Load Modeling and State Estimation

Load Modeling and State Estimation

Methods for Power Distribution Systems:

Final Report

Prepared For:

United States Department of Energy

SBIR Grant No. DE-FG02-06ER84647

DoE Project Officer:

Eric M. Lightner

OE / Forrestal Building

U. S. Department of Energy

1000 Independence Avenue, SW

Washington, DC 20585

Email: [email protected]

Phone: (202) 586-8130

Principal Investigator: Team Member:

Thomas E. McDermott, P.E., Ph.D. Mesut Baran, Ph.D.

MelTran, Inc. North Carolina State University

90 Clairton Blvd, Suite A Campus Box 7911

Pittsburgh, PA 15236-3917 Raleigh, NC 27695-7911

Email: [email protected] Email: [email protected]

Phone: (412) 653-0407 Phone: (919) 515-5081

EnerNex Project # 1055-0001

May 7, 2010

mailto:[email protected]



Project 1055-0001 Distribution System State Estimation

i May 7, 2010

Table of Contents

1 Executive Summary ................................................................................................................ 1

2 Accomplishments vs. Goals .................................................................................................... 2

3 Summary of Activity............................................................................................................... 3

3.1 Branch Current State Estimation ...................................................................................... 3

3.2 Feeder Measurements ....................................................................................................... 5

3.3 CIEE / Southern California Edison Project ...................................................................... 8

3.4 CEATI / Southern Company Project ................................................................................ 8

4 Products Developed .............................................................................................................. 10

5 References ............................................................................................................................. 11

List of Figures

Figure 1 - Line Section and State Variables for Branch Current State Estimation......................... 4

Figure 2 - Test Feeder Instrumented with Substation and Feeder Meters, Line Post Sensors,

Wireless Current Sensors, and AMI Voltage Measurements ......................................................... 6

Figure 3 - Data Historian and Feeder Electrical Model Interfaced to State Estimation ................. 7

List of Tables

Table 1 - Project Tasks, Goals, and Accomplishments .................................................................. 2

SBIR/STTR RIGHTS NOTICE

These SBIR/STTR data are furnished with SBIR/STTR rights under Grant No. DE-FG02-

06ER84647. For a period of 4 years after acceptance of all items to be delivered under this grant,

the Government agrees to use these data for Government purposes only, and they shall not be

disclosed outside the Government (including disclosure for procurement purposes) during such

period without permission of the grantee, except that, subject to the foregoing use and disclosure

prohibitions, such data may be disclosed for use by support contractors. After the aforesaid 4-year

period the Government has a royalty-free license to use, and to authorize others to use on its

behalf, these data for Government purposes, but is relieved of all disclosure prohibitions and

assumes no liability for unauthorized use of these data by third parties. This Notice shall be

affixed to any reproductions of these data in whole or in part.


1 May 7, 2010

1 Executive Summary State estimation is a key enabler for any number of “smart grid” applications on the distribution

system; these include reactive power management, outage management, loss reduction, demand

response, adaptable over-current protection, condition-based maintenance, distributed generation

dispatch, integration with transmission system operations, and more. At a February, 2008 DOE

meeting hosted by Pacific Northwest National Laboratory (PNNL), state estimation was listed as

one of eight non-prioritized requirements for modeling and simulation. State estimation’s

importance was reinforced in DOE’s first biannual Smart Grid report [1].

The project objective was to provide robust state estimation for distribution systems, comparable

to what has been available on transmission systems for decades [2]. Classical methods work

poorly on distribution feeders for several reasons:

Very few measurements are available, sometimes only the voltage and current at the

substation.

Switch states, capacitor bank states and transformer/regulator taps may not be directly

monitored, as they typically are on transmission systems.

Many of the feeder measurements are current, rather than power (P and Q).

Three-phase unbalances and low X/R ratios complicate the measurement function

In addition, it’s necessary to use historical load data as pseudo-measurements. Due to the radial

structure of must feeders, load and state estimation are practically synonymous for most North

American distribution systems.

