Download Implementation of Control Center Based Voltage and Var

1
Implementation of Control Center Based
Voltage and Var Optimization in Distribution
Management System
Xiaoming Feng, William Peterson, Fang Yang, Gamini M. Wickramasekara, John Finney
Abstract--This paper presents the implementation of an online
voltage and var optimization (VVO) application that runs at the
control center for distribution systems.
The innovative VVO application combines state of the art
optimization technology with detailed and accurate modeling of
distribution systems. The multi-phase, unbalanced system model
can have radial or meshed circuits, single or multiple sources,
wye and/or delta connected transformers, grounded or
ungrounded, voltage dependent load models, ganged or unganged controls of capacitor banks and voltage regulators.
This application is capable of optimizing large and complex
networks with online application speed. It is designed for smart
grid application to optimize distribution energy delivery
efficiency by minimizing the energy loss and manage system
demand.
Index Terms—Distribution management system, energy
efficiency, multi-phase, multi-source, unbalanced, meshed,
reactive compensation devices, utility control center, var
compensation, voltage and var optimization (VVO), voltage
regulating devices
I
I. INTRODUCTION
n a world strapped for energy and in need for significant
reduction in carbon emission, energy efficiency and demand
management have become key strategies for electric power
grid operators all over the world to make the best use of the
energy, optimize utilization of transmission and distribution
assets, and meet growing demand. Distribution systems,
which historically accounted for about 4~5% of the total
energy losses from power plants to end users, is the last
frontier for energy efficiency and demand management
technology.
Most of the energy loss occurring on the distribution system
is the ohmic loss resulting from the electric current flowing
through conductions made from aluminum or copper. The
amount of loss is proportional to the product of the conductor
resistance and the square of the current magnitude. Energy
loss can be reduced, therefore, either by reducing resistance or
the current magnitude or both. The resistance of a conductor
is determined by the resistivity of the conductor material, by
its cross sectional area, and by its length, none of which can
All authors are affiliated with ABB Inc., Raleigh, North Carolina, 27606.
978-1-4244-6547-7/10/$26.00 © 2010 IEEE
be changed easily in existing distribution networks. However,
the current magnitude can be reduced by eliminating
unnecessary current flows in the distribution network.
For any conductor in a distribution network, the current
flowing through it can be decomposed into two components:
active and reactive. The active component is in phase with the
terminal voltage phasor angle and delivers the active energy
that does the real work for a utility. The reactive component
is orthogonal in phase to the terminal voltage phasor angle and
delivers the reactive power that does not do any real work, but
uses nevertheless the energy delivery capacity of the
conductors.
Reactive power (var) compensation devices are designed to
reduce or eliminate the unproductive component of the current
and thus energy loss. Reactive compensation devices, such as
capacitor banks, are used to reduce the reactive power flows
through out the distribution network. The capacitor banks
may be located in the substation or on the feeders.
The voltage profile on the feeders can also affect the current
distribution, although indirectly and to a smaller extent, affect
energy loss. On the other hand, the voltage profile could have
significant influence on the system demand for the system that
has considerable amount of voltage dependent loads, such as
constant impedance loads.
Voltage regulating devices include substation transformers
that have on load tap changers and special transformers with
tap changers called voltage regulators at various locations on
the feeders. By adjusting the tap settings of these devices, the
voltage profiles on the feeders can be controlled to varying
degrees.
Historically, the voltage and var control devices are
regulated in accordance with locally available measurements
of, for example, voltage or current. On a feeder with multiple
voltage regulation and var compensation devices, each device
is controlled independently, without regard for the resulting
consequences of actions taken by other control devices. This
practice yields inconsistent control actions and sub-optimal
results.
To achieve optimal result, a coordinated approach is
preferred. The coordination could be done centrally using a
substation automation system or a distribution management
system. Two main obstacles prevented the adoption of
coordinated control: 1) affordable two-way communication
infrastructure, and 2) robust voltage and var optimization
algorithm to handle large and complex real distribution
systems.
