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Brazilian System Operator Online Security
Assessment System Jorge Jardim, Senior Member, IEEE, Carlos Neto and Marcelos Groetaers dos Santos, Member, IEEE
On the other hand, there have been many challenges for
deploying real time security assessment tools, the main being
the achievement of accuracy and time performance. As
operating conditions vary continuously, obviously the
assessment must be fast enough to be of any use.
Abstract--Implementation details and performance results of
the Brazilian System Operator dynamic security assessment are
presented. It describes the adopted models, analytical methods
and functions. Important implementation issues such as network
size, contingency screening, EMS integration, user interface and
quality of data are also commented. . The computational burden of a security analysis depends
basically on network model size, number of contingencies
(disturbances with considerable probability of occurring),
analytical methods, assessment functions needed and level of
modeling details. Accuracy is dictated mainly by the later.
Early works in this area would only consider analytical
methods based on simplified network modeling, as there was
no other alternative to meet time requirements. Today, high
performance computing is available at low cost. This has
made detailed modeling methods competitive. Hybrid
security assessment system solutions that combine simplified-
fast and detailed-accurate algorithms are also considered when
designing a security assessment system, as an attempt to take
advantage of the best of both worlds.
Index Terms -- Contingency analysis, distributed processing,
dynamic security assessment, dynamic simulation, energy
function, power flow, security region.
I. NOMENCLATURE
DSA - Dynamic Security Assessment System.
EMS - Energy Management System.
OLTC - On Load Tap Change.
ONS - Operador Nacional do Sistema Elétrico - Brazil
RTU - Remote Terminal Unit.
II. INTRODUCTION
POWER system security assessment is a fundamental
process in the expansion and operational planning of
power grids. Assessments are based on simulation studies to
quantify vulnerability of power systems when subject to major
disturbances. Such studies are performed off-line by expert
engineers for the purpose of upgrading the grid, planning
near-future outage schedules, validating economical or
transaction dispatches, etc. However, the need of online
security assessment has been recognized for decades [5-8].
There are plenty of reasons for that, but essentially it may be
risky or anti-economical or both to relying only on off-line
studies for operating stressed networks or networks with high
levels of operational uncertainties. It is not possible, even for
well-planned systems, to assess all possible real-time
operating conditions off-line because of the combinatorial
nature of the problem, which makes it very complex from the
computational viewpoint. Consequently off-line operation
planning can be over conservative, when based on worst-case
scenarios, or optimistic, when oversees possible degraded
operating conditions.
Other critical issues are related to quality of real-time data
available and possible impacts of advanced applications on
existing operation and planning processes.
In this paper the design choices made for the ONS DSA
[11-13] and related critical issues are presented. Section III
briefly describes the characteristics of the Brazilian system
and ONS control centers. Section IV presents the methods
and algorithms adopted and main assessment functions. Other
implementation issues are depicted in Section V. Section VI
shows performance results. This is followed by conclusions.
III. DESCRIPTION OF THE BRAZILIAN SYSTEM
The installed capacity of the Brazilian power system is
approximately 88.000 MW being 84% hydro and 16% thermal
(coal, oil, nuclear and gas) generation. There are 80,000 km
of transmission lines (230 kV and above). The peak load is
around 57 MW. The system is owned by various utilities and
operated by the Operador Nacional do Sistema Elétrico –
ONS.
The power system model for studies at operational
planning environment contains approximately 4000 buses and
5600 branches, considering all voltage levels. Supervised
data from the various utilities are concentrated in four regional
centers and retransmitted to the main control center. All
centers perform their own topology processing, state
estimation and steady-state analysis, but the model available at
the main control center is more suitable for dynamic security
J. Jardim is with Operador Nacional do Sistema Elétrico - ONS, 20091-
005 Rio de Janeiro, RJ Brazil (e-mail: jorge.jardim@ons.org.br).
C. Neto is with Operador Nacional do Sistema Elétrico - ONS, 20091-005
Rio de Janeiro, RJ Brazil (e-mail: cneto@ons.org.br).
