
48
12
A2X
(FL
)Y600m
PT
AB
14
SA
LA
TR
UC 400K
VA
SF
6
48b1235
PT
A 1
UZ
PT
A 2
UZ
PT
A 3
UZ
PT
A 4
UZ
PT
A 5
UZ
100 K
VA
100 K
VA
100 K
VA
100 K
VA
100 K
VA
20m
R
AL
150
4.1
91 m
610
28
70
165m
70
140m
70
1044 m
50
148 m
50
20 m
11
40
41
43
58
89
70
30 m
70
724 m
70
1540 m
50
20 m
RE
TE
A P
RO
IEC
TA
TA
AL
150
830 m
Fig
. 1
Th
e d
iag
ram
of
the
net
wo
rk a
nal
ized
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The network includes an existing zone and a projected zone (surrounded by a yellow border). As a rule, for the
projected works, the distribution operator requires the influence of network expansion on network power losses.
Specific to this network is that hydroelectric power plant (CHE) Dărmăneşti has 2 different groups of different
powers that work alternately. Working with one group or another significantly changes the network regime.
The main charactheristics of the network are:
The length of 20 kV lines – 18.8 km
The active power consumed in the network in the existing situation – 2.01 MW
The reactive power consumed in the network in the existing situation – 1.25 MVAR
The active power consumed in the network in year I after commissioning (PIF) – 2.17 MW
The reactive power consumed in the network in year I after PIF – 1.34 MVAR
The active power consumed in the network in year II after PIF – 2.19 MW
The reactive power consumed in the network in year II after PIF – 1.36 MVAR
The active power consumed in the network in year III after PIF – 2.22 MW
The reactive power consumed in the network in year III after PIF – 1.37 MVAR
The maximum active power produced by CHE (with large group in operation)– 3.67 MW
The maximum reactive power produced by CHE (with large group in operation)– 1.5 MVAR
The active power produced by CHE (with small group in operation)– 0.29 MW
The active power produced by CHE (with small group in operation) – 0.12 MVAR
The power installed in transformers in the network stations, the existing situation – 2.65 MVA
The power installed in the transformers in the network stations, the projected situation – 3.15 MVA
3. LOSS METHOD ON NETWORK ELEMENTS
Among the methods listed in point 1 of the paper, the loss method on network elements is the only one widely
applicable if the electricity consumption characteristic is known (by type of consumer: domestic, industrial, etc.).
This method is based on the loss time τ, defined as the conventional time interval, in which, in a constantly
charged network element at maximum SM load, there would be losses of electric energy equal to those produced
in the case of its operation according to the curve of real load.
In old normative [1], it is calculated with one of the relationships:
T
T
TSM
SM
SM
3175 0 275
8760 0 363
.
. [h/year] (1)
SM
SMSM
T
TT
27520
10000 [h/year] (2)
Where TSM represents the duration of use of the apparent maximum load - a conventional time interval in which,
through a permanently charged network element at full load, a quantity of electrical energy equal to that
transported in the case of its operation according to the curve of real load. This amount can be determined by the
amount of energy transited:
M
2
Q
2
P
SMS
WWT
[h/an] (3)
where WP, WQ active energy [kWh/year] and reactive, respectively, [kVARh/year], transited in one year, and SM
maximum apparent power [kVA] recorded over the same period.
The annual energy loss on a variable load element is calculated with the relationship:
W P I RS
URM
M max 3 22
2 [KWh/an] (4)
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
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where IM represents the maximum load current associated with the apparent SM load, for the three-phase network
element operating at the voltage between the phases U.
In the new normatives [2], [3], the method of calculating the loss time τ is changed:
*T (5)
where T is the reference duration (typically 8760 h), and τ *, the loss factor, determined by the following
formula:
* 2(1 )u up k p k (6)
where p is a statistically determined coefficient with p € (0.15÷0.3), in the absence of other information it can be
considered p = 0.2 [2], and ku the filling factor of the load curve determined by the relationship:
max SMu
med
S Tk
S T (7)
2 2
P Q
med
W WS
T
(8)
In the normative [3], the following principles are proposed for the determination of MT / JT network losses:
a) for Pmax <100 kW, the energy losses will be calculated on the network elements: LEA / LES JT and in
TRAFO MT / JT;
b) for 100kW ≤ Pmax < 400 kW the energy losses will be calculated on the network elements: LEA/LES JT,
TRAFO MT/JT, LEA/LES MT;
c) for Pmax ≥ 400 kW the energy losses will be calculated on the network elements: LEA/LES JT, TRAFO
MT/JT, LEA/LES MT, TRAFO IT/MT.
4. CALCULATION OF ENERGY LOSS, CLASSIC METHOD, NTE 401
For the analysis of the methodology presented by NTE 013 [2], compared to the classical method NTE 401, [1],
we will determine the energy losses for the presented network using both variants. In order to determine the
energy losses, we need the power losses in the maximum load regime. For this we used the RP V3.1 software,
[4].
Power losses in characteristic regimes are shown in Table 1.
Table 1. Power losses in characteristic regimes
Note: Zone 1 – axle 20 kV, st. Dărmaneşti – CHE
Zone 2 – derivatives + transformation posts (PT)- existing
Zone 3 – projected network (including PTs), Total/OHL 20 kV/Trans 20/0,4 kV
Year
CHE with small group CHE with large group
Total
[kW]
Zone 1
[kW]
Zone 2
[kW]
Zone 3
[kW]
Total
[kW]
Zone 1
[kW]
Zone 2
[kW]
Zone 3
[kW]
Existent 79,84 9,11 70,73 - 90 20,7 69,3 -
Year 1 82,03 9,40 70,72
1,91
90,5 19,28 69,3
1,90
0,12 0,11
1,79 1,79
Year 2 82,75 9,74 70,74
2,27
90,42 18,84 69,3
2,26
0,13 0,13
2,14 2,13
Year 3 83,41 10,02 70,75
2,64
90,43 18,48 69,3
2,61
0,16 0,15
2,48 2,46
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Iron losses - Existing: 4,89 kW
Iron losses – projected: 4,89+0,725 kW = 5,615 kW
Determination of annual energy losses is presented in Table 2.
Table 2. Energy losses calculated using the NTE 401 method
No.
Characteristic features UM
Calculated
values
Calculation relations
0 1 2 3 4 5
1. Input data
T – reference duration
Pmax – the maximum active
power consumed in the
network, the existing situation
Qmax – the maximum reactive
power consumed in the
network, the existing situation
WP – the active energy
consumed in the network in
one year, the existing situation
WQ – the reactive energy
consumed in the network in
one year, the existing situation
h
MW
MVAR
MWh/year
MVARh/year
8760
2.01
1.25
2268
1186
1 year
wattmeters readings
warmth readings
reading counters
reading counters
2.
Calculation of loss
time for power
supply network of
domestic consumers
TSM – duration of use of the
apparent maximum load
τc – loss time for domestic
consumers
h/year
h/ year
1081.28
448.71
2 2
2 2
max max
P Q
SM
W WT
P Q
3175 0.275
8760 0.363
SMc SM
SM
TT
T
3.
Loss time for axle 20
kV
WanCHE - Energy
produced in one year by
CHE Uz
MWh/ year 9978.36 reading counters
PmaxCHE - Puterea
maxima CHE
MW 3.672 Load curve
TCHE - duration of use of
the maximum load for CHE
h/ year 2717.42
max
anCHECHE
CHE
WT
P
τCHE - loss time for axle
CHE
h/ year 1217.85
3175 0.275
8760 0.363
CHE CHE
CHE
CHE
T
T
T
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10
4.
Energy losses in the
existing situation,
axle 20 kV
ΔPz1 - Power loss in zone 1
(20 kV axle) in maximum
load regime
kW 20.7 From listing regime
ΔWz1exist - Energy losses
in in 20 kV axle, existing
MWh/
year
28.38
1000
11
CHEzexitz
PW
5. Energy losses in year
I after PIF
ΔPz11 - Power loss in zone 1
(20 kV axle) in maximum
load regime
kW 19.28 From listing regime
ΔPz31 - Power losses in zone
3, projected (20 kV lines) in
maximum load regime
kW 0,11 From listing regime
ΔWz11 - Energy losses in 20
kV axle
MWh/
year
26.43
1000
1111
CHEzz
PW
ΔWz31 - Energy losses in in
zone projected (20 kV lines)
in maximum load regime
MWh/
year
0.049
1000
3131
czz
PW
ΔWz11sup - Additional
energy losses in zone 1 (20
kV axle) due to the
projected load
MWh/
year
-1.95 11sup 11
1
z z
z exist
W W
W
6. Energy losses in year
II after PIF
ΔPz12 - Power loss in zone 1
(20 kV axle) in maximum
load regime
kW 18.84 From listing regime
ΔPz32 - Power losses in zone
3, projected (20 kV lines) in
maximum load regime
kW 0.13 From listing regime
ΔWz12 - Energy losses in 20
kV axle
MWh/
year
25.83
1000
1212
CHEzz
PW
ΔWz32 - Energy losses in in
zone projected (20 kV lines)
MWh/
year
0.058
1000
3232
czz
PW
ΔWz12sup - Additional
energy losses in zone 1 (20
kV axle) due to the
projected load
MWh/
year
-2.55 12sup 12
1
z z
z exist
W W
W
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1 1 T H I N T E R N A T I O N A L C O N F E R E N C E O N I N D U S T R I A L P O W E R E N G I N E E R I N G
11
In the table above, only the change in annual energy losses due to the extension of the network with 5
transformation stations was presented, this information being requested by the distribution operator.
5. CALCULATION OF ENERGY LOSS, NTE 013 METHOD
The power losses in maximum load regime remain as shown in Table 1. Energy losses are given in Table 3.
Table 3. Energy losses calculated using the NTE 013 method
No.
Characteristic features UM
Calculated
values
Calculation relations
0 1 2 3 4 5
1.
Calculation of loss
time for power
supply network of
domestic consumers
ku – filling factor of the load curve
p - statistically determined
coefficient
τr - Loss time, relative, for domestic
consumers
τc – Loss time for domestic
consumers
-
-
-
h/year
0.123
0.2
0.037
323
SMu
Tk
T
NTE 013
2)1( uur kpkp
c r T
2.
Loss time for axle V.
Uzului 2 (evacuation
CHE)
T – reference duration h/ year 8760
ku - filling factor of the load curve - 0.31 CHE
u
Tk
T
p - statistically determined
coefficient
- 0.2 DEGR P02-02-21, Ed.1
τr - Loss time, relative, for axle CHE - 0.139 2)1( uur kpkp
7. Energy losses in year
III after PIF
ΔPz13 - Power loss in zone 1
(20 kV axle) in maximum
load regime
kW 18.48 From listing regime
ΔPz33 - Power losses in zone
3, projected (20 kV lines) in
maximum load regime
kW 0.15 From listing regime
ΔWz13 - Energy losses in 20
kV axle
MWh/
year
25.34
1000
1313
CHEzz
PW
ΔWz33 - Energy losses in in
zone projected (20 kV lines)
MWh/
year
0.067
1000
3333
czz
PW
ΔWz13sup - Additional
energy losses in zone 1 (20
kV axle) due to the
projected load
MWh/
year
-3.04 13sup 13
1
z z
z exist
W W
W
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τCHE - Loss time for axle CHE h/ year 1217.85 TrCHE
3.
