Possibility of network voltage control using demand side ... savetovanje... · Outline of...
Transcript of Possibility of network voltage control using demand side ... savetovanje... · Outline of...
Possibility of network voltage control
using demand side management
Prof Jovica V. Milanović Dipl.Ing., MSc, PhD, DSc, CEng, FIET, FIEEE, Foreign member of SAES
Deputy Head of School & Director of External Affairs
Manchester, M13 9PL, United Kingdom
33rd Symposium CIGRÉ Serbia
6th June 2017, Zlatibor, Serbia,
School of Electrical & Electronic Engineering
Outline of presentation
• Network & Voltage controllability challenges
• Advanced demand profiling & management
• How it could be used for voltage control
• Summary
Network & Voltage controllability
challenges
The drivers
• Evolving/new market structures and operation
• Increasingly liberalised market
• Increased cross-boarder bulk power transfers to facilitate effectiveness of market mechanisms
• The participation of controllable (that can be both, controlled and used
for control) plant in generation mix is reducing
• The nature and behaviour of load has changed and is changing
(e.g., spatial in addition to temporal variation)
• New transmission components (still insufficiently understood,
compared to “old” technologies) are being added to the system
Controllability challenge
• The complexity and vulnerability of the system is increasing
• The system control, stability and security are becoming
increasingly important and much more time dependent than before
CONTROL CENTRE
CENTRAL (BULK)
POWER GENERATION ANCILLARY SERVICES
• “Every little helps” is becoming very important
– Additional/Advanced controllability of RES
– Deployment and control of energy storage
– Efficient Demand Side Management (DSM)
Advanced demand profiling &
management
(Advanced) Demand profiling
Identification/estimation and forecasting of static
composition (in terms of load categories and load
controllability) and dynamic response of demand
Why (or) Do we need to consider it ?
Efficient DSM
• To apply efficiently Demand Side Management (shift load from the
hours of high consumption to hours of low consumption) to facilitate
required “change in paradigm” and enable load-follows-generation
approach for efficient integration of RES we need to be able to
forecast (reasonably accurately) the demand from 30 min (or less)
to a day (or more) in advance and to control it.
– At present only demand for real power at bulk supply points is forecasted
• Forecasting only total demand for real power (P) is not sufficient
– the information about the available reactive power (Q) is required as well,
e.g., for voltage regulation
Efficient DSM
• Knowledge of total demand (be it accurate and for both P and Q) is
not sufficient
- the information about demand composition in terms of controllable
(that can be shifted from one hour to the next) and uncontrollable load
is needed
• Knowledge of demand composition is not sufficient
- the information about load category (based on dynamic response to
system disturbances) mix in both controllable and uncontrollable part
of demand is needed
• Knowledge of demand composition in terms of load category is
not sufficient
- the information about dynamic response of aggregate demand at
bulk supply point before and after the shift of controllable load is
needed
Estimation of demand composition
1. Assuming that we have online measurements from
advanced smart meters installed at individual
customer sites
Demand profile
Decomposed Load Curves
based on Load Categories
Decomposed Load Curves
based on Load Type
Decomposed Load Curves
based on Load Catrollability Decomposed Load Curves
based on Load Categories
00:00 06:00 12:00 18:00 23:590
100
200
300
400
Time [h]
Reac
tive p
ower
[kva
r]
Average
Mean
Most probable
Typical
Controllable/uncontrollable load Load categories
Probabilistic estimation of Q
2. Assuming that we don’t have any online
measurements from individual customers
Probabilistic estimation of demand
profile & controllability
On-line measurement
using Smart Meters
Data processing &
Aggregation based
on Load categories
& controlability
Deterministic DDLC
Based on Categories
& Controlability
Participation of Load
Categories in Different
Load Classes
Metering Data Customer Surveys General Knowledge of Demand
Composition
DDLC Based on Load
Classes DDLC Based on
Load Categories
& Controlability
Data Processing
with Parameter
Uncertainty
Probabilistic DDLC
Based on Categories
& Controlability
On-line measurement
using Smart Meters
Data processing &
Aggregation based
on Load categories
& controlability
Deterministic DDLC
Based on Categories
& Controlability
Is this all we need?