M. G. dos Santos is with Operador Nacional do Sistema Elétrico - ONS,
20091-005 Rio de Janeiro, RJ Brazil (e-mail: marcels@ons.org.br).
7142440178X/06/$20.00 ©2006 IEEE PSCE 2006
analysis, as it is a superset of the regional models. This
includes a supervised network (mainly 230 kV and above) of
approximately 1800 buses. Ongoing installation of new
Remote Terminal Units - RTUs will increase this figure in a
near future.
IV. METHODS, ALGORITHMS AND MODELING
A. Assessment Functions
Six security analysis methods are currently available in the
ONS DSA, as follows.
Operating Point Stead-State Contingency Analysis –
This is the classical steady-state contingency analysis.
For this method four criteria are checked and
tabulated: thermal limit violation, voltage violation,
voltage deviation and no power flow solution.
Contingencies can be ranked according their respective
severity.
Operating Point Dynamic Contingency Analysis –
This is a dynamic contingency analysis. For this
method nine criteria can be checked and tabulated:
transient temporized voltage sag, transient
instantaneous voltage sag, transient temporized voltage
swell, transient instantaneous voltage swell, MW
margin for critical generators and critical cluster,
synchronous generator angular damping, transient
angle deviation, steady-state angle deviation and
frequency deviation.
Import-Export steady-state transfer limit between two-
generation areas. Boundaries are given for the four
steady-state criteria.
Import-Export dynamic transfer limit between two-
generation areas. Boundaries are given for the nine
dynamic criteria.
Steady-state security region computation - This is the
set of secure power dispatch for three interconnected
generation areas. A security contour can be given for
each steady-state criterion.
Dynamic security region computation - This is the set
of secure power dispatch for three interconnected
generation areas. A security contour can be given for
each dynamic criterion.
Import/export transfer limit functionality computes security
borders of a transmission corridor. Two interconnected
generation regions are defined. Power is exchanged back and
forth to search the security boundaries (import/export). A
boundary per criterion can be computed. The available criteria
are the same as the contingency analysis.
The security region is similar to import/export limit
calculations, but in this case there are three-generation groups
instead of two. It is also possible to consider two-generation
groups and a load group as a set of variable parameters. The
available criteria are the same as for contingency analysis.
B. Preventive and Corrective Functions
The DSA offers the following resources for preventive
corrective actions:
Ranking of voltage control resources (generator or
shunt compensation) to correct voltage violation.
Suggested MW re-dispatch (amount and location) to
alleviate thermal limit violation.
Suggested MW re-dispatch (amount and location) to
avoid angular instability.
Suggested MW re-dispatch (location) to improve
damping.
Suggested load shedding (amount and location) to
move from alert/emergency states to secure state.
One important aspect to be emphasized here is the
importance of results visualization, in particular for security
region computations, as it automatically provides simple
means of removing violations by moving the operating point
in the generation/load space (MW changes).
C. Analytical Methods
ONS DSA adopts the detail modeling approach, but it also
contains simplified methods for filtering and sensitivity
calculations. The following methods are used in the
implementation of the above functions.
Power Flow Methods: The power flow methods can
represent up to 100,000 buses and 100 bus sections per bus.
Models for voltage sensitive loads, shunt compensation
(continuous and discrete), phase shifters, on load tap changers
and DC links (conventional and capacitor commutated
converter) are available. The power flow computation
methods are the following.
DC power flow: This method is used only for
initialization and contingency screening purpose.
Full-Newton power flow: In this method all controls
(OLTC, DC Link, etc.) are solved simultaneously by
the Newton method.
Synthetic Dynamic Power Flow: This is a very robust
power flow method [14] in which most of controls are
represented by synthetic dynamic models. The
formulation is such that if the solution exists and the
problem is properly formulated, the solution is found.