Energy losses in the
existing situation,
axle 20 kV
ΔPz1 - Power loss in zone 1 (20 kV
axle) in maximum load regime
kW 20.7 From listing regime
ΔWz1exist - Energy losses in in 20 kV
axle, existing
MWh/
year
25.21
1000
11
CHEzexitz
PW
4. Energy losses in year
I after PIF
ΔPz11 - Power loss in zone 1 (20 kV
axle) in maximum load regime
kW 19.28 From listing regime
ΔPz31 - Power losses in zone 3,
projected (20 kV lines) in maximum
load regime
kW 0,11 From listing regime
ΔWz11 - Energy losses in 20 kV axle MWh/
year
23.48
1000
1111
CHEzz
PW
ΔWz31 - Energy losses in in zone
projected (20 kV lines) in maximum
load regime
MWh/
an
0.036
1000
3131
czz
PW
ΔWz11sup - Additional energy losses
in zone 1 (20 kV axle) due to the
projected load
MWh/
year
-1.73 11sup 11
1
z z
z exist
W W
W
5. Energy losses in year
II after PIF
ΔPz12 - Power loss in zone 1 (20 kV
axle) in maximum load regime
kW 18.84 From listing regime
ΔPz32 - Power losses in zone 3,
projected (20 kV lines) in maximum
load regime
kW 0.13 From listing regime
ΔWz12 - Energy losses in 20 kV axle MWh/
year
22.94
1000
1212
CHEzz
PW
ΔWz32 - Energy losses in in zone
projected (20 kV lines)
MWh/
year
0.042
1000
3232
czz
PW
ΔWz12sup - Additional energy losses
in zone 1 (20 kV axle) due to the
projected load
MWh/
year
-2,266 12sup 12
1
z z
z exist
W W
W
6. Energy losses in year
III after PIF
ΔPz13 - Power loss in zone 1 (20 kV
axle) in maximum load regime
kW 18.48 From listing regime
ΔPz33 - Power losses in zone 3,
projected (20 kV lines) in maximum
load regime
kW 0.15 From listing regime
ΔWz13 - Energy losses in 20 kV axle MWh/
year
22.51
1000
1313
CHEzz
PW
ΔWz33 - Energy losses in in zone
projected (20 kV lines)
MWh/
year
0.048
1000
3333
czz
PW
ΔWz13sup - Additional energy losses
in zone 1 (20 kV axle) due to the
projected load
MWh/
year
-2.704 13sup 13
1
z z
z exist
W W
W
6. CONCLUSIONS
From the data presented above, the following results:
The NTE 013 normative introduces a number of new methods for calculating network power
losses. These methods do not add extra clarity and precision.
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The statistical method presented in the normative requires a series of accurate determinations of the
energy losses for at least 5 years. However, there is no precise method, not even through
measurements, due to the large number of measurement points that are needed.
The method of loss on network elements, the method tested in this paper, leads to lower energy
losses by about 28% for domestic consumers and by 11% lower on the 20 kV axle. The difference
is significant, but the normative shows the coefficient p determined statistically with a margin of
values, but does not show a practical method of determining it. For this reason, the user must take
the recommended value p=0.2.
In the example shown, the energy losses in the public network decrease due to the fact that the
extension of the network is near a source which reduces the losses in the evacuation line.
REFERENCES
[1] ANRE - Methodology for the determination of the economic section of the conductors in electrical
distribution installations of 1 - 110 kV, indicative NTE 401/03/00, approved by ANRE President's Decision
no.269 of 4.06.2003
[2] ANRE - Energy technical norm for the determination of the own technological consumption in the public
electricity networks, indicative NTE 013/16/00, approved by the ANRE Order no. 26 / 22.06.2016.
[3] DELGAZ GRID - Methodology for the determination of OTC in public-interest electrical networks for the
supply of domestic consumers, code DEGR P02-02-21, Ed.1, 2017.
[4] Hazi Gh., Application for the calculus regimes of the electrical power systems - RP V3.0, Romanian Tehnical
Sciences Academy, Modelling and Optimization in the Machines Building Field, Volume 2, MOCM-10, ISSN
1224-7480, pp. 45-50, 2004
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2
IMPROVING THE OPERATING DIAGRAM OF A SUBSTATION
HAZI ANETA11, HAZI GHEORGHE1, VERNICA SORIN GABRIEL1
1“Vasile Alecsandri” University of Bacau, Calea Marasesti 156, Bacau, 600115, Romania
Abstract: The connecting diagram of a substation must ensure its safe and economical
operation. Selection of the normal operating diagram may result in minimal energy losses in
the substation in condition of compliance with nominal parameters of equipment and
continuity of supply to consumers. In the paper a mathematical model for the calculation of
energy losses in transformers according to the load curve is made. Here are presented the
energy losses and short-circuit currents for a substation. Finally, the normal operation
diagram of the substation is proposed.
Keywords: substation, operating diagram, energy losses, short-circuit current
1. INTRODUCTION
The substations are the nodes in the power system where several lines and transformers are connected together,
[2]. Most substations have double bus connecting diagram, sectioned or not [1]. The transformer substations
have one or more transformers that can operate in a radial or parallel diagram, [4]. The normal operation diagram
of the substation is set so that the safe operation of the substation is maximum, the short-circuit currents on the
bus-bars are lower than the breaking currents of the breakers in the cells of the respective bus and the energy
losses are minimal.
The operation regime of the power transformers in the substations is decisive in assessing the energy losses in
the substation, [3]. Connecting the transformer in parallel leads to increased short-circuit currents, [5]
In this paper, a multi-function substation, ie connections, transformation, evacuation and distribution, is
analyzed, in order to establish an operating diagram that ensures short-circuit currents on the bus within the
permissible limits and minimum energy losses provided maximum safety is in use.
2. CALCULATION METHODOLOGY
The method of losses on network elements [6] is used. This consists in calculating the losses based on the loads
of the elements in the substation under different operating regimes and their technical characteristics.
The electricity losses in the substations are mainly determined by the electricity losses in the transformers. In the
case of load according to the load curve of the characteristic regime these losses are determined with the
following relationship:
2n
2max
scovar
Tconst
TTS
SPTPWWW [kWh] (1)
1 Corresponding author, email [email protected]
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where: constTW - the loss of active electrical energy in core that is independent of the load, in [kWh] ; var
TW -
the loss of active electrical energy in coil which is proportional to the square of the load, in [kWh]; oP - no-load
losses of the transformer, in [kW]; scP - load losses of the transformer, in [kW]; nS - the nominal apparent
power of the transformer, in [kVA]; maxS - maximum apparent power calculated for the characteristic curve
load curve, in [kVA];T – the duration of the characteristic regime, in [h]; – loss time determined for the
curve load of the characteristic regime, in [h].
Maximum apparent power is calculated with the relationship:
2max
2maxmax QPS [kVA] (2)
where: maxP - maximum active power, in [kW]; maxQ - maximum reactive power, in [kVar].
The loss time is:
*T [h] (3)
where * is the loss factor determined by the following relationship:
2uu
* kp1kp (4)
where: p=0.2 is a coefficient determined statistically; uk is the fill factor of the load curve that is determined
with the next relation:
T
T
S
Sk max
max
medu (5)
medS is the average apparent power, in [kVA]:
T
WWS
2r
2a
med
(6)
maxT is the usage time of maximum apparent power maxS , in [h]
max
2r
2a
maxS
WWT
(7)
where: aW , rW the active electrical energy in [kWh] and reactive, respectively in [kVar], passing through the
transformer during the characteristic regime.
The calculation of the short-circuit currents on the 20 kV and 0.4 kV bus is done by the absolute unit method,
[7].
3. THE NUMERICAL RESULTS
A study of a substation has been carried out to improve its operating diagram according to the calculation
methodology presented above. It is a distribution substation with two 110/20 kV transformers, fig.1. On the 110
kV side, the connecting diagram of the substation is a double bus diagram with 14 cells. On the 20 kV side there
are double bus diagram in U with 19 cells. The internal services of the substation are supplied by 2 transformers
of 20 / 0.4 kV.
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Fig.1. Single diagram of substation analyzed
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The normal operating diagram in the summer regime is with transformer 1 (T1) in operation and transformer 2
(T2) in the reserve with automatic release of reserves (ARR). The normal operating diagram in the winter regime
is with T1 and T2 in operation, in radial diagram with ARR on transverse coupling. A transformer is used to
supply internal services, TSI1 because it has Peterson coil and TSI2 is in reserve with ARR. Monthly average
loads of the two transformers are shown in Figure 2.
To improve the operating scheme, simulations were made for the following cases:
1- Operation with only one transformer all year
2- Operation with a transformer in summer and with two transformers in winter, in radial scheme, with
different load coefficients
3- Operation with a transformer in summer and two transformers in parallel in winter
The loads of the two transformers for the three simulations are shown in Figure 3 and Figure 4.
Fig.2. Real loads of the two transformers
Fig.3. The loads of T1
Fig.4. The loads of T2
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The annual electricity losses for different charging coefficients of the two transformers in winter period in case 2
are shown in Figure 5. It is seen that the smallest losses for the two transformers are obtained for a load
coefficient of 0.3 of the transformer T1.
The annual electricity losses for the three simulations for the optimum load factor of 0.3 of the transformer T1 in
simulation 2 are shown in Figure 6. It can be noticed that the lowest annual losses are obtained for the case of
operation with only one transformer, respectively T1 - summer and T2 - winter.
The short-circuit values, current and power, on the 110 kV, 20 kV and 0.4 kV bus for different operating regimes
are shown in Table 1. It is noted that the smallest values of short-circuit current and power are obtained for
operation with the transformer T1 and the internal service transformer TSI2. Because TSI2 is not a Peterson coil,
it cannot be used in the normal diagram but only as a reserve transformer.
Fig.5. Annual energy losses for simulation 2
Fig.6. Annual energy losses for the real situation and for simulations
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Table 1. The short-circuit values
Point of
short-circuit Operation diagram Voltage, [kV]
Short-circuit current,
[kA]
Short-circuit power,
[MVA]
k1 110 16.4 3127.4
k2 T1 20 4.4 152.5
T2 20 7.1 246.2
T1//T2 20 10.8 376.0
k3 T1, TSI1 0.4 2.9 2.0
T2, TSI1 0.4 3.8 2.7
T1//T2, TSI1 0.4 4.7 3.3
T1, TSI2 0.4 2.7 1.8
T2, TSI2 0.4 3.5 2.4
T1//T2, TSI2 0.4 4.2 2.9
4. CONCLUSIONS
The analysis of the power station, done in this paper, in order to improve its operating diagram to obtain the
lowest electricity losses and the lowest values of the short-circuit current, highlighted the following conclusions:
- the lowest electricity losses are obtained in case of operation with only one transformer, respectively T1 in
summer and T2 in winter. It cannot be operated with T1 all year long because the winter requirement exceeds its
nominal power.