2
The accelerated adoption of substation automation (SA),
feeder automation (FA) technology, and the wide-spread
deployment of advanced metering infrastructure (AMI) have
over the last few years laid the foundations for a centralized
control approach, by providing the necessary sensor, actuator,
and reliable two-way communications between the field and
the distribution system control center. Until recently, however,
VVO technologies have not been mature enough to optimize
large and complex distribution systems with satisfactory
performance in solution quality, robustness, and speed.
The search for VVO methodologies has been going on for
several decades. References [1]-[7] are representatives of
using analytical techniques to solve the problem, in which the
distribution system is generally modeled as the radial system,
the impact of the detailed and accurate system component
models on the solution is not taken into account, and the
optimization problem is simplified to make it easy to solve. In
the last decade, some other techniques have been attempted to
solve the problem, including the rule based techniques [8][14] and Meta-heuristic techniques [15]-[18] (e.g., genetic
algorithms, simulated annealing, particle swarming, etc).
These approaches can avoid the modeling complexity.
However, they are limited in solving small scale problems and
in off-line applications where online performance is not
required.
This paper begins with a review of the technical challenges
for voltage and var optimization for the distribution systems.
It describes the special requirements for modeling distribution
systems with unbalanced construction and loading conditions,
especially for meshed network with multiple sources. It then
presents an implementation of a centralized voltage and var
optimization application. A general formulation of the
centralized VVO is provided, where the problem is cast as
mixed integer quadratic programming for a var optimization
sub-problem and iterative mixed integer quadratic
programming for a voltage regulation sub-problem.
Representative results of the centralized VVO using real
utility circuits with thousand nodes are provided to illustrate
the effectiveness of this implementation.
II. TECHNICAL CHALLENGES
The objective of VVO is to minimize the energy loss on a
distribution circuit or the total MW demand supplied from a
substation. The controls are the switching status of the
capacitor banks and the tap settings of voltage regulation
devices.
Two main obstacles lie ahead of the coordinated voltage
and var optimization.
1) Accurate modeling of the distribution system’s
behavior under any control setting
2) Efficient and robust search algorithm for optimization
to provide discrete solution for capacitor bank status
and taps of voltage regulating devices.
A good VVO solution approach should be able to handle
large, real distribution systems that have any combinations of
the following characteristics:
• Multi-phase unbalanced construction and loading
• Radial or meshed systems
• Single or multiple sources
• Various transformer connections (Y/Y, Y/∆, ∆/Y,
∆/∆, grounded or ungrounded)
• Various load connections (Y or ∆)
• Various combination of voltage dependent load
models
• Ganged or un-ganged control for capacitor banks
and voltage regulating devices.
VVO in essence is a non-linear combinatorial optimization
problem with the following characteristics:
o Integer decision variables – both the switching status
of capacitor banks and the tap position of regulation
transformers are integer variables
o Nonlinear objective being an implicit function of
decision variables – energy loss or system demand
are implicit functions of the controls
o High dimension nonlinear constraints – power flow
equations numbering in the thousands in the multiphase system model
o Non-convex objective function and solution set
o High dimension search space – with un-ganged
control, the number of control variables could double
or triple.
For a distribution circuits with 5 capacitor banks, each of
which has two status (on and off), and 5 voltage regulating
devices, each of which has 32 tap settings, the total number of
combinations of controls for ganged operation is 25 * 325, or
for un-ganged operation, 215 * 3215.
It is well known among operation research theoretician and
practitioners that for such mixed integer nonlinear, non
convex (MINLP-NC) problems, it is very difficult to develop
efficient search algorithms. A good algorithm is the one that
delivers optimal or very near optimal solution efficiently,
which is a very essential attribute for online applications.
Since a certain amount of computational resource (CPU
time) is needed to evaluate the loss or demand for a single
specific control solution (a single functional evaluation), an
algorithm that requires fewer number of functional evaluation
in order to find the optimal solution is generally regarded as
more efficient than one that requires more functional
evaluations to achieve the same objective. In the case of
VVO, a single function evaluation involves solving a set of
non-linear equations, the unbalanced load flow, with several
thousand state variables.
III. CONTROL CENTER BASED VVO
This paper presents a control center based VVO that has
been recently developed and implemented by authors. This
new generation of VVO is capable of optimizing very large
and complex unbalanced meshed distribution networks with
online application speed. It combines accurate distribution
system modeling (a general purpose unbalanced load flow
model) and state of the art optimization techniques built on
mixed integer quadratic programming to overcome the
technical challenges for coordinated VVO.