This method is many times slower than the Newton
method. Therefore, it is only used when it is absolutely
necessary to confirm the existence of a power flow
solution.
Continuation Power Flow: The tangent vector
approach [9] is adopted for this implementation. The
continuation method is used for moving operating
points in search of security borders or for computation
of PV curves (maximum loadability).
Optimal Power Flow: The primal-dual interior point
method [2] is adopted for this implementation. It is
currently used to generate bases for near-real-time and
days-ahead scenarios. There are also plans for using it
for contingency constrained optimization.
Sensitivity Analysis: This is used to ranking controls
for voltage and flow controls and estimate contingency
severity.
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V. OTHER IMPORTANT ISSUESTime Domain Simulation Method: The simulation method
adopts the ABM-BDF numerical integration method [1,3]
associated with variable-step-variable-order approach and
simultaneous solution of algebraic and differential equations.
These characteristics allow improved performance (more than
10 times faster) compared to the fixed time step approach and
high numerical stability. Simulations can be early terminated
either by instability detection or steady-state convergence.
A. Network Size
Of course, the size of the network directly impacts
performance. Therefore, it is important to minimize the size
of the network through the use of equivalents, as long as it
does not impact the results of the assessment for the areas of
interest. The present supervised and planning study networks
contain approximately 1800 and 4000 buses respectively.
Currently there is no external equivalent represented in the
real-time simulation model. But there is an ongoing study to
determine the ideal network size for this model. Simulations
with both models must provide similar results. It is expected
that there will be necessary to include some low-voltage
unsupervised equivalents to improve the model.
Energy Function and Single Machine Equivalent Methods:
Individual numerical energy functions [16] and SIME [4]
methods are used for energy margin computation and filtering
of critical machines.
Prony Analysis: This method [15] is used for modal
analysis (damping assessment) of synchronous machines.
D. Dynamic ModelsReal time base cases include almost all generation power
plant and their respective transmission systems. Therefore, no
external dynamic equivalent is required, but similar generating
units at a power plant are automatically aggregated for
security assessment studies, which significantly reduces the
size of the dynamic model.
Dynamic models necessary to simulate the Brazilian
system include synchronous machines and respective controls,
dc links (conventional and capacitor commutated converter),
static var compensators, controlled series capacitors, OLTC,
out-of-step, under-voltage and under-frequency relays, and
special protection schemes.B. Contingency Set
E. Distributed Processing Architecture A large set of contingencies dramatically impacts
performance. Therefore, it is important to have means of
selecting only those that can potentially cause criteria
violation. So far, off-line security assessment studies around
the world have been performed using small sets of
contingencies. This can be done because engineers know
from repetitive simulations what are the bottlenecks of their
systems. Besides, it is not practical to process, book-keep and
post-process thousands or even hundreds of cases manually.
Therefore, it is quite unnecessary and a waste of time to
impose thousands of contingencies to the real-time security
assessment. On the other hand, the contingency set cannot be
too small at the risk of missing critical ones. Therefore, a
larger than necessary set is initially used and subsequently
filtered (contingency screening) to improve performance.
To meet the performance requirements, the DSA adopts a
distributed processing approach in a manager/worker
(master/slaves) configuration, Fig. 1. The manager process
contains the high level instructions to perform a security
assessment functionality. The low-level instructions (solve a
power flow problem, perform a time domain simulation, etc.)
are done at worker processes. Manager is responsible for
generating base cases, distributing tasks among servers,
collecting the respective reports, communicating with external
world, managing distributed resources, storing/displaying
results and plots. Workers receive tasks, process them using
the specified power system simulation tool and send
respective diagnosis in a report to the Client.
DSA
Server N
DSA
Server 2
DSA
Server 1
DSA Manager EMS Algorithms and methods based on simplified modeling and
artificial intelligence methods are more suitable for
contingency screening. So far, the DSA has only used DC
power flow and sensitivity analysis for contingency screening.