- the lowest short-circuit currents are obtained with T1 and TSI2 operation. However, since the Peterson coil is at
TSI1, it is proposed to operate with T1 and TSI1.
By using a performance improvement diagram, it can achieve annual savings of 93 MWh compared to the
normal operation diagram of the station.
REFERENCES
[1] John D. McDonald, Electric Power Engineering Handbook, Second edition, CRC Press Taylor & Francis
Group, US, 2006
[2] Hazi A., Hazi Gh., Statii electrice moderne, Editura Pim, Iasi, 2013
[3] ABB S.p.A. , Power Products Division, Technical guide The MV/LV transformer substations, 2015
[4] Dominik Pieniazek, P.E., HV Substation Design: Applications and Considerations, IEEE CED – Houston
Chapter, October 2-3, 2012
[5] Malafeev A., Iuldasheva A., Short-circuit failures simulation for evaluation of structural reliability of power
supply systems, Procedia Engineering 129, 2015, pp.433 – 439
[6] ***NTE 013/16/00, Normă tehnică energetică privind determinarea consumului propriu tehnologic în
retelele electrice de interes public
[7] ***PE 134/95, Normativ privind metodologia de calcul al curentilor de scurtcircuit în retelele electrice cu
tensiune peste 1 kV
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3
CFD SIMULATION OF THE MATHEMATICAL MODELS OF THE INTERACTION
BETWEEN THE BLADES AND FLUID
BOSTAN VIOREL, BOSTAN ION, GUTU MARIN, RABEI ION, DULGHERU
VALERIU*
Technical University of Moldova, Chisinău, Republic of Moldova
Abstract: The effect of tip speed ratio (λ) or (TSR) has been examined for different chord
lengths. The equilibrium between TSR, chord length and type of chord is necessary to be
found in order to get a maximum coefficient of performance (Cp) for the wind turbine rotor.
Different chord lengths based on Wortman FX 63 137 and NACA 0018 are simulated for
different TSRs. This task is done using finite element analysis methods. In this scope, the
software ANSYS CFX was used. The simulation is done on the 3D CAD models of the
wind turbines. The blades of the turbines are based constructively on the chords mentioned
above. Also, by means of CFD analysis, it was evaluated the rotor’s performance equipped
with asymmetric airfoil blades (Wortman FX 63 137) when the blades are positioned with
camber in and out relative rotation axis. All CAE simulations were done using ANSYS
software. The Workbench Project schematic is presented in the appendix 1.
Keywords: wind turbine rotor, mathematical models, blades, coefficient of performance
1. INTRODUCTION
Wind energy has been used by mankind over thousands of years. For over 3000 years the windmills have been
used for pumping water or grinding (milling). And nowadays, in the century of information technologies, nuclear
energy and electricity, thousands of windmills are used for pumping water and oil, for irrigation and production
of mechanical energy to drive low-power mechanisms on different continents. Nowadays, the phrase “use of
wind energy” means, primarily non-pollutant electrical energy produced at a significant scale by modern
“windmills” called wind turbines, a term that attempts to outline their similarity to steam or gas turbines, which
are used for producing electricity, and also to make a distinction between their old and new destination.
The absolute majority of the sold turbines are with horizontal and vertical axis. In turbines with vertical axis the
wind direction is perpendicular to the axis of rotation, respectively, perpendicular to the ground surface.
Although vertical axis turbines have lost the competition, engineers come back again and again to this design
scheme, the main reason being the following two indisputable advantages [1]:
The generator, the multiplier and other functional components can be placed on the ground surface; the
gondola and massive tower are not required;
The turbine does not require a special device to track wind direction.
Unfortunately, the disadvantages of these turbines prevail compared to the advantages:
1. Wind speed in the adjacent to the surface layer is small. Thus, we save on tower construction, but lose
the power developed by the turbine.
2. Wind energy conversion factor into mechanical energy is lower.
3. Some types of turbines, such as the Darrieus or Evence turbines do not provide starting. An auxiliary
motor is required to start the turbine or a small turbine of Savonius type.
4. High power turbines need support cables, which considerably increase the occupied land area.
Replacing the main thrust bearing requires complete disassembly of the turbine.
___________________________ 1 Corresponding author, email [email protected]
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2. 3D MODEL OF THE TURBINE AND SIMULATIONS’ SETTINGS
As determined in the previous report, the chords that are going to be used for the blades are Wortman FX 63 137
and NACA 0018. The effect of tip speed ratio (λ) or (TSR) has been examined for different chord lengths. The
equilibrium between TSR, chord length and type of chord is necessary to be found in order to get a maximum
coefficient of performance (Cp) for the wind turbine rotor. Different chord lengths based on Wortman FX 63 137
and NACA 0018 are simulated for different TSRs. This task is done using finite element analysis methods. In this
scope, the software ANSYS CFX was used. The simulation is done on the 3D CAD models of the wind turbines.
The blades of the turbines are based constructively on the chords mentioned above.
Also, by means of CFD analysis, it was evaluated the rotor’s performance equipped with asymmetric airfoil
blades (Wortman FX 63 137) when the blades are positioned with camber in and out relative rotation axis. All
CAE simulations were done using ANSYS software. The Workbench Project schematic is presented in the
appendix 1.
2.1. Fluid Domain Modeling and Meshing
The rotor geometry, designed using SolidWorks, was then imported into the ANSYS DesignModeler software.
The dimensions of the computational fluid domain were chosen taking into account the recommendations of [2]
so that the boundaries of the field do not influence the free flow of the air. The simulated fluid domain was
divided into two subdomains: the Stator (static) subdomain and the Rotor subdomain inside of it (of cylindrical
form, which rotates around its axis). Figure 1 shows the considered fluid domains.
Fig. 1. The fluid domains.
The mesh used later on for finite method analysis was generated using the ANSYS Meshing Workbench. This is
an integrated software that offers various meshing strategies. After importing the geometric model the following
regions were defined: (Inlet – the face with black arrows on the perimeter), (Outlet – the opposed face of the
Inlet), (Openings – the four side faces), and the common regions between Stator and Rotor (Fluid-Fluid). The
basic dimensions of the mesh are as follow: the minimal size of the inflation around the blade = 0.7 mm and the
maximum size of the side of one face = 220 mm (fig. 3).
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The transition from the fine-meshed areas to the gross meshed ones was done by specifying the Growth Rate =
1.1 expansion factor. The maximum variation of the characteristic dimensions of two adjacent elements is not
bigger than 5%. The entire domain was meshed into approx. 4 200 000 finite elements.
The effects formed on the blades’ surfaces are very important because here it is where the lift and drag are
formed, boundary layer separation occurs and other important effects take place In order to obtain more accurate
results in the close proximity of the blades’ faces where the boundary layer forms, rectangular finite elements
have been generated by expanding them from the surface of the blade outwards [3]. This was done using
Inflation Layer technique around blades’ surfaces: Number of Layers = 9, the Growth Rate = 1.18 (relative
thickness between two adjacent layers), and Growth Rate Type = Geometric. Figure 2, (b) shows the mesh
details around the blade.
a
b
Fig. 2. Meshed fluid domain (a) and details of boundary layer around the blade (b).
2.2. Problem setup
Problem setup was done using CFD module of the ANSYS software. In order to verify the conversion efficiency
of the turbine, several modes have been simulated. To both airfoils are assigned different chord lengths and each
chord length is simulated under different tip speed ratios. The wind speed considered for simulations is 11 m/s.
The parameters of interest that were analyzed are presented in the table 1.
Table 1. Analyzed rotor parameters.
Airfoils Chord length, m Wind speed, m/s Tip speed ratios Rotational speed, min-1
NACA 0018
0.2; 0.3; 0.4 11
3 126
3.5 147
Wortman FX 63
137
4 168
4.5 189
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2.3. Solutions and CFD Results for NACA
0018
The solving of discretized equations was
performed in parallel using all 16 logical cores.
The goal is to obtain 5 kW of power at a wind
speed of 11 m/s so we have to determine for
which chord length that is possible. The variable
of interest is the power output. More
specifically, the power output for each case
chosen is shown according to the table 1.
The simulations were carried. By analyzing the
results, the graphs in the figure 3 were obtained.
From here, one can notice that the maximum
power for this turbine is when the chord length
is equal to 0.3 m running at a rotational speed
of 168 rpm, which corresponds to a TSR of 4.
As mentioned, the maximum power
output of
4700 W is obtained for the chord
length of 0.3 m at 168 rpm. At the
same rotational speed, for the chord
length of 0.2 m the value of power is
1760 W and for the chord length of
0.4 m the power value is 3100 W.
One might guess that a maximum
power can be obtained for a chord
length between 0.3 and 0.4 m so we
made a simulation to find that out.
The simulation was carried for a
rotational speed of 168 rpm. The
results presented show that for the
chord length equal to 0.35 m the
power output is equal to 3700 W so it
decreases. We can conclude that the
optimal chord length for this turbine
is 0.3 m.
1. 2.4. Model development and
analysis of the downscaled
blade section
Fig. 3. The parameters of the 5 kW rotor at the wind speed
of 11 m/s for NACA 0018.
Fig 4. Downscaled blade segment
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The downscaled blade segment
whose characteristics are analyzed in
the wind tunnel has the constructive parameters as shown in the figure 4. The segment has the same airfoil as the
blades for real wind turbine but scaled down 1:2. The segment has been obtained by 3D printing. After printing
the surfaces were smoothed so it minimizes the drag forces due to friction. Also on the two ends of the blade two
plates are mounted in order to reduce blade tip loses.
The results of the analysis of the blade segment are presented in the figure 5. One can notice that the maximum
performance of the blade, presented here in terms of Lift/Drag, is for an angle of attack 10 degrees. The
performance is not dependent on the wind speed
when their value is higher than 8 m/s. The
sensitivity of the blade is high when the wind
speed is lower than 8 m/s.
3. CONCLUSIONS
By experimental research there were determined
the performances of the downscaled blade
segment (scale 1:2) based on NACA 0018
airfoil in terms of Lift over Drag forces for
different wind speeds and for different angles of
attack.
The CFD simulation is applied on the
downscaled wind rotor in order to determine the
aerodynamic performances. The CFD model
applied here is the same as the one used for
simulating 5 kW wind rotor.