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24
40
60
80
100
Hourtime (sec)
Po
wer, P
(%
)
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24-20
-10
0
10
20
Hourtime (sec)
Reactiv
e P
ow
er,Q
(%
)
• Demand composition affects demand (load) dynamic response
following small or large voltage (predominantly) or frequency
disturbance in the network
•Changing of transformer tap position
• Fault in the system
• Different dynamic response of the load will affect overall dynamic
response of the system (voltage, frequency or angular stability of
the system)
•The contribution of the load (with given composition) to
system dynamic response depends on
• The size of the load
• The location of the load
Responses of different load categories
0 1 2 3 4 50.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1.05
t (sec)
P (p
.u)
a)
Upper Limit
Lower Limit
Most Probable Response
Average Response
Dish Washer
Refrigerator
Room Air Conditioner
Washing Machine
Dryer
0 1 2 3 4 50.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t (sec)
Q (p
.u)
b)
a) real power responses, and b) reactive power
response for residential and commercial motors with
most probable and average response specified
0 0.2 0.4 0.6 0.8 1 1.20.92
0.94
0.96
0.98
1
1.02
t (sec)
P (p
.u)
a)
0 0.2 0.4 0.6 0.8 1 1.20.92
0.94
0.96
0.98
1
1.02
t (sec)
P (p
.u)
b)
0 0.2 0.4 0.6 0.8 1 1.20.15
0.3
0.45
0.6
t (sec)
Q (p
.u)
c)
0 0.2 0.4 0.6 0.8 1 1.20.15
0.3
0.45
0.6
t (sec)
Q (p
.u)
d)
Upper Limit
Lower Limit
Average Response
Most Probable Response
a) small industrial IM P response,
and b) large industrial IM P
response to step down voltage; c)
small industrial IM Q response,
and d) large industrial IM Q
response to step down voltage.
(The upper and lower limit and the
average responses are also shown.)
0 0.5 1 1.5 2-0.5
-0.4
-0.3
-0.2
-0.1
0
time(sec)
Q (p
.u.)
a)
500 samples
-0.5 -0.4 -0.3 -0.2 -0.1 00
0.02
0.04
0.06
Steady State Q(p.u)
Prob
abilit
y
b)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-0.5-0.4-0.3-0.2-0.1
00.10.20.30.40.5
time(sec)
Q (p
.u)
c)
Lower Limit
Most Probable Response
Upper Limit
Real power responses to a step reduction in voltage for
SMPS: a) responses obtained from 500 MC simulations; b)
probability histogram of steady-state power after voltage
drop; c) upper and lower limit of the responses and the
most probable response
The effect of demand profile on dynamic
response of the load
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24
40
60
80
100
Hourtime (sec)
Po
wer, P
(%
)
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24-20
-10
0
10
20
Hourtime (sec)
Reactiv
e P
ow
er,Q
(%
)
Comparison of different a) P and b) Q responses at different times of day (solid
line: 03:00; dashed line: 04:00; dash-dot line: 12:00; dotted line: 18:00)
How it can be used for voltage control
Effect of demand management
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66 63
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7168
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233255257
293 292
294 295 297 296
299 298
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77238
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269
287 285286
56
BA C D E
F
H I J K L
ON
G
132kV
33kV
11kV
3.3kV
275kV
400kV
G
195
196
Commercial
Industrial
Mixed
IM; 15%
I; 5%
Z; 24% P; 56%
Lights;
16% Cooking;
6%
H Water;
10%
Space
Heating;
18%
Appliaces;
50%
Domestic electricity
consumption broken
down by end use. ~
~ ~
G1
G2 G3
BSP of DN C
2
7
8
93
3005
4
1
10
11
BSP of DN B
BSP of DN A
Change in demand & composition
Exponential load model
00
V
VPP
Z
(K=2)
I (K=1)
P (K=0)
V (
p.u
.)