A. General Problem Definition for VVO
The objective of VVO is to minimize the weighted sum of
energy loss + MW load + Voltage violation + Current
3
violation, subject to a variety of engineering constraints, such
as:
o Power flow equations (multi-phase, multi-source,
unbalanced, meshed system)
o Voltage constraints (phase to neutral or phase to
phase)
o Current constraints (cables, overhead lines,
transformers, neutral, grounding resistance)
o Tap change constraints (operation ranges)
o Shunt capacitor change constraints (operation ranges)
The control variables for the optimization include:
o Switchable shunts capacitor banks (ganged or unganged)
o Controllable taps of transformer/voltage regulators
(ganged or un-ganged)
B. Solution Approach
The VVO problem is formulated as a sequence of capacitor
(var) only optimization and voltage regulation (voltage) only
optimization tasks. The var optimization task is formulated as
a mixed integer quadratic program. The voltage optimization
problem is formulated as a sequence of LP problems [19].
C. Distribution Network Model
Detailed and realistic network modeling is essential to the
proper functioning of VVO. Real distribution networks do
not change from unbalanced operation to balanced operation,
or from a meshed network to radial network so that a
simplified network model can be used. The model used for
VVO must fit the operation reality, not the other way around.
For VVO, phase based models are used to represent every
network component. Loads/capacitor banks can be delta or
wye connected. Transformers can be connected in various
delta/wye
and
various
secondary
leading/lagging
configurations with/without ground resistance, with primary
or secondary regulation capability. Figure 1- Figure 4 provide
representative model examples of various distribution network
components including distribution feeders, loads, capacitor
banks and transformers.
~
Node k
V ak +
~
V bk +
~
V ck +
~
I ak
~
I bk
~
I ck
z aa
~
~
Ib
z bb
~
~
Ic
~
~
V ab
V ca
~
~
V bc
I Lb
~
I Lc
Figure 2 : ∆-connected load
+
~
I Ca
~
jBa
-
~
V bn
~
I Cb
V an
jBb
+
~
V cn
jBc +
~
I Cc
Figure 3 : Y-connected capacitor bank
~
~
VA
IA
~
~
Y
Ia
~
~
~
VB
~
VC
~
Va
*
*
-
Ib
IB
~
Y
*
*
~
*
*
~
~
Vb
~
IC
Ic
Y
t:1
Figure 4 : Y/Y connected transformer (one of many possible
connections VVO can model)
D. Implementation Environment
The mixed integer quadratic programming based VVO was
implemented in a distribution management system (DMS).
Shown in Figure 5 is the operator GUI of the DMS, from
which an operator can initiate a variety of integrated
applications, including load allocation, balanced power flow,
unbalanced load flow, outage management, voltage and var
optimization, and others.
z cc
l~
V al
+
I al ~
V bl
~
I bl + ~
V cl
~
I cl +
Node
~
}zab⎫⎪ ~
z ac
~ ⎬
}zbc ⎪⎭
~
Vc
~
Ia
~
~
I La
~
~
I abcsk
~
1
1
I abcsl
[Yabcs ]
[Yabcs ]
2
2
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- -
Figure 1 : Three-wire model of three-phase four-wire
construction
Figure 5 : DMS GUI
4
E. Defining features
In the current VVO implementation, both voltage and var
controls can be ganged or un-ganged. Voltage constraints are
enforced for each individual phase, using phase to ground or
phase to phase voltage, depending on the connection type of
the load.
Table 1 is a concise summary of the features of the
proposed VVO in contrast to prior art.
Table 1 : Comparison of VVO capability with prior art
Prior Art
Single phase
“equivalent” model
Balanced load
Single source
Radial system
Ganged control
Academic system size
Offline performance
Heuristic
Proposed VVO Capability
Multi-phase, unbalanced
Model
Unbalanced load
Multi-source
Meshed system
Ganged or un-ganged control
Real utility system size
Online performance
MIP, MIQP
and Table 5 provide each test case with the information
including specific load allocation (LA) level, percentage of
constant power load, capacitor control type (ganged or unganged control), the demand (kW) and loss (kW) before and
after optimization, the reduction in demand and loss brought
by the optimization. Note that the amount of loss reductions
depends on the initial control setting of the controls.