Experimental research on the built downscaled
wind rotor is to be carried out in the wind tunnel in order to determine the aerodynamic performances. The
results are to be compared with the ones obtained by CFD simulation using ANSYS CFX in order to validate the
CFD model used for simulating the 5 kW wind rotor.
REFERENCES
[1] Bostan I., Gheorghe A., Dulgheru V., Sobor I., Bostan V., Sochirean A. Resilient Energy Systems.
Renewables: Wind, Solar, Hydro. - Springer, VIII, 2013. - 507 p. – ISBN 978-94-007-4188-1
[2] Mohamed M. H., Ali A.M, Hafiz A. A. CFD analysis for H-rotor Darrieus turbine as a low speed wind
energy converter. Engineering Science and Technology, an International Journal. Volume 18, Issue 1, March
2015, Pp. 1-13.
[3] Bostan V. Modele matematice în inginerie: probleme de contact. Modelări si simulări numerice în aero-
hidrodinamică. Chisinău: S.n., 2014. 470p. ISBN 978-9975-80-831-6.
Fig 5. Blade segment performance determined in the wind tunnel.
Fig 6. 3D printed downscaled blade segment and tunnel
wind tunnel
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4
MUTUAL INFLUENCES OF THE MODERN INFORMATION TECHNOLOGIES
AND ENERGETIC INDUSTRY
FLORENTIN PALADI1, VLADIMIR PRIMAC1
1Faculty of Physics and Engineering, Moldova State University, str. Alexei Mateevici, 60,
MD-2009, Republic of Moldova
Abstract: Blockchain, autonomous vehicles, 3D printing, and robotics are coming swiftly in our
daily life. All new inventions, systems and devices of information technologies can't work
without power. Even IT researches can't be performed without energy supply. At the same time
such essential progress in IT industry has increased energy consumption and motivated Energetic
Industry also to progress and invent new technologies. Even today's energy distribution is
impossible without information systems and automatic control.
The aim of this presentation is to explore the mutual influence of IT and Energetic Industries, and
identify the impact of potential mutual influences.
Keywords: information technologies, renewable energy, blockchain, smart grid, digitalization
1. INTRODUCTION
Impetuous evolution of Information technology industry leads to significant changes in Energetic sector.
Growing number of electronic devices, invention of new technologies, such as blockchain and electrical
vehicles, introduction of robots in industry and other domains leads to permanently growing of energy
consumption. Energetic industry also is one of beneficiary of IT progress, so it can provide needed amounts of
electrical power.
The digital progress significantly affects the power industry. Previous great steps in power industry were aimed
to develop hardware, which is capable to generate cheap or costless renewable energy. Next years the main
scope will be to improve energy generation systems and to make them smarter and more efficient.
According to the World Economic Forum, it is possible to generate $1.3 trillion by digitalizing electricity
generation worldwide in only 10 years - between 2016 and 2025 [1].
This digitalization lists 5 initiatives in particular:
- better management of asset performance
- real-time platforms data
- integration of energy storage
- customer-centric solutions
Significant changes in the power industry are long overdue. Networks used in power industry permanently
become much more complex. This is caused by progress in renewable energy power generation. Another factor
of influence to the networks is the increasing number of small or even individual distributed power producers.
Demand on power is increasing worldwide, but infrastructure is old and sometimes it is difficult to meet the
demand in new conditions. Used equipment is difficult and costly to maintain. But the positive thing is that
government and international regulations are driving the power industry to be much more efficient and cleaner.
Intervention of IT in power industry is expensive, but technology's ability to improve productivity can pay
dividends.
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2. CONSUMPTION OF HUGE AMOUNT OF ENERGY BY IT INDUSTRY AND CAUSES OF THIS
The most energy-intensive segments of the world economy since the beginning of the industrial revolution are
metallurgy and heavy engineering. They are followed by chemical and oil refining complexes. But who would
have thought that information technologies can consume more power than is spent for the smelting of metals and
energy supply for the housing and communal services of megacities? According to analysts of the world IT
market, work with bitcoins that includes mining, storage and transactions, require as much electricity as is
consumed by almost all countries in the world. At the same time, if last year global energy consumption
amounted to about 300 TWh per hour, then by 2025 this energy volume could increase by 10 times, almost 3000
TWh per hour. And IT share in this volume will be approximately one fifth - 600 TWh that is twice more than
today's annual world energy consumption [2].
This is caused by the introduction of new and expansion of traditional information technologies, for example,
online education, e-commerce, fifth generation mobile communication (5G), Internet of Things (IoT), cloud
computing and storage systems (cloud data centers), artificial intelligence and, of course, rapid increase of smart
devices - their amount has already surpassed the population of the Earth, their data are stored in server farms,
which require huge amounts of energy. Obviously, slowing down, and especially stopping the development of
innovative IT tools and services, can be equated with crimes against humanity, because, according to the UN,
Information Technologies and the Internet in particular are not just valuable resources for ensuring people's
livelihoods, but also their inalienable rights.
But how to proceed if these goals require expanding the construction of energy-generating enterprises, which are
known to be the largest pollutants of the atmosphere? According to analysts of the online edition Climate Home
News, which owns the largest global network of correspondent points, billions of mobile devices used daily over
the next decade will cause emissions of 3.5% of the total amount of pollutants emanating from energy generating
companies, working on hydrocarbon energy carriers, and by 2040 this share will increase to 14%.
The answer is obvious: the development of alternative energy source, such as solar and wind power stations
(SES and VES), and of course smart energy management.
3. BLOCKCHAIN AND ENERGY SECTOR
Blockchain is a new technology developed to enable peer-to-peer transactions without an intermediary. This
technology can change the way we process/handle the transactions. New model has the scope to move away
from a centralized structure, (markets, exchanges, trading platforms, big companies) towards decentralized
systems, (end customers can interact directly) [3].
Outside of the financial sector, the energy sector is one of the industries which can be significantly affected by
blockchain invention.
One of the pilot projects of blockchain usage in energetics is implemented in Brooklyn, New York. There was
created a microgrid which consists of 10 houses. Five of them are equipped with solar panels. All unused energy
is sold to neighboring buildings. While all the buildings are still connected to the microgrid, transactions are
managed and stored on a blockchain. Usage of smart meters and blockchain technology allows easing
transactions between neighbors [4].
About 5% of energy is lost between power plant and our home. It is caused by location of power plant, which is
situated far away of living area. Power plants can't be located closer because of ecological reasons (coal and gas
stations) or security reason (nuclear energy). But renewable energy gives us opportunity to turn into source of
energy practically every building. We can equip our house with solar panels, heat pump, and wind turbine.
Initially grids were built to function only in one way: to transport amounts of energy from the producer to our
houses. But with renewable energy we can become not only consumers. We can sale surpluses of generated
energy.
But managing of real-time optimization of consumption and trading between neighbors requires complete update
of existing grid. It requires new technologies for track energy usage and digital payments. Such inventions are
named "smart grid".
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Smart grids are modernized power grids that use information and communication networks and technologies to
collect information on energy production and energy consumption that automatically increases efficiency,
reliability, economic benefits, and the sustainability of production and distribution of electricity.
Blockchain can help us with all listed above processes - we are able to trade it directly with other consumers in a
peer-to-peer network.
4. THE INFLUENCE OF IT ON OIL AND GAS INDUSTRY
Oil and gas are the fuels which drive our world. Even with all the talk about renewable energy, these two
hydrocarbons are still the base of massive amount of electricity power in our world, give us possibility to travel
and keep us warm. But decreasing in the demand for this fuel in some regions of the world leads company to
decrease amounts of costs and expenses and to do technological processes more efficient.
4.1 Digital oilfields
One of the main steps in order to remain efficient on the market is to cut down operational costs. And
Informational Technologies is the best place to find such possibilities. The Modern computational and
modulation methods and fast processing systems in combination with huge amounts of useful data can help in
developing of "digital oilfields". Such invention gives possibility to engineers and geologists to simulate an
entire oilfield. These simulations are used in order to find the most optimal spots for oil and gas extraction with
minimum expenses in drilling and extracting maximum amounts during service. These informational analytics
reduces number of data operators, which reduces staffing costs and improves overall efficiency of the industry
[5].
4.2 New Oil and Gas fields’ exploration
In time all readily available sources begin to end. Nowadays it is becoming increasingly difficult to find new
reservoirs. Modern computers with pool of available data are able to find new reservoirs which were impossible
to find just several years ago.
4.3Distance doesn't matter
The amount of available reservoirs in areas close to ports and cities is permanently decreasing and it leads to
moving towards the trend of offshore drilling. Informational technologies are powering these extractions. There
is no need of big bases in remote areas and engineers, management and even doctors can contact workers
through video conferencing in remote areas instead of visiting them in any situation.
4.4 New approaches
Horizontal drilling and hydraulic fracturing can't be performed without support of IT industry. The Key to
success in this approach is it supports: 3D seismic modeling, cloud computing and big data.
4.5 Fuel technology conversions
Today it is said everywhere about harms of using hydrocarbons. But development of technologies, used to find
new sources of Oil and Gas can be easily transferred to the industries - to find and discover new mineral deposits
of useful rare metals [6].
5. CONCLUSIONS
This article has covered only a couple of aspects of IT influence over the energy sector. At the same time the
development of renewable energy also causes drastically changes in energy management which is performed by
information systems. This mutual influence will be increasing over the time and both industries will gain.
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REFERENCES
[1]http://reports.weforum.org/digital-transformation/electricity-generating-value-through-digital-
transformation/ (10.05.2018)
[2]http://www.gadgetsshop.ru/2018/01/zelenaya-energetika-v-it-ili-kak-bitkoiny-vliyayut-na-
ekologiyu.html (08.05.2018)
[3] https://www.worldenergy.org/news-and-media/news/the-potential-for-blockchain-technology-in-the-
energy-sector/ (05.05.2018)
[4] https://richtopia.com/emerging-technologies/blockchain-renewable-energy-sustainability (14.05.2018)
[5] https://www.renewableenergyworld.com/articles/2018/02/blockchain-could-change-everything-for-
energy.html (10.05.2018)
[6] https://www.linkedin.com/pulse/importance-information-technology-oil-gas-industry-today-fred-
zillman/ (09.05.2018)
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5
Power Management Strategies Based on Energy Storage Technologies - Review for
Future Implementations in Real-Time Emulators
MOHMED ASHGLAF, CRISTIAN NICHITA
University Le Havre Normandie
Research Group in Electrical Engineering and Automatics - GREAH
75 rue Bellot, 76058, Le Havre Cedex, France
Abstract— Electrical energy storage (EES) system has been a key for enabling complex power systems such as
smart grids and intermittent renewable power resources to be reliable. This paper presents an up-to-date
technical review for different energy storage systems. It synthesizes the main working principle of some storage
technology and highlighting the key advantages, disadvantages and main applications when integrated with
power systems. A review of recently studies has been investigated in the field of energy storage systems
integrated with power system. This review provide a guideline for further technological development and new
applications in order to improve the efficiency of wind/tidal real time emulators designed for renewable energies
research integrating the power supply systems.