A new method for dynamic contingency screening, which
does not require time simulation, is under development.
However, the authors consider that artificial intelligence
algorithms are potentially the best approach in this area [12].
The screening should also be able to simultaneously select
cases based on multi-criteria (steady-state and dynamic).
Fig. 1. Client-Server distributed processing environment.
Fail of a worker process can be detected by the manager
process, which reassigns the task to another process. This
also causes an alarm for system maintenance. A monitor
process in the EMS detects fails of the manager process and
restart the DSA reallocating the manager process to another
node if necessary.
C. Quality of Real-Time Data
Online security assessment is only possible if there are
sufficient and good quality real-time data available for the
kind of required analysis. The real-time model in ONS
includes most of the bulk transmission system and power
plants. This enables dynamic studies for almost all regions.
However, problems with data measurement, database and state
estimation have prevented full use of the DSA tools. A lot of
The DSA at the main control center uses 12 3GHz
processors and run under Windows platform. Regional
control centers will also run DSA functions in a near future.
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effort has been dedicated to improvements on these areas.
Special attention must be paid to the problem of ill defined
or conflicting controls in the power flow model (e.g., two
parallel OLTC transformers controlling different buses).
Although the software must be prepared to deal with such
situations, this has a performance cost that can and should be
avoided.
D. Impact on Processes
Implementation of any online security assessment
functionality affects existing planning and operating
processes, which may need to be re-engineered. Operating
orders must be adapted to avoid possible conflicts with the
DSA results.
E. Integration with EMS
In the current phase network state is exchanged by flat file,
the DSA system runs asynchronously with EMS and the
graphical user interface is the one implemented in the DSA
system. In the last phase, the DSA system access the EMS
database to retrieve network data, runs synchronously with
EMS, DSA results are updated in the EMS database and EMS
displays the results.
ONS uses presently three different technologies in its five
control centers. As there is a high probability of upgrading to
just one in a near future, the project to tightly integrate the
DSA has been postponed.
F. User Interface
The DSA graphic interface is based on windows. Data
input and editing is done through dialog boxes. Data output is
displayed in report tables, Fig.2, nomograms (2-dimensional
plotting), Fig. 3 , and single-line diagrams, Fig. 4.
The security region visualization, Fig. 3, is one of the most
powerful visualizations tools for security assessment.
Dispatchers can see if the operating point in generation
coordinates lies in the secure (green) or alert (yellow or red)
region. If the operating point (OP) is in the yellow region, at
least one of the credible contingencies will cause thermal limit
violation. If the operating point is in the red region, at least
one of the contingencies will cause instability. Mouse
positioning tips and report tables provide detail information
per violation. One contour per security criterion can be plot.
For example, a contour for voltage drop (blue) is also plot in
Fig. 3.
Single line diagrams are useful to quickly inspect out of
services components and data editing in study mode. It can be
also a powerful visualization tool for signaling violations.
There are plans for developments in this area.
The user interface resides in the manager process, but are
imported and displayed in any of the dispatcher's monitors or
projected on the control room wall as any other EMS
interface.
G. Future Developments
There are two main development plans for this project.
One is focused on the improvement of contingency screening
methods using fast-simplified methods and artificial
intelligence algorithms. The other is to quantify risk on the
assessment [10].
Fig. 2. Report tables - generator security margin.
Fig. 3. Security region nomogram.
Fig. 4. Single line diagram.
VI. PERFORMANCE
The performance must meet the requirements and is
affected by the following factors.
- The choice of methods and algorithms as mentioned in
the previous paragraphs.
- Size of the network model.
- Number of contingencies to be simulated.
- Computational resources available.