Obviously, if the loss optimization starts from an optimal
solution, there is little room, if any, to further reduce the loss.
Table 3 : Test system load flow dimension
ID
Max No. Nodes
Max No. Loads
1
2
3
4
5
6
7
8
2019
2838
5577
2622
2460
1599
7884
5100
834
1293
2595
924
855
528
4152
2574
Max No.
Branches
2097
2898
5748
2757
2559
1680
8124
5277
IV. PRELIMINARY EXPERIENCES
The VVO implementation has been tested on various
utilities’ distribution networks using a general purpose office
desktop computer. The sizes of the test systems range from
1,600 to 7,800 nodes and 1,600 to 8,100 branches per circuit.
Optimization improved the loss from 2.5 percent to 67 percent
and demand reduction from 1.4 percent to 5.8 percent. The
algorithm and its implementation is very efficient, it finished
most the optimization in less than 10 seconds. It is able to
converge with 5~8 iterations for voltage regulation
optimization with fifteen un-ganged tap controls.
The key dimensions of the tested system circuits are
provided in Table 2, showing for each circuit the ID, numbers
of circuit total components, feeders, nodes, loads, lines,
capacitor banks, regulating transformers. Table 3 shows the
approximate number of nodes, loads, and branches of these
test circuits. These parameters are indicators of the size of the
nonlinear equations that unbalanced load flow needs to solve.
Table 2 : Test system dimension
ID
Comp.
No.
Feeder
No.
Node
No.
Load
No.
Line
No.
1
1760
4
673
278
2
2455
3
946
431
3
4869
4
1859
865
4
2327
6
874
308
5
2167
5
820
285
6
1406
4
533
176
7
6987
3
2628
1384
8
4512
4
1700
858
Comp. – Component, Cap. – Capacitor
Reg. Xfrm – Regulating Transformer
699
966
1916
919
853
560
2708
1759
Cap.
Bank
No.
7
8
6
8
6
4
7
8
Reg.
Xfrm
No.
1
1
2
1
0
1
2
2
Summarized here are representative test results for var only
optimization, voltage regulation optimization, and combined
voltage and var optimization. For var optimization, Table 4
Table 4 : Base case results for var only optimization
ID
Load
Level
Constant
Power Load
1
2
3
4
5
20%
20%
20%
20%
20%
100%
100%
100%
100%
100%
Base
Demand
(kW)
6605.0
8341.1
9614.8
2941.1
2629.6
Base
Loss
(kW)
36.5
98.5
91.6
139.5
121.9
Control
Type
Ganged
Ganged
Ganged
Ganged
Ganged
Table 5 : Optimized results for var only optimization
Opt.
Opt.
Loss
Demand
(kW)
(kW)
1
6599.1
30.5
2
8313.4
70.7
3
9612.3
89.0
4
2862.3
60.6
5
2547.2
39.5
Opt. – Optimized
ID
Demand
Reduction
(kW)
5.9
27.8
2.5
78.8
82.4
Loss
Reduction
(kW)
5.9
27.8
2.5
78.9
82.4
Loss
Reduction
16.3%
28.2%
2.8%
56.5%
67.6%
For voltage regulator optimization, Table 6 and Table 7
provide each test case with the information including specific
load allocation (LA) level, percentage of constant power load,
voltage regulating transformer control type (ganged or
unganged), the demand (kW) and objective function value
before and after optimization, the reduction in demand and
objective brought by the optimization. The objective column
may have a different value depending if there is any voltage
violations, the objective value is the total demand plus and
voltage violations weighted by a large penalty factor. After the
voltage regulation optimization, both the voltage violations
and the total demand have been reduced. It can be seen that
when the load is 100% constant power, the effect of voltage
regulation optimization is the elimination of the voltage
5
violations and small reduction in the loss. When the
percentage of constant impedance load is significant, voltage
regulation optimization not only reduces the voltage
violations, but also significantly reduces the total demand.