This project used an algorithm called Branch Current State Estimation (BCSE), which is more

effective because it decouples the three phases of a distribution system, and uses branch current

instead of node voltage as a state variable, which is a better match to current measurement. Some

benefits of distribution system state estimation are:

1. Improved reliability

a. Locate faults quicker

b. Restore power quicker, to more customers with less risk of creating more problems

c. Avoid overloads

d. Adaptable over-current protection settings as the load or weather varies

2. Better asset utilization

a. Balance loads among feeders and phases

b. Control and dispatch of distributed generation

c. Volt/VAR control on feeders

d. Just-in-time feeder upgrades and maintenance

3. Predict load response to market or other signals

Two pilot projects are underway at Southern Company (co-funded by CEATI, DALCM 5085)

and at Southern California Edison (co-funded by CIEE, PODR01-X06). Code to support the

algorithm has been added to the OpenDSS simulator (http://sourceforge.net/projects/electricdss/).

http://sourceforge.net/projects/electricdss/


2 May 7, 2010

2 Accomplishments vs. Goals Table 1 summarizes the tasks and goals as originally planned, with actual results. In general, the

changes reflect a normal process of iterating to the best methods and platforms to implement a

new algorithm. It was more difficult than expected to find a host utility for field trials. The two

basic reasons are that utilities don’t have load information correlated to their electrical models,

and that it would take significant engineering and crew time to support this project, even though

EnerNex requested no utility funding. After sustained effort, two pilot projects were secured as

described in Section 3 of this report.

Table 1 - Project Tasks, Goals, and Accomplishments

Task Goal Result

Phase I – 1 IEEE Power Quality Data Interchange Format

(PQDIF) extensions to support state estimation

Ongoing standards participation; PQDIF

measurements not used in pilot project

Phase I – 2 OSIsoft PI data historian platform development PI-ACE training and development;

this platform not selected for a pilot project

Phase I – 3 MatLab platform development Using OpenDSS and C code instead

Phase I – 4 Algorithm specification for phase II Selected BCSE from NC State University;

converted MatLab code to C

Phase II – 1 Load modeling Based on AMI data from CEATI project

Phase II – 2 State estimation – voltage measurement functions,

topology identification, and testing

Completed and published, section

3.1 of this report and [6-9]

Phase II – 3 Bad data identification Testing of meter phase identification, see

section 3.4 of this report

Phase II – 4 Define data historian tabs for real-time feeder

monitoring

Outlined in section 3.2 of this report

Phase II – 5 Feeder model interface Developed and tested CIM interface to

OpenDSS at the first distribution CIM

interoperability tests in December 2009.

MultiSpeak interface development underway.

Phase II – 6 Software build management Code is maintained with OpenDSS on

SourceForge

Phase II – 7 Administration and review Completed Peer Review in October 2008

Phase II – 8 Marketing – initiate field trials Two projects underway, see sections 3.3 and

3.4 of this report


3 May 7, 2010

3 Summary of Activity During the three years of Phases I and II, emphasis shifted from data historians to AMI projects,

which offer the best opportunity for collecting detailed load data. EnerNex contributed to the

MultiSpeak and Common Information Model (CIM) data exchange formats, which offer the best

opportunity for interfacing advanced algorithms into a utility’s existing IT infrastructure.

EnerNex also participated in open-source modeling efforts GridLab-D and OpenDSS, which

offer the most accessible simulation platforms for state estimation. The Branch Current State

Estimation (BCSE) algorithm, developed by Mesut Baran at North Carolina State University,

was selected during Phase I as the best candidate for commercial implementation. These efforts

were documented in previous project progress reports [3-5]. The rest of this section describes the

BCSE algorithm, and two current projects using it.

3.1 Branch Current State Estimation

State estimation is the prediction of all voltages and currents in the system, from a limited set of

actual measurements. It must account for missing or bad data, load variations, and local control

operations such as capacitor switching, voltage regulator operation, or automatic switch

operation. Transmission state estimation uses weighted least squares to estimate all voltage

magnitudes and phases, built around a load flow solution

The problem is tougher on distributions systems because of high feeder resistance (or low X/R

ratio), unbalanced loads and impedances, predominance of current magnitude measurements

rather than real and reactive power, and relatively fewer measurements than on a transmission

system.

Equation (1) expresses that state estimation minimizes the estimated error by weighted least

squares. The state variables, X, are the node voltage phasors (magnitude and phase). For

transmission they are positive sequence, or balanced three-phase. The measurement functions, h,

are typically load flow solution outputs corresponding to measurements from the SCADA

system. For example, h could be a node voltage (simplest) or a branch current. Or it could be P

and Q in a branch, determined by node voltages at each end, and knowing the branch (line or

transformer) impedance. Therefore, state estimation requires an electrical model of the power

system, too. The measurement values are z. The weighting functions, w, can be adjusted for the

type or quality of individual measurements.