Ideally the time taken for completing security assessment
analysis should be minimal, i.e., assessment should be
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completed a few seconds after the system state has been
estimated. In practice, some minutes are tolerated, as it is
assumed that system state does not change significantly during
the tolerance period. However, one can claim that tolerance in
the range of minutes can be dangerous as events causing large
discontinuities, such as tripping of a major transmission line,
can invalidate the assessment results, living dispatcher without
reliable information for the same period. On the other hand,
the existence of real-time security assessment implies that
major events (disturbances) have already been considered in
previous analyses and should at most move the system to alert
state.
Performance requirements are typically in the range of 2 -
15 minutes. The actual response varies with system operating
conditions, i.e., computational cost of power flow and time
domain simulations are generally more expensive for stressed
cases. The target performance per function for ONS DSA is
shown in Table I.
TABLE I
PERFORMANCE REQUIREMENTS
Functionality Contingencies Performance
Static Contingency
Analysis
100 < 10 s
Dynamic Security
Analysis
100 < 1 min
Static Transfer
Limit
10 < 5 s
Dynamic Transfer
Limit
10 < 30 s
Static Security
Region
10 < 1 min
Dynamic Security
Region
10 < 2 min
State estimation is processed every 2 minutes. Therefore, it
is desirable that the most expensive function (dynamic
security region) should run within this cycle. Again, if the
system size increases and/or more functions or contingencies
need to be added in the cycle, it is possible to adjust the
response by adding more processors.
VII. CONCLUSIONS
This paper presents the main features and experience with
the ONS DSA. The system employs traditional analytical
tools such as power flow and time domain simulations
methods.
Detailed modeling and embedded diagnosis algorithms
such as energy function and Prony analysis are used for
dynamic simulations. Functions for contingency analysis,
transfer limits and security region are available. The DSA
meets the required performance using distributed processing.
Significant effort is being applied to improve the quality of
real-time data, which is a sine qua non condition to implement
an online DSA.
Integration of the DSA with the EMS is currently through
flat files, but a tight coupling is planned.
Special attention must be paid to the impact of online DSA
in real-time and planning processes.
Future developments are in the field of risk security
assessment and artificial intelligence contingency screening.
VIII. REFERENCES
Periodicals: [1] J. Y. Astic, A. Bihain and M. Jerosolimski, “The mixed Adams - BDF
Variable Step Size Algorithm to Simulate Transient and Long Term
Phenomena In Power Systems”, IEEE Trans. on PS, Vol. 9, No. 2, May
1994.
[2] S. Granville " Optimal reactive Dispatch Through Interior Point
Methods" , IEEE Trans. PS, Vol. 9, No. 1, Feb 1994.
Books:[3] J. D. Lambert, "Numerical Methods for Ordinary Differential Systems:
The Initial Value Problem", Wiley, 1991.
[4] M. Pavella, D. Ernst, D. Ruiz-Veja “Transient Stability of Power
Systems: A Unified Approach to Assessment and Control”, Norwel,
MA: Kluwer, 2000.
Technical Reports: [5] T. E. DyLiacco, "Control of Power Systems via the Multi-level
Concept," Case Western Reserve University System Research Center
Report No. SRC-68-19, June 1968.
Papers from Conference Proceedings: [6] H. D. Limmer, "Security Applications of On-line Digital Computers,"
Second Power Systems Computation Conference, Stockholm, June 27,
1966.
[7] S. Hayashi, "Power System Security Assessing by Digital Computer
Simulation - Basis Control," in Proc. PICA Conference, Denver,
Colorado, May 18-21, 1969.
[8] A. S. Debs, A. R. Benson "Security Assessment of Power Systems," in
Proc. System Engineering for Power: Status and Prospects, Henniker,
NH, Washington, DC, 1975.
[9] V. Ajjarapu, C. Christy, 'The Continuation Power Flow: A Tool for
Steady State Voltage Stability Analisys', IEEE PICA, May 91, pp 304-
311.
[10] A. M. Leite da Silva, J. L. Jardim, A. M. Rei, J. C. O. Mello “Dynamic
Security Risk Assessment”, Power Engineering Society Summer
Meeting, 1999 IEEE, Vol. 1, pp 198-205, 18-22 July 1999.