Keywords - Storage Energy; Power Management; Real Time Emulators;
1. INTRODUCTION
In GREAH Laboratory researchers have developed a real time emulators for wind systems, represented in
Figure 1, and for tidal hybrid systems shown in Figure 2[1-2].
In order to optimize the generated power studies, energy storage units has to be be implemented.
Figure 1 Wind emulator in GREAH
We considered that this development should be done by firstly studying the new storage technologies that could
be implemented in our emulator structures, that's why we did this state of the art.
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Figure 2 Tidal emulator in GREAH.
“Electrical Energy Storage project team” this “White” paper [3] has been prepared by Special Working Group on
technology and market watch, in the IEC Market Strategy Board, with a contribution from the Fraunhofer Institut
für Solare Energie systeme. This paper treated the main three roles of ESS in order to improve power quality by
maintaining frequency and voltage, reduce electricity cost and improve the reliability of power supply, also this
paper discussed in brief, present and future market needs for EES technologies, reviews their technological
features. While in [4] an up-to-date technical review for different types of ESS has been presented. Operation
principle of each storage technology, advantages, disadvantages and main applications explained when integrated
with power systems.
Authors in [5] addressed intensively ESS through the state-of-the-art technologies available, and where they
would be most fit in a power generation and distribution system. As well as, an overview of the main operation
principles, performance features the up-to-date research and development of most interesting EES technologies,
and ESS classification based on the types of energy stored has been illustrated. Finally, a overall comparison and
an potential analysis of the reviewed application technologies are presented. Another research in [6] also discusses
the multi types of storage technology, the development trend and the different applications of storage technology
in the power system. The need to store energy, various types of storage techniques and their field of application
has been described [7]. As well as, characteristics and comparison been presented in order to determine the most
appropriate technique for each type of application. The integration of energy storage systems within renewable
resources specifically wind resource was demonstrated in [8]. It simulated 1MWh of time, 1 to 9 hours of high and
low wind energy, and the main objective of this research was to find characteristics and the most suitable type of
energy storage system that can be used with such power generation system. The final result of this work shows
that, 67.80% of energy gained in high wind energy scenario, whereas 19% stored in low wind energy scenario.
Also, another research [9] in various energy storage technologies been analyzed and compared for marine
application, emphasize was given to the role of energy storage systems for reducing power fluctuation in
renewable energy sources.
However, in this paper, the most recent up-to-date research and development of EES are comprehensively
reviewed and meanwhile variants of different applications are categories based on power management strategies.
This paper is organized as follows: first section an introduction to energy storage systems. Second section talks
about ESS history, third section describes the application of ESS and its role in the power system, fourth section
describes the basic operation principle of ESS. Fifth section about the application of ESS in power system. Sixth
section compares between different types of ESS, and finally, conclusion of this literature review.
2. HISTORY OF ENERGY STORAGE SYSTEMS
Thermal energy storage system TESS is one of the oldest forms of energy storage systems in the world where ice
is harvested for food preservation and cooling purposes, in China cold water was injected into an aquifer.
Subsequently, it was noted that water had maintained it temperature for long period of time [10] Pumped hydro
energy storage systems PHS has existed for a long time – the first pumped hydro storage plants were used in Italy
and Switzerland in the 1890s.
The Wallace Dam Pumped Hydro Project was placed in full commercial operation in December 1980. In 1999
first seawater pumped hydro plant was built in Japan (Yanbaru, 30 MW) [3].
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First fuel cell of about 2000 years old was discovered, near Baghdad, Iraq and it was first described by German archaeologist Wilhelm Koning. Then, the first step began when Emile Alphonse Faure invented and patented the so-called sticky plates in 1880; large scale production of lead-acid batteries was developed.
The next step was to increase storage capacity and electric power in battery. The most common used types of
batteries Lead-acid battery invented by Gaston Plante in 1859 at France and it is the oldest and most widely
used. Flow Battery FB invented by Lawrence Thaller in 1976 [11]. Hereafter summarized history of batteries in
table 1, [12].
Table 1 History of batteries
The first energy storage program was an integration of batteries with photovoltaic and wind energy systems in
1978, titled “Batteries for Specific Solar Applications” [13].
3. THE ROLE OF ENERGY STORAGE IN POWER SYSTEMS
The Energy Storage Systems (ESS) is a key component in the electrical generation and stability, energy storage is
one of the features of this century, where the need of energy increasing dramatically. Electrical interruption or its
instability is one of the big challenges at all levels of life and may cause a death to a patient in Intensive Care Unit
(ICU) or big losses in business. From this point of view, the need for storing energy becomes very essential to
save lives and money. Also, using energy storage system will decrease the gas combustion hence the gas emission
and reducing the environment pollution. The following graph shows the contribution of energy storage systems in
power demand and generation for different periods of time during the day [14]. As shown in Fig. 3&4, using ESS will improve the performance of power generation system from first step of
power generation, power transmission and finally the end user, these attractive vales that can be added by the ESS such as: i) reduce generation cost during peak demand periods, (ii) maintain continuous and flexible supply, (iii) management in power generation, (iv) improving power quality/ reliability, (v) help insertion of smart power grids.
Developer /
Inventor
Countr
y
Year Invention
Luigi Galvani Italy 1786 Animal
Electricity
Alessandro Volta Italy 1800 Voltaic Pile
John F. Daniell Britain 1836 Daniell Cell
Sir William Robert
Grove
Britain 1839 Fuel Cell
Robert Bunsen German 1842 Used liquid electrodes to
supply
electricity
Gaston Plante France 1859 Lead Acid Battery
Georges Leclanche France 1868 Leclanche Cell
Thomas Alva
Edison
United
states
1901 Alkaline/Accu
mulator
T.A. Edison & Waldemar ungner
U.S.A &
Sweden
1895-1905
Nickel-cadmium /
nickel-iron
Andre 1930 Silver/oxide
zinc cell
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Figure 3 Effect of energy storage system in power generation and consumption [15]
Figure 4 Application of ESS in power system [15]
Batteries are classified as small-scale storage systems in low distribution system. More than 64% of the utilized capacity of batteries is used is take place in the emergency units in the hospitals. Besides that, several battery systems (19%) are located in the office building as show in Fig. 3.
With regard to the energy storage market, the high percentage (99%) capacity of installed storage is provide by plants of pumped hydroelectric as in Fig.4.
Figure 5 utilization of Battery as energy storage system [16]
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Figure 6 Installed capacity of electrical storage systems [16]
4. BASIC THEORY OF ENERGY STORAGE SYSTEMS
4.1. Thermal Energy Storage Systems TES Thermal energy storage (TES) is a process of stocking excessive energy in a form of heat, by heating or cooling a storage medium so that the stored energy can be used later for heating and cooling applications and power generation. TES systems are used particularly in buildings and industrial processes [17], energy can be stored by means of TES depends on two basic principle, sensible heat (e.g. water, rock) and latent (e.g. water/ice, salt hydrates) [18].
Figure 7 Thermal energy storage system [19]
Main thermal storage techniques (Fig. 5):
Sensible Thermal Energy Storage
Underground Thermal Energy Storage (UTES)
Phase Change Materials for TES
Thermal Energy Storage via Chemical Reactions [17]
4.2. Pumped Hydro Storage System PHESS The PHS considered as a large scale storage unite represents about 99% of worldwide storage capacity, the main idea of PHES is to pump and store water at high levels during off-peak periods, and reuse it to operate power generation turbines during peak periods as shown in Fig.6. In another ward, store electric energy in another form of energy "hydraulic potential energy". Pumping and generating generally follow a daily cycle but weekly or even seasonal cycling is also possible with larger PHES plant [20].
The overall efficiency of this type of ESS can be determined by finding out the ratio of the energy used to pump up the water and produced energy. In addition, the period of storing and production is an important factor. The following two expressions used to find out the efficiency of the system: [21]
.g.h .vpumping
p
E
(1)
. . . .generator gE g hv (2)
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4.3. Flywheel Energy Storage System FESS
In a very simple explanation the flywheel is made of a metal in cylindrical form, supported by magnetic bearing
located in vacuum chamber to reduce friction losses and connected to a rotor of generator/motor as in Fig.7.
Kinetic energy can be stored in the flywheel in rotation form [22]. The amount of stored energy depends on the
inertia and speed rotation of the flywheel. The energy absorbed and released by a rotating flywheel rotor [23]. The
kinetic energy that can be stored in flywheel according to the following equation:
21.
2E I (3)
Where: I: inertia momentum of the flywheel
Ω: angular speed There are some factors affect the maximum energy that can be stored in flywheel system, these factors can be
expressed in the following relation:
msp sE K
(4)
Where:
Esp: maximum specific energy density
σm: Maximum tensile strength of flywheel material
ks: Shape factor
ρ: Density of the flywheel material [24]
Figure 8 Flywheel energy storage system FESS [5]
4.4. Compressed Air Energy Storage System (CAES) The intermittency of wind was a main reason to use this technology, the excessive electrical energy used to operate series of air compressors, to store electrical energy in a form of compressed air in reservoirs or over ground tanks, in order to reuse it later in peak demand (Figure 9).
Figure 9 Compressed Air Energy Storage System CAES [5]
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4.5. Batteries A battery storage system mainly composed of electrode, anode and electrolyte (Fig. 9) which can be at solid, liquid or ropy/viscous states, electrical energy can be stored in a form of controlled chemical reaction which reproduces electricity when it is needed "bi-directional convert of energy" [5]. In this category of ESS there are main common types of Batteries namely (Lead-acid battery, Sodium Sulfur battery, Lithium ion battery, Metal air battery, Flow battery) [25].
Figure 10 battery schematic [15]
A. Superconducting Magnetic Energy System SMES An SMES device is a dc current device that stores energy in the magnetic field. The dc current flowing through a superconducting wire in a large magnet creates the magnetic field [26]. The coil is made of Niobiumtitane (NbTi) filaments and cooled below its superconducting critical temperature, one of the attractive properties of SMES that it can be fully charged and discharged in a very short of time, and high value of energy can be stored in it (Fig. 10). The energy stored in the coil, E, is given by expression [27].
21.
2E L I (5)
Where: L inductance, & I current.
Figure 11 Superconducting Magnetic Energy System SMES [27]
4.6. Fuel Cell Energy Storage System FCESS Fuel cell based on the separation of water into its original components Hydrogen and Oxygen as shown in Fig. 11. Hydrogen is used to generate electricity; also it can be transported in portable cylinders or tanks and stored for long time. [28].
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Figure 12 hydrogen storage and fuel cell [5]
5. CLASSIFICATION OF ENERGY STORAGE SYSTEMS
Electrical energy storage systems can be classified according to the energy storage principle into five categories as
shown in the following block diagram. However, storage systems can be classified also according to different
factors such as the period of charging and discharging: long period such as (high-energy batteries like sodium
sulfur battery and flow battery), and short period such as (super-capacitors and flywheels). In addition, another
classification can be done according to their load capacity as: low, medium and high storage capacity.