2

1

))(()( xx ii

m

i

i hzwJmin (1)

If you have only the substation voltage and feeder current, then you have to estimate all the other

quantities by scaling the load models, based on archived data. Sometimes the archived data

accounts for season, day of the week, hour of the day, etc., but these are relatively coarse

adjustments. Inherently, the pseudo-measurements will be less accurate than real measurements.

The resulting state estimates are also relatively coarse. Existing distribution system state

estimators operate this way. The results are used to identify overloads and assist in service

restoration, but they are not good enough for “smart grid” applications.


4 May 7, 2010

Sometimes there will be a few downstream current measurements available, such as from an

automated recloser. While helpful, these are not as good as the P, Q measurements typical of

transmission state estimators. One reason is that current magnitude measurements convey no

direct information about phase angles (actually, there are non-unique solutions for the phase

angles). Another reason is that equation (1) is “ill conditioned” for current measurements on

lightly loaded lines, because of the nearly equal voltage magnitudes at each end.

A typical transmission state estimator “decouples” the solution’s real and imaginary parts, taking

advantage of the fact that real power flow depends mainly on phase angles, while reactive power

flow depends mainly on voltage magnitudes. This doesn’t work very well on distribution feeders

with relatively high resistance, or low X/R ratio. The solution cannot be decoupled. Also,

distribution feeders can be significantly unbalanced, and the state estimator should account for

that by providing estimates by phase.

BCSE still uses weighted least squares, but estimates the branch currents instead of node

voltages. Any voltage measurements are ignored, except for the substation bus voltage, which

becomes the reference. If you know the substation voltage plus all the branch currents, then you

know all of the downstream node voltages as well.

Figure 1 and equation (2) show the line voltage drop as a function of branch current. BCSE still

needs an electrical model of the system. There is a measurement function for each of the three

phases of a branch, but it depends only on the currents in that phase. So the state estimation

problem is decoupled by phases. The current magnitude and phase angle solutions are still

coupled, because of the low X/R ratio. Overall, the solution is easier than if using node voltages

as the state variable.

A current magnitude measurement has a simple measurement function that fits right into the

BCSE. This gets around all of the problems with current measurements in traditional SE. The

pseudo-measurements from archived load data, namely power and power factor, are converted

into load current magnitude and angle for BCSE. The missing data and topology identification

issues still apply to BCSE. These may add more variables to be estimated, such as capacitor and

switch on/off status.

Vt Vs

ph. 1

ph. 2

ph. 3

grnd.

S t,1

S t,2

S t,3

Il,1

Il,2

Il,3

Il+1,1

Vt,1

Vt,2

Vt,3

Vs,1

Vs,2

Vs,3

gl

z11 z12 z13

z21 z22 z23

z31 z32 z33

Il,1

Il,2

Il,3

(2)

Figure 1 - Line Section and State Variables for Branch Current State Estimation

Measurements of P and Q, either real or pseudo-measurements, lead to linear measurement

functions in the real and imaginary parts of the current. The most recent estimated values of node

voltage appear in the denominator of (3), and (4) combines the real and imaginary parts.

(3)


5 May 7, 2010

(4)

Current magnitude measurement leads to non-linear measurement functions, coupling real and

imaginary parts of current in equations (5) – (7).

(5)

(6)

(7)

Voltage drop measurement couples both phases and real / imaginary parts of the current in

equations (8) – (10). This leads to some loss in efficiency when BCSE processes voltage

measurements from AMI. In a pilot project, more emphasis was placed on AMI demand interval

measurements, which can be converted to equivalent P and Q, or to current magnitude.

(8)

(9)

(10)

Once all of the measurement functions have been defined, they are used to update the gain

matrix, G, and measurement Jacobian, H. Equations (11) and (12) are used to iteratively refine

the estimated branch currents in x.

(11)

(12)

3.2 Feeder Measurements

Figure 2 shows a radial feeder with measurement of real power (P), reactive power (Q), voltage

(V), current magnitude (I), and temperature (T) at the feeder breaker or substation bus. This is

supplemented by wireless current sensor measurements (I) at key branch points. While some

automated devices (e.g., reclosers) could also provide such measurements, they are much more


6 May 7, 2010

expensive to deploy. Wireless sensors on switches and capacitor banks can assist greatly with

network topology identification. Line post sensors at key points provide a complement of P, Q,

I, and V measurements out on the feeder. Figure 2 also shows a large number of voltage

measurements (V) from customer meters; these meters also collect load information for

statistical analysis and modeling.

