voltage control of distribution network using an artificial intelligence planning method
DESCRIPTION
Voltage Control of Distribution Network Using an Artificial Intelligence Planning Method. Jianing Cao 1 Keith Bell 1 Amanda Coles 2 Andrew Coles 2 Department of Electronic and Electrical Engineering Department of Computer and Information Sciences University of Strathclyde, UK. - PowerPoint PPT PresentationTRANSCRIPT
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
Source: R.A.F. Currie, G.W. Ault, C.E.T. Foote, G.M. Burt, J.R. McDonald
Figure 1. Example of a substation with active management facilities
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: 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
HVLVMTPM
[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 systemSource: Keith Bell, Andrew Coles, Maria Fox, Derek Long and Amanda Smith
Using PDDL (planning domain definition language)1. A domain file for predicates and actions2. A problem file for objects, initial states & goal
Frankfurt (Germany), 6-9 June 2011
Software integrationNetwork ParametersNetwork Parameters
Planner parameters
Planner parameters
Derive sensitivity factorsDerive sensitivity factors
Metric-FF (planner)Metric-FF (planner)
Run sequence of Load Flow
Run sequence of Load Flow
Update sensitivity factorsUpdate sensitivity factors
Voltages outside limits?
Voltages outside limits?
YesYes
NoNoFinal planFinal plan
Control actions/Control targets
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-2010
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
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
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
Tap
setti
ng (%
)
Time (hour)
Trans.PF1
Trans.PFC
Trans.VC
Trans.PF1.PLAN0.920.930.940.950.960.970.980.99
11.01
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
Volta
ge (p
.u.)
Time (hour)
Vmin.VC
Vmin.PFC
Vmin.PF1.PLAN
Vmin.PF1
PF1 mode: Base Case 1: transformer tap varied from -3% to -2%Base Case 2: minimum voltage 0.947 per unit at 18:00Planner’s result: 0.96 per unit
PFC/VC mode:Planner suggested no change from base case 2 since voltage is not beyond limits
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
Acknowledgement: the work described has been funded by• EPSRC under research grant EP/D062721• Supergen ‘HiDEF’ programme
Thank you for your attention.