FigureError! Reference source not found. Classification of electrical energy storage systems
6. APPLICATION OF ESS
6.1. Electro-chemical Energy Storage Systems Hereafter a review - papers issued in this type of energy storage systems used as storage unit or compensator in renewable power generation systems. Some of the drawbacks facing the integration of energy storage into the grid been addressed and evaluated in [29] it demonstrated the main battery technologies for energy storage, identify their challenges, and provide perspectives on future directions. Found that, battery systems can offer a number of high-value opportunities and lower costs can be obtained.
This review includes: sodium-sulfur batteries that are commercially available for grid applications, redox-flow batteries that offer low cost, and lithium-ion batteries whose development for commercial electronics.
However, author in [30] treated power compensation via energy storage system (Flow Battery Storage FBS) as main objective. The flow battery (FBS) connected with thermal and hydro power plant; the function of suggested item FB is to maintain power plant output in balance state and at the predicted value. The proposed system modeled in MATLAB, the obtained results indicates that the system well balanced.
Also, Battery Storage System BSS in [31] considered as main part for efficiency improvement of water desalination unite supplied by renewable resource PV-wind turbine power generation system to eliminate the intermittently of required electrical power by the desalination unite as Fig. 12.
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Figure 13 Hybrid energy source with Batteries
6.2. Thermal Energy Storage Systems In [32], the study focused on thermal storage energy (storing energy in a form of heat or cold). Compared with other forms of energy storage techniques specifically "Transfer energy pumping station" (STEP), which is a form of kinetic energy. Results show that, storing energy in this form is suitable for long-term, more efficient and less expensive than the other forms of energy storage systems. Whereas, storing energy in form of heat is more complex to be implemented and expensive. In [33] & [34], these two papers a literature review emphasize on the development of thermal energy storage systems in France. To provide higher storage energy, Phase Changing Materials (PCMs) types of materials used and because of their range of state change, applications in energy storage for heating and cooling purposes and its integration with power generation systems (Fig. 13).
Figure 14 Thermal station in France
6.3. Mechanical Energy Storage Systems
6.3.1. Pumped hydro storage system
The effect of pumped hydro storage system on fuel consumption in hybrid diesel power generator and wind
turbine has been detailed in [35]. The target of this research is to maximize wind turbine penetration in the power
grid of an island in Canada where pumped hydro energy storage system and battery bank been used to overcome
the problem of power instability and fuel consumption. System has been simulated in Simulink and results
indicate that using of hydro energy system with battery bank has a positive effect on wind energy penetration,
reduces the power fluctuation and fuel consumption. Consequently, emission of greenhouse gases reduced. In
[36], pumped hydro storage (PHS) is implemented to support the standalone hybrid solar-wind system connected
to a micro-grid. This paper addressed a new solution for the challenging task about energy storage. Results
indicate that the intermittent nature of the renewable resources can be compensated by introducing the PHS
technology.
6.3.2. Flywheel energy storage system
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Authors in [23] analyzed the maximum energy storage and energy sufficiency of flywheel, results indicates that
there is still a part of the energy even when FESS doing the effective energy output, so Lead acid-battery will be
combined with FESS to form hybrid energy storage system.
This hybrid energy storage system will be installed in a grid and non-grid wind power generation system; results
obtained indicate that using such hybrid system would be effective and efficient to store energy with high
efficiency.
6.3.3. Compressed Air Energy Storage System In [37]-[38], two cases of studies were carried out in Germany and Denmark 2007, 2009 respectively, to
investigate the feasibility and contribution of CAES in wind power generation. Both obtained results indicate
that, the use of such energy storage system will be efficient and feasible in case of large-scale power generation
systems only. A state-of-art addressed the working principles, current researches and developments of CAES, as
well as over view of geological study of underground cavern for CAES in [39]. Besides that, study of
characteristics and feasibility, application and challenges faced this type of energy storage system has been
carried out. The final conclusion drawn from this study shows that, CAES efficiency and performance has a
direct relationship with the scale of the production unit.
A small- compressed air energy storage system proposed as a conventional concept of an integrated induction
generator (IG) in [40]. The system consists of 3 main components: air compressor, energy storage system and
power generation. IG is used to produce electrical energy. Thus, there is no fuel will be burned and no pollution.
This research addressed the modeling and simulation to obtain the characteristics of the new concept of CAES
power plant, which can be helpful in system designing of renewable energy conversion. 6.4. Electrical Energy Storage Systems An overview of the SMES technology and their applications in electrical power and energy systems carried out in
[26]. The SMES has been classified into three main groups (Thyristor-based SMES, voltage-source converter-
based SMES, and current-source-converter-based SMES). Due to the instability of the resources of renewable
energy, ESS is needed to be used in order to mitigate power fluctuation in power systems. In [41], SMES been
used to stabilize and control power generated by wind turbine, the dynamic performance of the suggested systems
is fully validated by computer simulation. The obtained result proved the efficiency of such storage unit to
stabilize the system efficiently and quickly. While in [42] the SMES is used to improve automatic generation
control (AGC) of interconnected hydrothermal power generation system. From the dynamic performance analysis
noticed that, SMES is an effective to follow the disturbance in the system and improves the dynamic performance
of the system. Wind power farm connected to the grid and energy storage technologies (fuel cell and super
capacitor) via DC /AC inverter in [43], for mitigating the output fluctuation. The system has validated with
simulation in MATLAB 7 and the results obtained was satisfying. The use of ESS was effective to overcome the
problem of long-time and short-time power fluctuation.
6.5. Chemical Energy Storage Systems
The literature of hybrid fuel cell and micro-turbine generation system has been presented in [44], to detail all the
various issues related to their interconnection, operation and control connected to the power distribution
network. In [45], a Hydrogen Energy Storage system (HES) model and thermally compensated electrolyser
proposed to support RES integrated into power grid. The developed model simulated in MATLAB/SIMULINK,
evaluated and compared with operated real power system, found that, this model is not suitable at high pressures.
An overall survey addressed hydrogen energy storage system, from hydrogen production, electrical generation
from renewable power sources, to hydrogen storage in various conditions and states, as well as the state-of-the-
art and the future development of individual technology is also discussed in [46]. A hydrogen storage system
[47] is used to overcome the problem of power fluctuation in a micro-grid renewable power system consists of
wind turbine and photovoltaic generators, the obtained result indicates the effectiveness of such storage system
in hybrid power generation to mitigate the power fluctuation. In [48] a case study of a hydrogen energy storage
system integrated with a wind power generation system connected to operation power grid the UK. The purpose
of this study is to evaluate and assess the contribution and effect of hydrogen storage system on the performance
of intermittent renewable energy resources RES, positive impact been noted on the performance of the power
system. Also, in [49] a hydrogen storage management system integrated with offshore wind farm for
compensating when there is a power shortage due to the instability of the wind speed. System has been simulated
and verified its efficiency and feasibility; the obtained results indicate that the suggested control strategy system
more flexible to satisfy the grid operation. [50] In this study a hydrogen storage system integrated with stand-
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alone hybrid renewable power generation system composed of PV, wind turbine and fuel cell, the aim of this
study to control design and power management. MATLAB/SIMULINK being used to simulate the proposed
system, obtained results show that efficient power management has been achieved.[51] The paper focuses on the
hybridization of a wind turbine as main power unite fuel cell (FC) and ultra-Capacitor (UC) systems as make up
power generation stand-alone system. As the wind turbine output power changes with the wind speed: an FC
storage system with a UC bank can be integrated with the wind turbine to avoid power fluctuation in the system.
A dynamic model, In MATLAB/Simulation and Sim-power software design and simulation of a wind/FC/UC
hybrid power generation system with power flow controllers proposed. This hybrid topology shows excellent
performance under variable wind speed and load power requirements.
7. GENERAL COMPARISONS BETWEEN DIFFERENT ENERGY STORAGE SYSTEMS
The comparison between energy storage systems depends on the purpose of energy storage, and frequency of use
as well as the location. Actually, it is difficult to find storage unit that meet all the power system requirements.
Therefore, it is not easy to compare between different devices. But always there are general factors that can be
considered to be used as a balance to find out most fit in a particular situation. The common factors or points are:
Costs, density of energy, specific power, recyclability, Accessibility, durability energy efficiency [52-53].
The high efficiency and long lifetime are the main advantages for thermal and electrical energy storage systems.
While mechanical is normally has high power and capacity but the initial cost is high as well.
The summery of advantages and disadvantages of energy storage technologies classification has details in Table
1. In more details, the pumped hydro energy storage system has the highest rated the longest lifetime but with
less power density. Lead acid batteries are commonly used in wind and solar energy applications. It is suitable
choice for application required high output charging efficiency and acceptable cost. The intensive technical
comparison for several EES systems has shown in Table2.
Table1 Main advantages and disadvantages of classified EES
Table2 Comparison of technical characteristics of EES systems
8. CONCLUSION
ESS Main Advantages Main Disadvantages
Electrochemical High efficiency short storage period
Mechanical High capacity and power high costs ( initial investment)
and Special site requirement
Chemical Long storage period Low efficiency
Thermal High efficiency and long
lifetime
High production cost, Requires
special charging circuit
Electrical High efficiency and long lifetime
Low Energy Density
ESS Wh/kg Power rating Discharge time Suitable storage
duration
Life time
(years)
Capital cost
$/kW
PHES 0.5–1.5 100–5000 MW 1–24 h+ Hours–months 40–60 600–2000
CAES 30–60 5–300 MW 1–24 h+ Hours–months 20–40 400–800
Lead-acid 30–50 0–20 MW Seconds –hours Minutes–days 5–15 300–600
Fuel cell 800–10,000 0–50 MW Seconds –24 h+ Hours–months 5–15 10,000+
Solar fuel 800–100,000 0–10 MW 1–24 h+ Hours–months – –
SMES 0.5–5 100 kW–10 MW 1 ms– 8 s Minutes–hours 20+ 200–300
Flywheel 10–30 0–250 kW 1 ms–15 min Seconds–minutes ∼15 250–350
Super
capacitor 2.5–15 0–300 kW 1 ms – 60 min Seconds–hours 20+ 100–300
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Power generated by renewable resource, cannot be continuous and provide immediate response to the demand
side. Thus, the store of energy becomes much needed, in order to store excessive produced power for later use
when it is needed in the demand side, and to avoid power fluctuation in power systems. Many studies and
technical reviews has been done in this field discussing ESS advantages, disadvantages, development trend and
the different applications of storage technology in the power system are presented in this article.
This study of energy storage systems is done to improve the efficiency of power systems emulators for renewable
energies and thus to extend the research works in this area.