[11] J. L. Jardim, C. A. da S. Neto, A. P. Alves da Silva, A. C. Zambroni de
Souza, D. M. Falcão, C. L. T. Borges, G. N. Taranto, “A Unified On-
Line Dynamic Security Assessment System”, Cigré, Paris, France, 27
Ago – 1 Sep 2000.
[12] J. L. Jardim, “Online Dynamic Security Assessment: Implementation
Problems and Potential Use of Artificial Intelligence”, Power
Engineering Society Summer Meeting, 2000. IEEE, Volume: 1, 16-20
July 2000.
[13] J. L. Jardim, C. S. Neto, W. T. Kwasnicki “Design Features of a
Dynamic Security Assessment System”, IEEE Power System Conference
and Exhibition, New York, Oct 13-16, 2004.
[14] J. L. Jardim, B. Stott, "Synthetic Dynamics Power Flow", IEEE General
Meeting, San Francisco, 12-16 June 2005
[15] J. F. Hauer "Application of Prony Analysis to the Determination of
Modal Content and Equivalent Models for Measured Power System
Response”, IEEE Winter Meeting, 215-4 PWRS, 1991.
[16] J. L. Jardim, B. Cory, N. Martins, “Efficient Transient Stability
Assessment Using Transient Energy Function”, Power Systems
Computation Conference – PSCC, Trondheim, Norway, June 1999.
[17] X. V. Filho, M. V. Ferreira, P. Gomes, M. G. Santos, E. Nery "A
Probabilistic Approach to Determine the Proximity Effect of the Voltage
Collapse Region," Cigré Session, Paris, France, Sep 1994.
[18] R. Prada, X. V. Filho, P. Gomes, M. G. Santos "Voltage Stability System
Critical Area Identification Based on the Existence of Maximum Power
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PSCC, Avignon, France, Aug-Sep 1993.
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IX. BIOGRAPHIES
Jorge Jardim received his PhD from Imperial College, London, UK. He
has worked in Rio de Janeiro in the FURNAS utility company, and at the
national electric power research center CEPEL. He is currently at ONS, the
operator of the Brazilian national power system. From 1999-2002 he was
with BC Hydro in Vancouver, Canada. His main field of interest is power
system analysis methods and software, with emphasis on voltage and dynamic
stability.
Carlos S. Neto received his B.Sc. in Electrical Engineering, in 1984, and
M.Sc. in Systems Engineering and Computation, in 1999, both from the Rio
de Janeiro Federal University, Brazil. He worked for a consulting (Themag)
and a utility (Furnas Centrais Eletricas) company and for a research center
(CEPEL) in Brazil. He worked also for a utility company (BCHydro) in
Canada. At present he is at ONS, Operador Nacional do Sistema (Brazilian
ISO). He has worked with power systems applications development and
studies in planning and operation areas.
Marcelos Groetaers dos Santos received his B.Sc. in Electrical
Engineering from Federal University of Rio de Janeiro, UFRJ, in 1982 and
M.Sc. from Federal University of Itajuba, UNIFEI, in 1995. He is presently
completing the D.Sc. degree at Fluminense Federal University, UFF, in the
area of power system security risk assessment. He was with CEPEL from
1982 to 1984. In 1985 he joined ELETROBRAS where he was in charge of
studies on power system steady state and dynamic performance and control in
the System Operational Planning and Technological Development
Department.
He lectured Power System Stability from 1986 to 1992 at Veiga de
Almeida University, UVA, Brazil, and at Escuela Superior Politecnica del
Litoral, ESPOL, Ecuador, in 1996, where he lectured and developed projects
in power system engineering education. He is currently working at Operador
Nacional do Sistema Elétrico, ONS, the Brazilian ISO, as manager of the
Electrical Methodologies and Models Department.
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