Table 6 : Base case results for voltage regulator only
optimization
ID
Load
Level
CPL
1
30%
50%
2
30%
50%
3
30%
50%
4
30%
50%
6
45%
100%
8
30%
50%
CPL-Constant Power Load
Base
Demand
(kW)
10000.5
12912.8
14618.2
4627.8
3784.1
15567.0
Base
Objective
(kW)
10000.4
14748.4
14618.2
24808.1
7152.7
16319.8
Control
Type
Unganged
Unganged
Unganged
Unganged
Unganged
Unganged
V. CONCLUSIONS
This paper describes the implementation of a state of the art
voltage and var optimization process that can work with multisource, multi-phase, unbalanced, meshed distribution systems.
The centralized VVO method has been implemented to work
with the comprehensive network modeling capabilities of the
unbalanced load flow (UBLF) within a DMS platform. The
implementation has been tested with real utility distribution
circuits with varying degree of unbalance and model
complexity. The implementation demonstrates the feasibility
and effectiveness of centralized VVO.
VI. REFERENCES
[1]
[2]
Table 7 : Optimized results for voltage regulator only
optimization
ID
Opt.
Opt.
Demand
Obj.
Demand
Obj..
Reduct.
Reduct.
(kW)
(kW)
(kW)
(kW)
1
9548.4
9548.4
452.1
452.0
2
12254
12254
659.2
2495
3
14417
14417
201.0
201.0
4
4571.6
18072
56.2
6736
6
3784.8
3784.8
-0.8
3368
8
14909
15108
657.8
1212
Opt.-Optimized, Obj.- Objective, Reduct.-Reduction
Demand
Reduct.
Obj .
Reduct.
4.5%
5.1%
1.4%
1.2%
0.0%
4.2%
4.5%
17%
1.4%
27%
47%
7.4%
[3]
[4]
[5]
[6]
For combined capacitor bank and voltage regulator
optimization, Table 8 and Table 9 provide each test case with
the information including specific load allocation (LA) level,
percentage of constant power load (CPL), capacitor and
voltage regulator control type, the demand (kW) and loss
(kW) before and after optimization, the reduction in demand
(kW) and loss (kW) brought by the optimization, as well as
the CUP time. The results show that both the total demand and
loss are reduced appreciable percentages.
[7]
[8]
[9]
[10]
Table 8 : Base case results for voltage and var optimization
Cap.
Xfrm.
Base
Base
Control Control
Loss
Demand
(Kw)
(kW)
1
30%
50%
10000.5
74.6
Gang
Ungang
2
30%
50%
12912.8
184.4
Gang
Ungang
8
30%
50%
15567.0
149.2
Gang
Ungang
CPL - Constant Power Load, Cap. – Capacitor, Xfrm. - Transformer
ID
LA
CPL
Table 9 : Optimized results for voltage and var optimization
ID
Opt.
Opt.
Demand
Demand
Loss
Reduct.
(kW)
(kW)
(kW)
1
9419.1
69.2
581.3
2
12728
162.2
184.4
8
14925
144.1
641.9
Opt.-Optimized, Reduct.-Reduction
Loss
Reduct.
(kW)
5.4
22.3
5.1
[11]
[12]
[13]
Demand
Reduct.
Loss.
Reduct.
[14]
5.8%
1.4%
4.1%
7.2%
12%
3.4%
[15]
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2009.
VII. BIOGRAPHIES
Xiaoming Feng (SM 2004) is Executive Consulting R&D Engineer with
ABB Inc. He has been with ABB Corporate Research for over ten years. He
leads research in a broad range of areas in power system simulation, control,
and optimization.
William Peterson is affiliated with ABB Inc. in Raleigh, North Carolina.
Fang Yang (M’2007) joined ABB US Corporate Research Center in
Raleigh, North Carolina in 2007. Her research interests include distribution
automation, power system reliability analysis, application of artificial
intelligence techniques in power system control.
Gamini M. Wickramasekara is affiliated with ABB Inc. in Raleigh,
North Carolina.
John Finney joined ABB in 1995 after receiving a PhD from Georgia
Institute of Technology, and has held roles since then as researcher,
developer, and research program manager. Currently John serves as
product manager for ABB's network management software systems.