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energy storage as a balancing mechanism in addressing the electric system integration issues inherent with
variable renewable energy resources," in Reliability of Transmission and Distribution Networks (RTDN 2011),
IET Conference on, London, 2011.
[48] Dante Fernando Recalde Melo, Le-Ren Chang-Chien, "Synergistic Control Between Hydrogen Storage
System and Offshore Wind Farm for Grid Operation," IEEE Transactions on Sustainable Energy, no. 1, pp. 18-
27, 2014.
[49] Milana Trifkovic, Mehdi Sheikhzadeh, Khaled Nigim, Prodromos Daoutidis, "Hierarchical control of a
renewable hybrid energy system," 51st Annual Conference onDecision and Control (CDC),E, 2012.
[50] O.C. Onar, M. Uzunoglu, M.S. Alam, "Dynamic modeling, design and simulation of a wind/fuel
cell/ultra-capacitor-based hybrid power generation system," Journal of Power Sources, no. ELSEVIER, pp. 707-
722, 2006.
[51] Kuphaldt, Tony R., "Lessons In Electric Circuits Volumme I-DC," 2006. [Online].
[52] Casey, Tina, "Flow Battery Vs. Tesla Battery Smackdown Looming," 2015. [Online].
6
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6
PROPOSALS TO IMPROVE THE OPERATING REGIMES OF
A GAS TURBINE PLANT
VERNICA SORIN-GABRIEL12, HAZI ANETA1, HAZI GHEORGHE1, GRIGORE
ROXANA1
1 “Vasile Alecsandri” University of Bacau, Calea Mărăşeşti 157, Bacau, 600115, Romania
Abstract: In this paper are shown the experimental data and results of the operating
regimes related to a gas turbine plant with heat recovery. Is present the thermal scheme and
the real parameters of the plant, underlying the experimental determinations. Both the
energetic analysis and the exergetic analysis are carried out of the whole ensemble studied.
Keywords: gas turbine, exergetic analyze, recovery boiler, cogeneration index
1. INTRODUCTION
Experimental analyze was performed on 130 gas turbine plant (GTP) Titan group of 14.3 MW and on a recovery
boiler of 22 MW [1,2-11]. To meet the proposed objectives, it was carried out the processing procedure of the
experimental data by implementing the calculate relations in the electronic program Engineering Equation Solver
(EES).
2. INTERPRETATION OF EXPERIMENTAL RESULTS AND THEIR COMPARISON WITH
THEORETICAL RESULTS
The thermal scheme of the GTP with heat recovery is presented in Figure 1. Operating regimes are characterized
by two categories of measured parameters:
- the parameters imposed by the operation of the gas turbine plant in the National Energy System and the city’s
heating network – electric power (PITG) and thermal power taken up by the water that is heated in the recovery
boiler ( aQ );
- the meteorological parameters – air temperature at the air filter inlet ( 0t ) and the atmospheric pressure specific
to Bacau ( 0p ).
In table 1 are presented the values of the calculated measures.
For the operating regime of the cogeneration plant, at electric powers between 12802 ÷ 14040 [kW] and constant
water flow (0.72 Dn [kg/s]), there is a trend towards increasing the thermal power ( aQ ) by decreasing the
atmospheric temperature ( 0t ), which does not decisively influence the stability of gas turbine operation.
Is highlighted the fact, that the cogeneration index (y) has an increasing trend at decreasing the air temperature at
the AF inlet.
2 Corresponding author, email [email protected]
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For all intervals of exergy values corresponding to the electric power in which it operates the gas turbine plant
with heat recovery, at constant water flow, is highlight an increase of heat exergy supplied in the district heating
network ( ,P CREx ) with the decrease of the air temperature at the AF inlet ( 0t ).
Unlike the experimental results obtained for thermal power 0aQ f t , those considering the second principle
of thermodynamics concludes the scientific importance of exergy analysis.
Fig.1. Thermal scheme of GTP: AF – air filter; AK – air compressor; CC – combustion chamber; GT – gas
turbine; EG – electric generator; GB – gear box; RB – recovery boiler, GK – gas compressor, 1,2, ...,7 –
characteristic points.
Table 1.The determined parameters of GTP with heat recovery
No. crt. aQ
[kW]
PITG
[kW] y
,P CREx
[kW]
ITGPEx
[kW]
1. 20699.6278 13740 0.6638 3948 14165
2. 19548.0017 13934 0.7128 3884 14365
3. 19913.6265 14040 0.705 4062 14474
4. 19703.3941 13971 0.7091 4044 14403
5. 19675.9803 13966 0.7098 4038 14398
6. 20398.0362 13949 0.6838 4183 14380
7. 21677.4216 14017 0.6466 4457 14451
8. 21631.7305 13942 0.6445 4322 14373
9. 21348.4765 13838 0.6482 4152 14266
10. 20845.8562 13195 0.633 3697 13603
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11. 20260.9403 12903 0.6368 3465 13302
12. 18935.5344 12802 0.6761 3138 13198
No. crt. t EL ex KA Dair
[kg/s]
1. 0.3406 0.3173 0.4268 16.639 50.06
2. 0.3669 0.3418 0.4568 15.645 47.35
3. 0.3598 0.3351 0.4515 15.958 48.2
4. 0.3622 0.3374 0.4546 15.775 47.7
5. 0.3628 0.3379 0.4552 15.75 47.63
6. 0.347 0.3232 0.4389 16.37 49.32
7. 0.3245 0.3023 0.4161 17.456 52.26
8. 0.3254 0.3031 0.4148 17.417 52.16
9. 0.3296 0.3071 0.417 17.183 51.53
10. 0.3283 0.3058 0.4092 16.776 50.44
11. 0.3336 0.3108 0.4121 16.303 49.16
12. 0.3603 0.3357 0.437 15.175 46.09
Experimental results for thermal efficiency ( t ) and electric efficiency ( EL ) of GTP, in function of air
temperature at the air filter inlet, are in accordance with theoretical results.
Experimental results for exergetic efficiency, in function of atmospheric air temperature, are in accordance with
theoretical results, which validate the calculation mathematical model (table 2).
Table 2.Comparison between theoretical and experimental results for exergetic efficiency of the GTP with heat
recovery
t0 [°C] ex [%], theoretical ex [%], experimental
10 47.071 46.63
15 46.008 45.21
20 44.944 44.05
25 43.879 43.37
Dependences ( )t KAf , ( )EL KAf with temperature (t3 [°C]) and air mass flow at the AK inlet (Dair
[kg/s]) as parameters, revels the fact that those two considered efficiencies have maximum values at optimal
compression ratios. Dependence ( )ex KAf with temperature (t3 [°C]) and air mass flow at the AK inlet (Dair
[kg/s]) as parameters, revels the fact that the exergetic efficiency of gas turbine plant with heat recovery have
maximum values at optimal compression ratios.
3. CONCLUSIONS
In the first part of this paper were presented the experimental results of the operating regimes of the GTP with
heat recovery.
For the experimental study of the cogeneration plant were represented the dependences for the thermodynamic
parameters of the working fluids in the operating characteristic regimes, for the exergetic flows and for thermal
efficiency, electric efficiency and exergetic efficiency of the GTP with heat recovery.
The experimental results for thermal efficiency, electric efficiency and exergetic efficiency are in accordance
with the theoretical results of optimization in the frame of the mathematical model propose.
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REFERENCES
[1] Najjar, Y.S.H., Efficient Use of Energy by Utilizing Gas Turbine Combined Systems, Applied Thermal
Engineering 21, Elsevier Ltd., 2001, p.407-438.
[2] Franco, A., Casarosa, C., On Some Perspectives for Increasing the Efficiency of Combined Cycle Power
Plants, Applied Thermal Engineering 22, Elsevier Ltd., 2002, p.1501-1518.
[3] Pilavachi, P.A., Power Generation with Gas Turbine Systems and Combined Heat and Power, Applied
Thermal Engineering 20, 2000, p.1421-1429.
[4] Barbu, E., Petcu, R., Silivestru, V., Vilag, V., Centrale termoelectrice cogenerative cu turbine cu gaze,
Tehnologiile energiei, nr.4, ISSN 1842-7189, 2007.
[5] Afgan, N.H., Carvalho, M.G., Pilavachi, P.A., Tourlidakis, A., Olkhanski, G.G., Martins, N., An Expert
System Concept for Diagnosis and Monitoring of Gas Turbine Combustion Chambers, Applied Thermal
Enginnering 26, Elsevier Ltd., 2006, p.766-771.
[6] Cai, R., Jiang, L., Analysis of the Recuperative Gas Turbine Cycle with a Recuperator Located Between
Turbines, Applied Thermal Engineering 26, Elsevier Ltd., 2006, p.89-96.
[7] Kong, X.Q., Wang, R.Z., Huang, X.H., Energy Optimization Model for a CCHP System with Available Gas
Turbines, Applied Thermal Engineering 25, 2005, p.377-391.
[8] Ebadi, M.J., Gorji-Bandpy, M., Exergetic Analysis of the Turbine Plants, International Journal Exergy,
Volume 2, No.1, 2005, p.31-39.
[9] Panait, T., Exergoeconomia sistemelor termoenergetice, Editura Fundaţiei Universitare „Dunărea de Jos”,
Galaţi, 2003.
[10] Najjar, Y.S.H., Alghamdi, A.S., Al-Beirutty, M.H., Comparative Performance of Combined Gas Turbine
Systems Under Three Different Blade Cooling Schemes, Applied Thermal Engineering 24, Elsevier Ltd., 2004,
p.1919-1934.
[11] Ganesan, V., Gas Turbines, Second Edition, Tata McGraw-Hill Publishing, New Delhi, p.636, 2003.
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7
ASPECTS RELATED TO THE UTILIZATION OF GEOTHERMAL ENERGY FOR
THE PRODUCTION OF ELECTRICITY IN ROMANIA
ROXANA GRIGORE12, SORIN-GABRIEL VERNICA1, MIHAI PUIU-BERIZINȚU1,
SILVIU IFTIME1
1 “Vasile Alecsandri” University of Bacau, Calea Mărăşeşti 157, Bacau, 600115, Romania
Abstract: In the energy field, European Commission proposes the next targets to 2030:
40% reductions in greenhouse emissions, at least 27% improvements in energy efficiency
and at least 27% share of renewable energy. Geothermal energy is a renewable energy,
defined as “energy stored in the form of heat under the surface of the earth” (EGEC). In
2017, 3% of the total electricity generated in the world was produced based on the use of
geothermal energy. In Europe, geothermal energy is widely used in Iceland, and geothermal
plants can also be found in Turkey, Italy, France, Germany. In Romania, where geothermal
sources reach a temperature of up to 125oC, geothermal energy usage is divided into the
following categories: 37% for heating, 30% for agriculture (greenhouses), 23% for
industrial processes, 7% for other purposes. This paper presents the opportunity of using
geothermal energy for electricity production in Romania.
