Transcript Voltage Control of Distribution Network Using an Artificial

Voltage Control of Distribution
Network Using an Artificial
Intelligence Planning Method
Jianing Cao1
Keith Bell1
Amanda Coles2
Andrew Coles2
1.Department
of Electronic and Electrical Engineering
2.Department of Computer and Information Sciences
University of Strathclyde, UK
Jianing Cao – UK – Session 5 – 1112
Frankfurt (Germany), 6-9 June 2011
Background

Distribution Network active control
Figure 1. Example of a substation with
active management facilities
Source: R.A.F. Currie, G.W. Ault, C.E.T. Foote, G.M. Burt, J.R. McDonald
Frankfurt (Germany), 6-9 June 2011
Objectives

Improve settings for controllers in a Distribution Network

E.g. mechanically switched capacitors (MSC) & tap changing transformers

Minimise control actions & wear-and-tear on equipment

Plan control targets to minimise human intervention

Respect the voltage limits

E.g. ± 6% for 33kV/11kV [1] (Case study: ± 5%)
[1]: D.A. Roberts, SP Power Systems LTD, 2004, “Network management systems for active distribution networks
– a feasibility study”
Frankfurt (Germany), 6-9 June 2011
Methodology

Multi-objective Artificial Intelligence planning method [2]
– forecast demand and generation for a given period,
e.g. a day (re-planning might be needed)

Load flow simulation
– Linear sensitivity factors reflecting voltage changes
with respect to control actions
[2]:K. Bell, A.I. Coles, M. Fox, D. Long, A.J. Smith, 2009, "The Role of AI Planning as a Decision Support Tool in
Power Substation Management", AI Communications, IOS Press, vol.22, 37-57.
Frankfurt (Germany), 6-9 June 2011
Details
• Planner objective function [2]
PM    T    M    LV    HV
– PM: plan metric
T: transformer steps
– M: MSC switches
LV/HV: low voltage/high voltage
– α/β: cost of control/switch action from transformer/MSC
– γ/δ: relative “cost” of voltage below 0.95 p.u / above 1.05 p.u
[2]:K. Bell, A.I. Coles, M. Fox, D. Long, A.J. Smith, 2009, "The Role of AI Planning as a Decision Support Tool in
Power Substation Management", AI Communications, IOS Press, vol.22, 37-57.
Frankfurt (Germany), 6-9 June 2011
Existing Planner
Figure 2. Overview of the VOLTS system
Source: Keith Bell, Andrew Coles, Maria Fox, Derek Long and Amanda Smith
Using PDDL (planning domain definition language)
1. A domain file for predicates and actions
2. A problem file for objects, initial states & goal
Frankfurt (Germany), 6-9 June 2011
Software integration
Network Parameters
Derive sensitivity factors
Planner
parameters
Metric-FF
(planner)
Run sequence of
Load Flow
Voltages
outside
limits?
Yes
Update sensitivity factors
No
Final plan
Control
actions/
Control
targets
Frankfurt (Germany), 6-9 June 2011
Distribution Network Model
Source: AuRA-NMS project
Frankfurt (Germany), 6-9 June 2011
Demand data in case study
Assumption of load

Constant power factor for each load throughout the day
Profiles follow National Grid’s half-hourly metered data

E.g. 30-Oct-20104.5
4
A1.Load1
3.5
A2.Load1
A5.Load1
3
A9.Load1
2.5
B1.Load1
2
C1.Load1
1.5
C6.Load1
1
C9.Load1
0.5
C10.Load1
0
C11.Load1
00:00
01:00
02:00
03:00
04:00
05:00
06:00
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
00:00

Demand (MW)

Time (hour)
C12.Load1
C13.Load1
Frankfurt (Germany), 6-9 June 2011
Generation data in case study

Combined Heat and Power (CHP)

Capacity of 4MW


Output maximum power when space & water heating needed
Power factor of unity or 0.8
Frankfurt (Germany), 6-9 June 2011
Simulation Process 1: setting base cases
Base case 1:
 voltage target of transformer set to 1.0 per unit
 Run load flows to get tap settings & voltages

Base case 2:
 tap position of transformers set to nominal (0)
 Run load flows to get voltages to compare

Frankfurt (Germany), 6-9 June 2011
Simulation Process 2: optimisation

Feed the planner with sensitivity factors &
initial conditions from load flow results

Generate new transformer tap settings

In set of load flows, set tap positions
according to the planner’s control output

Compare against the base case.
Frankfurt (Germany), 6-9 June 2011
Simulation results

Tap settings

Minimum voltage on the network
0
-1.5
-2
Trans.PF1
-2.5
Trans.PFC
Trans.VC
-3
Trans.PF1.PLAN
-3.5
00:00
01:30
03:00
04:30
06:00
07:30
09:00
10:30
12:00
13:30
15:00
16:30
18:00
19:30
21:00
22:30
00:00
-4
Voltage (p.u.)
Tap setting (%)
-1
1.01
1
0.99
0.98
0.97
0.96
0.95
0.94
0.93
0.92
Vmin.VC
Vmin.PFC
Vmin.PF1.PLAN
Vmin.PF1
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
00:00
-0.5
Time (hour)
Time (hour)
PFC/VC mode:
Planner suggested no
change from base case 2
since voltage is not
beyond limits
PF1 mode:
Base Case 1: transformer tap varied from -3% to -2%
Base Case 2: minimum voltage 0.947 per unit at 18:00
Planner’s result: 0.96 per unit
Frankfurt (Germany), 6-9 June 2011
Summary

Conclusion
 Successful integration between the planner and
a load-flow simulator
 A sequence of control settings were found
 Achieved required voltage profile with fewer
tap changes in planned mode
 Hence, less wear-and-tear on the equipment
Frankfurt (Germany), 6-9 June 2011
Summary

Future development
 Larger distribution network with more
controllers/loads/distributed generators
 Test another ‘worst case’ scenario –
low demand & high generation
 The planner’s robustness to forecast errors
to be tested
Thank you for your attention.
Acknowledgement: the work described has been funded by
• EPSRC under research grant EP/D062721
• Supergen ‘HiDEF’ programme