R

R

vv

v

v

v

v

v

v

v

v

v

v

v

I I II

I

P, Q, I

P, Q, I

T, V

I

P, Q, I, V

Figure 2 - Test Feeder Instrumented with Substation and Feeder Meters, Line Post Sensors, Wireless Current

Sensors, and AMI Voltage Measurements

Figure 3 shows how several data sources can be linked to BCSE through a data historian. Several

commercial SCADA and EMS products interface with Osisoft’s PI already.

Level 1 tags (or points, in SCADA terminology) cover what most utilities have today; although

they might not be using it for distribution state estimation.

1. Substation voltage

2. Feeder current, power, and reactive power

3. Ambient temperature

Level 2 adds the direct measurement of local control states that would otherwise have to be

estimated. For example, feeder capacitor banks could either be centrally dispatched, or

communicate their on/off state back to the substation. Disconnect switches can be automated and

their actual state would then be a measurement. Likewise for line voltage regulators. Most

automated products outside the substation fence are not compliant with IEC 61850, so

integration of them into a SE could be a bit more work.

1. Switch, capacitor, and tap changer status

2. Switch and regulator currents

Level 3 represents an attempt to flesh out the missing feeder measurements. Some utilities have

a limited number of power quality meters that could provide some data. AMI has the potential of

providing a measurement of some type at every customer load. Various low-cost sensors are also

under development; these may provide current or other measurements.


7 May 7, 2010

1. Power quality monitors (utility or customer)

2. Automated meter reading devices

3. Modern sensors

BCSE still requires a redundant set of measurements to work. Whatever measurements are still

missing have to be filled in with pseudo-measurements. These can be better utilized than they are

now, by incorporating real-time measurements such as temperature, or similar loads that have

instrumentation. This requires a statistical processing model, based on the listed types of data.

Pseudo-measurements can also be helpful in forecasting load response in real time. For example,

one might wish to predict the price signal necessary to achieve a certain load reduction in a

certain period of time. Useful load model data includes:

1. Load survey data

a. Customer classes

b. Load profiles by day and hour

c. kW, and either kVAR or power factor

d. corresponding weather data (temperature)

2. Feeder section loads

a. Customer numbers or size, and class

b. Monthly energy (kWh) for each load, with corresponding temperature

Figure 3 - Data Historian and Feeder Electrical Model Interfaced to State Estimation


8 May 7, 2010

3.3 CIEE / Southern California Edison Project

The California Institute for Energy and Environment (CIEE) funds this project under sub-award

PODR01-X06. It includes the use of LightHouse MV current sensors from Tollgrade

Communications [www.tollgrade.com/lighthouse]. These are clamp-on devices powered

inductively, transmitting current measurements through a wireless network. The cost is relatively

low, at about $700 per sensor. Tollgrade is based in Pittsburgh and has been in regular

communication with EnerNex about these devices.

SCE is primarily interested in the outage management aspect of state estimation, in a learning

platform for advanced distribution management system (DMS) applications, and in the

possibility of improved fault location. The LightHouse MV sensors have current waveform

capture capability that will aid in fault location. One overhead feeder served from a single

substation will be selected by SCE. The substation meters will be supplemented with

approximately 25 LightHouse current measurements linked to a single aggregator, and many

AMI voltage measurements.

SCE uses the following software and hardware that will be interfaced to the BCSE:

CYME’s CYMDIST analysis software, for the feeder electrical models

GE SmallWorld, for GIS interface and display

CGI (to become M3I) for the Outage Management System (OMS)

Emeter, for meter data management (MDM)

Itron, for automated meters

InStep eDNA, for data historian

The project was delayed starting due to state budget constraints in California, and in the

meantime the responsible personnel at SCE were transferred to other responsibilities. Site

selection is now underway to complete the project during 2010.

3.4 CEATI / Southern Company Project

CEATI (formerly Canadian Electric Association Technology Institute) funds this project as

DALCM 5085, under the Distribution Asset Life Cycle Management group. The overall goal is

to identify which phase each customer load is actually connected to, using wireless AMI data.