Keywords: geothermal energy, electricity, binary cycle geothermal power plant
1. INTRODUCTION
Starting from specialist general assumptions, geothermal energy is the Earth’s heat which can be utilized by
humankind as an energy source. The Earth is made up of crust, mantle and core, as in Fig. 1. The temperature of
the earth increases with depth, in the inner core reaching more than 4500oC [1]. Although the Earth’s thermal
energy is immense, only the thermal energy of the crust being estimated of the order of 5.4 x 1021 MJ, the
geological conditions and current technologies do not allow it to be fully exploited [2], [3].
Geothermal energy is considered a renewable resource of energy due to the ability of the earth to produce
magma, and due to the continuous heat transfer between subsurface rock and water [4]. At the same time,
geothermal energy is considered clean, because during the exploitation, it does not generate waste. The
geothermal fluid which is brought to the surface is injected back into the ground [1]. Geothermal energy
contributes to reduced global warming effects and its deployment helps reduce a country’s dependence on fossil
fuels.
The geothermal energy has been used from ancient times, for bathing or for space heating. In our time,
geothermal energy is utilized for different applications like: heat and electricity production, industrial processes,
water heating in fish farming, desalination and agricultural applications (greenhouses, drying of plants, etc.).
The Earth’s thermal energy is brought to the surface and, with the help of various technologies, is converted into
an energy-efficient resource for the people. Technologies and uses depend on the thermodynamic proprieties of
the geothermal fluids. A generally accepted classification divides geothermal resources into: low, intermediate
and high enthalpy resources. According to Muffler and Cataldi, the temperature for low enthalpy geothermal
fluid is under 90oC, the temperature for intermediate enthalpy geothermal fluid is between 90oC and 150oC, and
the temperature for high enthalpy geothermal fluid is over 150 oC [5]. High-temperature geothermal resources
are used for electricity generation. To produce thermal energy using geothermal energy, heat pumps are used.
The product, thermal energy, is used for heating, domestic hot water or for cooling the air. These systems are
more common than geothermal power plants, due to the fact that they do not requires large funds for their
building and installing. They also have a very low temperature geothermal source [6]. 2 Corresponding author, email [email protected]
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Fig. 1. The interior of the Earth, [1]
2. THE GLOBAL SITUATION FOR ELECTRICITY GENERATION USING GEOTHERMAL
ENERGY
According to [7], in 2016, the global geothermal installed capacity was 12.7 GW. Table 1 presents the net
installed geothermal power capacity by country in 2016 [6].
Table 1. Net installed geothermal power capacity by country in 2016
Country Capacity [MW]
USA 2511
Phillippines 1916
Indonesia 1534
Kenya 1116
New Zeeland 986
Mexico 951
Italy 824
Turkey 821
Iceland 665
Japan 533
Costa Rica 207
El Salvador 204
Nicaragua 155
Russian Federation 78
Papua New Guinea 53
In 2017, the global geothermal power generation was 84.8 TWh, while the cumulative capacity reached was 14
GW. Global geothermal power capacity is expected to rise to just over 17 GW by 2023, with the biggest capacity
additions expected in Indonesia, Kenya, Philippines and Turkey, [8]. The largest group of geothermal power
plants in the word is The Geysers Complex, located in USA. The complex, and its 22 geothermal power plants
therein, have a combined installed capacity of 1520 MW.
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3. GEOTHERMAL ENERGY IN ROMANIA
Romania has a low enthalpy geothermal potential, located along the western border with Hungary (Pannonian
Plain), in the central part and in the south-central part of the country, as presented in Fig. 2.
The main geothermal areas in Romania are:
The Panonian geothermal aquifer;
The Oradea geothermal reservoir;
The Bors geothermal reservoir;
The Beius geothermal reservoir;
The Ciumeghiu geothermal reservoir;
The Cozia-Calimanesti geothermal reservoir;
The Otopeni geothemal reservoir.
Fig. 2. Geothermal areas in Romania [9]
Since the Roman Empire, Romania has been known to use thermal springs for bathing and geothermal fluids for
heating. The search for geothermal resources utilized for production of electricity and heat began in 1960, based
on the hydrocarbon research programme [11]. With more than 250 exploration wells already having been drilled,
the geological research program still continues, with a few new wells being drilled each year. Although the total
capacity of the existing wells is about 480 MWt, only about 246 MWt is currently being used. 96 wells are used
for producing hot water at temperatures from 40oC to 115oC. 35 wells are used for balneology, with a flow rate
of more than 360 l/s and temperatures from 38oC to 65oC [11], [12].
In Romania, there are two main companies currently exploiting geothermal resources: Transgex S.A. and
Foradex S.A. The University of Oradea has established a Geothermal Research Center which provides
geothermal training and research [10]. The main uses of geothermal energy in Romania are shown in the graph
of Fig. 3.
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Fig. 3. Geothermal energy in Romania
In 2013, in Oradea, Transgex S.A. installed the first geothermal power plant in Romania. The installed capacity
of the pilot plant is 50 kWe and the electricity production of the first year was 400 MWh [13]. The geothermal
power plant is an ORC module, manufactured by ELECTRATHERM, S 4000 model.
4. BINARY CYCLE GEOTHERMAL POWER PLANT
There are three large types of geothermal power plants: dry steam power plant, flash steam power plant and
binary cycle power plant.
For Romania, which has reservoirs with lower temperatures, binary cycle power plant is the most suitable
alternative. This plant uses two kinds of fluids: a geothermal fluid which evaporates at a low boiling point and a
fluid which drives the turbine. The binary fluid is operated through a conventional Rankine cycle. This fluid can
be an organic fluid such as Isopentane, Isobutane. Fig. 4 shows the principal elements of this type of plant.
Fig. 4. Binary cycle power plant: 1 – well production, 2 – pressure regulation valve, 3 – heat exchanger, 4 –
turbine, 5 –electric generаtor,6 – condenser, 7 – cooling water pump, 8 – cooling tower, 9 – condenser pump,
10 – Injection well.
The geothermal fluid (primary working fluid) is passed throuh at heat exchanger (4) to heat the organic fluid
(secondary working fluid) that vaporizes at a lower temperature than water. This fluid is used to drive the turbine
(4) and is then condensed into the condenser (6). The fluid from the binary plant is recycled back to the heat
exchanger and forms a closed loop [4]. The geothermal fluid is injected back to the reservoir. In this way, a
relatively low geothermal thermal potential can be used. A variant of the binary cycle is the Kalina cycle, where
a solution of ammonia is used as the working agent.
Investment costs for a such a geothermal plant depend on the cost of geothermal drilling and the cost of the
surface equipment. Higher uncertainties can be expected in respect to the drilling process and the number of
geothermal wells required for the plant. According with [14], the drilling cost of low temperature geothermal
development is about 10%-20% of the total development cost.
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The analysis realised in the Report [15] suggests that in the area of Oradea municipality, investments in a Kalina
cycle geothermal power plant could be recovered in 7.69 years.
5. CONCLUSIONS
Geothermal energy is a renewable and clean source of energy that is not influenced by seasonal temparature
changes and that contributes to reduced global warming effects. The capacity factor is 98% which is very high
in comparison with other renewable energy sources.
In Romania, as shown in Fig. 5, there are areas with temperatures above 140°C at 3000 m depth that could be
exploited for electricity production. Although the implementation of projects for electricity generation is
possible, the relatively high investiment costs represent the main challenge.
Fig. 5. Romania- geothermal map – areas where could be possible electricity production
[IGR 2006 source]
Geothermal power plants represent one of the most advantageous solutions to supply energy for isolated
consumers. They eliminate the need of long lines to transport electricity, which generate significant costs for
investment and maintenance. The technologies used are not sophisticated, based on commercially mature
equipment, and the geothermal energy source is virtually inexhaustible. Moreover, there is a particularly high
availability compared to other categories of power plants. Geothermal installations are mainly used to cover the
basis load curve of an energy system.
REFERENCES
[1] Bologa O., Crenganis M., Geothermal Energy, Lucrarile celei de-a VIII-a Conferinte anuale a ASTR,
available at: http://www.agir.ro/buletine/2021.pdf
[2] Dickson M., Fanelli M., Geothermal energy:utilization and technology, 2003, available at:
httр://unesdoс.unesсo.org/imаges/0013/001332/133254e.рdf#nаmeddest=149593
[3] Topliceanu L., Puiu P.G., Contribution of Geothermal Resources to Energy Autonomy: Evaluation and
Management Methodology, Energies 2016, 9(8), 612; https://doi.org/10.3390/en9080612
[4] Mburu M., Geothermal Energy Utilisation, available at: https://orkustofnun.is/gogn/unu-gtp-sc/UNU-GTP-
SC-17-0204.pdf
[5] Muffler, L.P.J., and Cataldi, R., 1978, Methods for regional assessment of geothermal resources:
Geothermics, v. 7, p. 53-89.
[6] Grigore R., Hazi A., Considerations about efficient use of geothermal heat pumps, Buletinul AGIR Nr.3 An
2012, pg. 167-172, available at: http://www.agir.ro/buletine/1379.pdf
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[7]*** https://www.irena.org/-
/media/Files/IRENA/Agency/Publication/2017/Aug/IRENA_Geothermal_Power_2017.pdf
[8] *** https://www.iea.org/topics/renewables/geothermal/
[9] *** http://add-energy.ro/tehnologii-de-obtinere-a-energiei-din-surse-geotermale/
[10] *** https://www.worldenergy.org/data/resources/country/romania/geothermal/
[11] Bendea C., Antal C., Rosca M., Geothermal Energy in Romania: Country Update 2010-2014, Proceedings
World Geothermal Congress 2015, Melbourne, Australia, 19-25 April 2015, available at: https://pangea.stanford.edu/ERE/db/WGC/papers/WGC/2015/01013.pdf
[12] *** http://geothermal.ro/pdf/Energii_regenerabile_Geotermal.pdf
[13] *** http://www.transgex.ro/index.php/en/about-us/portfolio/70-iosia-en
[14] httрs://www.geothermаl-energy.org/рdf/IGАstаndаrd/INАGА/2001/2001-27.рdf
[15] http://remsis.utcluj.ro/wp-content/uploads/2016/11/Raport-2016-Oradea.pdf
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INDEX OF AUTHORS
A
ASHGLAF MOHMED – 29
B
BOSTAN ION – 20
BOSTAN VIOREL – 20
D
DULGHERU VALERIU – 20
G
GRIGORE ROXANA – 43,47
GUȚU MARIN – 20
H
HAZI ANETA- 5,14,43
HAZI GHEORGHE – 5,14,43
I
IFITME SILVIU – 47
N
NICHITA CRISTIAN – 29
P
PALADI FLORENTIN- 25
PRIMAC VLADIMIR- 25
PUIU-BERIZUNȚU MIHAI – 47
V
VERNICA SORIN-GABRIEL – 5,14,43,47