Power (p.u.)
A
BC
0
50
100
150
200
250
300
350
400
450
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Po
wer
, M
W
Hours
Aggeregated load profile
Base
Shift Dom. IM+Z
Shift Dom.+Com. IM
Change in demand composition
Effect on voltage levels – TN & DN
Voltage level of transmission (above) and distribution (below) network for shifting domestic load
(Z+IM) at peak time. Left – before the shift; Right – after shifting Z+IM load at domestic buses
Sensitivity analysis of PV curves - DN
ΔV/ ΔP = (V0-Vc)/(Po-Pc) from operating point to
critical point at peak (above) and low (below) time
Summary
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24
40
60
80
100
Hourtime (sec)
Po
wer, P
(%
)
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24-20
-10
0
10
20
Hourtime (sec)
Reactiv
e P
ow
er,Q
(%
)
Given
On-line measurements form installed smart meters
General information about demand composition at a bus (e.g., 30%
commercial, 10% industrial and 60% domestic)
Standard P, V and Q (or a combination of those) half-hourly (or at other
time intervals) measurements over some period of time in the past
Weather forecast for the following day
It can be forecasted/estimated
• Total P and Q demand
• Demand composition in terms of categories
• Demand composition in terms of controllable and non-controllable
demand
• Dynamic P and Q response
at that bus (aggregation point) at any given time “now” or in the future)
and how it will change if we “move” part of demand to a different hour
Summary
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24
40
60
80
100
Hourtime (sec)
Po
wer, P
(%
)
0 1 2 3 4 5 6 7 8 0 3 6 9 12 15 18 21 24-20
-10
0
10
20
Hourtime (sec)
Reactiv
e P
ow
er,Q
(%
)
By embedding information of demand composition in different
network analysis programmes (either as a part of objective
function or as a constraint) greater flexibility and cost
effectiveness of network performance can be achieved with
respect to all steady state and transient network performance
criteria/attributes. The influence of smart meter coverage and missing data on the
accuracy of load decomposition?
DR based on price
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230
50
75
228
74
229
20221
51 76
13
268
87
48 47
49222
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42
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39
38
37
269
235 236
231
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79
80
81
85
88
290
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291
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97 98
101 99
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103 104
105
106 107
108
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112
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114 115
116
121
122
123124
125
126
127
128
117
118
119 120
86
11
10
8
9
7
6
5
4
55
1 65
64
66 63
57
226 227
58
149
147
154
155
150 153 156
148 146
145
141
143
142
144
140
139
129
130133
131
135
137 136
138
134
157
161
158
186
184
132
160
165
162
163
164
166
167
168169
170
180
181
182 183
185
187
188
189
190 191
192
193
194
197
198
200 199
201
202
203 204
205
206
207
208209
151
152
224
232
77
159
225
215
216
211
212210
213
214
217
218 171
219
220
172
173
174
175
176
177
178
62
60
59
61
2
3
72
70
179
25
27
29
30
32
31 33
3435
36
7168
67 69
73
89
45
44
249 250
266267
242
252
260
289
237
244
261
251
241 245246 243
262272270253 274271 276 275 263 264 273
240
254258 256259
277
247
278
248
280
234
279
233255257
293 292
294 295 297 296
299 298
300
77238
288
269
287 285286
56
BA C D E
F
H I J K L
ON
G
132kV
33kV
11kV
0.4kV
275kV
400kV
G
A B CD HI
Substation A
JK L
0
1000
2000
3000
4000
5.63 6.11
2487.46
5.9
1692.28
4322.47
1722.30
Buildings
Pro
fit
[£]
JK
CD
HI
EBA
L
the profit resulting from the participation of consumers in
the DSM program based on price