(Note: AMI hardware that transmits data over distribution wires would positively identify the

phase, but much AMI hardware is wireless). The application can use archived data; it does not

have to work in real time. The feeder model was converted from CYMDIST to OpenDSS for the

BCSE to run, using AMI and distribution SCADA measurements.

Results to date indicate that:

1. Some AMI meters don’t have sufficient precision in their voltage measurements. For

state estimation, the hardware and configuration need to be specified ahead of time.

2. Most AMI systems don’t sample or transmit voltage measurements as often as load

measurement. Some AMI systems only transmit voltage alarms.

3. A model of the service drop and transformer is necessary to link feeder voltages to AMI

voltages. This requires additional data and assumptions beyond the feeder model.


9 May 7, 2010

4. With line post sensors, AMI can be allocated to “zones” with improved estimation

results.

5. Further improvement is possible if using AMI demand interval measurements, either 15-

minute or hourly, to serve as pseudo-load measurements. Selected AMI voltage

measurements can then be checked against the estimated feeder model. However,

precision of the demand measurements can still be an issue for shorter demand intervals.

The final report on this project is due within the next month or two.


10 May 7, 2010

4 Products Developed Four conference papers have been written and presented [6-9]. These all acknowledge

government support through this project. All are available through IEEE Xplore.

The project has fostered and supported many other collaborations:

MultiSpeak Initiative – proposed extensions to facilitate feeder model exchange; these

were adopted in MultiSpeak version 4.

International Electrotechnical Commission (IEC) Working Group 14 – contributing to an

international standard on distribution system model exchange, including improvements to

the Common Information Model (CIM) for distribution.

Electric Power Research Institute – performed a gap analysis of the CIM, facilitated the

release of their Distribution System Simulator (OpenDSS) on SourceForge, and provided

a new sparse matrix solver for OpenDSS.

Pacific Northwest National Laboratory – development partner for GridLab-D, working

on OpenDSS and MultiSpeak /CIM interfaces.

Tollgrade Communications – helped with business case development for Lighthouse MV

wireless current sensor.

North Carolina State University – is a subcontractor on this project. Also, EnerNex

supported their successful proposal to NSF establishing an engineering research center.

Elster Electric – discussions of pilot project with their Advanced Grid Initiative (AGI).

OSIsoft – EnerNex is a development partner for the PI data historian.

CEATI and Southern Company / Alabama Power – EnerNex and NC State have nearly

completed a pilot project using AMI and DSCADA for distribution system phasing. Use

of the state estimation algorithm was a key part of this project.

CIEE and Southern California Edison – EnerNex, Tollgrade, and NC State are

conducting a pilot project incorporating advanced state estimation with wireless current

sensors into a distribution management system.


11 May 7, 2010

5 References 1. U. S. Department of Energy, Smart Grid System Report, July 2009.

2. A. Abur and A. G. Exposito, Power System State Estimation: Theory and

Implementation, Marcel Dekker, 2004.

3. Mesut Baran and Jaesung Jung, “Branch Current State Estimation with Voltage

Measurements”, May 29, 2008.

4. T. E. McDermott, “Load Modeling and State Estimation Methods for Power Distribution

Systems: Phase II Continuation Report,” June 13, 2008.

5. T. E. McDermott, “RDSI Peer Review Summary: Load Modeling and State Estimation

Methods for Power Distribution Systems,” October 30, 2008.

6. M. Baran and T. E. McDermott, "Distribution system state estimation using AMI data,"

IEEE Power Systems Conference and Exposition, pp.1-3, 15-18 March 2009, Seattle,

WA.

7. M. Baran and T. E. McDermott, "State estimation for real time monitoring of distribution

feeders," IEEE Power & Energy Society General Meeting, pp.1-4, 26-30 July 2009,

Calgary, AB.

8. M. E. Baran, Jaesung Jung, and T. E. McDermott, "Including voltage measurements in

branch current state estimation for distribution systems," IEEE Power & Energy Society

General Meeting, pp.1-5, 26-30 July 2009, Calgary, AB.

9. M. E. Baran, Jaesung Jung, and T. E. McDermott, "Topology error identification using

branch current state estimation for distribution systems," IEEE Transmission &

Distribution Conference & Exposition: Asia and Pacific, pp.1-4, 26-30 Oct. 2009, Seoul,

Korea.

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