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An approach to bulk power system performance assessment
El,ECffl lC EL SEVIER
Electric Power Systems Research 32 (1995) 145-151
An approach to bulk power system performance assessment M.Th. Schilling, P. Gomes UFF-('AA 'ELETROBR,4S, Division of Technological Development of the Electric Operation (DOLT), Avenida Marechal Floriano 19, 20080-003 Rio de Janeiro, R J, Brazil Received 10 October 1994
Abstract
This paper focuses on the main characteristics, basic concepts and preliminary quantitative results of an integrated power system performance evaluation system (IPES), which is currently being developed in Brazil by several electrical utilities. Ke),words: Performance assessment; Expert systems; Operation monitoring; Reliability indices
1. Introduction
"When you can measure what you are speaking about and express it in numbers, you know something about it, and when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind." Lord Kelvin Striving for quality and enhancing efficiency seem to be hallmarks of the current era. Electricity, seen as a commodity, is not free from this influence. Therefore, electrical utilities all over the world are increasingly trying to incorporate these concepts in their routine activities [1]. In a broad sense, performance evaluation of almost any modern business or enterprise, such as that of power utilities, is a threefold complex problem comprising: (i) performance assessment of managerial aspects, (ii) economic performance and (iii) 'product' performance. Nevertheless, the sole availability of a strategy to evaluate a power utility's overall performance (i.e. measuring system) is not of very much help by itself. The disposal of a set of standards (i.e. criteria) is recognized as essential to allow a fair comparison between results, data and goals. Based on that comparison, sound decision making may be eventually attained [2]. In order to achieve part of this objective, a number of utilities in Brazil, led by the hold!ng company for the electric power sector (ELETROBRAS), launched a multiphase project referred to as IPES (integrated per0378-7796/95/$09.50 ~ 1995 Elsevier Science S.A. All rights reserved
S S D I 0378-7796(94)00907-L
formance evaluation system). Currently, 17 utilities are taking part in the IPES project but other utilities and universities are expected to join the effort in the near future. The project deals essentially with the 'product' performance, interpreted as the electrical performance assessment problem. This paper focuses on the main characteristics, objectives, concepts and features of the IPES project. The stage under current development is addressed and some preliminary practical results are presented and discussed.
2. IPES: an advanced expert system
As a general rule, almost every utility has its own supervisory system which operates as an aid to monitor the real-time system states, perform statistical analysis of data and assist in post-operational studies. Unfortunately, most of these systems are not totally comparable when different utilities are considered. Discrepancies arise as a result of diversity in prevailing operational practices in each utility, such as different techniques of data recording, (un)availability of measurement equipment, lack of standards, staff with non-homogeneous training levels, etc. Therefore, the task of monitoring a large national system composed of several utilities, as is the case in Brazil and other countries, demands special arrangements. Trying, for instance, to combine or integrate the already existing monitoring systems available in each utility does not necessarily render the best solution.
146
M. Th. Schilling, P. Gomes Electric Power Sv~lems Resear<'h 32 (1995) 145 151
MEASUREMENT ]
FUNCTION[
How to evaluate performance?
[ ~
[_J DIAGNOSlSI How to iudge
[
I
I I t~eres~"s? I ~, =
FUNCTION What acfiQns should be enforced?
This module also renders an overall conceptual appraisal of the system, ascribing to it qualifiers such as normal, alert, redundant, critical, unsafe, etc. The mathematical techniques and algorithms resorted to by the inference engine of this particular expert system encompass a wide spectrum of alternatives including the application of time series analysis, fuzzy logic, decision theory, pattern recognition, neural network techniques, advanced statistics, genetic algorithms, etc. Some of these are still in the developmental stage and are dependent on specifications related to the measurement module.
2.3. Management Junction Fig. 1. IPES: an advanced expert system.
Although the establishment of an ad hoc system to track the entire system poses a number of serious hurdles to be circumvented, it offers the opportunity for introducing new strategies that may eventually lead to better solutions. The application of artificial intelligence techniques seems to provide a feasible approach to tackle the complexities and intricacies of the bulk power system performance assessment problem. Accordingly, the IPES project is being envisaged as a major expert system [3], as depicted in Fig. 1, combining three other minor expert systems related to the following functions: (ii) measurement, (ii) diagnosis, (iii) management. Each of the three minor expert systems is to be developed independently, but during all stages of the project they are expected to be fully compatible with each other [4-7]. Each of the minor systems will have its own components (i.e. knowledge bases, inference engines and interfaces). The minor modules will be briefly discussed in the following subsections.
2. I. Measurement function The aim of this function is to obtain a comprehensive set of performance indices associated with both the preand post-operational time frames. Since this paper is mainly focused on this function, further details will be presented in Section 3.
2.2. Diagnosis function While the results given by the measurement module are essentially quantitative, those obtained from the diagnosis module have a qualitative nature. They judge the system conditions as expressed by the numerical values evaluated or estimated by the measurement module. After the successful completion of activities expected to be performed by this module, the analyst should be informed about several key aspects of the system behaviour such as the occurrence of operational constraint violations, adequacy levels, security margins, and the degree of discrepancies with regard to established criteria [8].
The aim of this module is to aid the decision-making process, providing the user with the best options with regard to possible corrective, preventive or predictive actions. A ranked list of alternatives should be produced, taking into account reliability and economic constraints. Depending upon the user's interests, several time frames may be selected, covering a broad spectrum of horizons such as real-time operations planning, expansion planning, etc. The knowledge base required by this expert system is rather complex and draws items from a large number of sources, among which the following may be listed: operational rules, data about system and equipment constraints, system behaviour forecasts, existing contracts between utilities, economic constraints, regulation rules, reliability targets (i.e. continuity, adequacy, security), maintenance policies, client satisfaction indicators, wheeling agreements, non-utility generator interaction rules, energy conservation strategies, guidelines for environmental protection, national security policies, etc. The interface of this module will also allow, under special circumstances, the user's direct influence in the decision-making procedures.
3. Key aspects of the measurement function Several utilities with different profiles (e.g. bulk transmission/generation, distribution, etc.) are cooperating with the IPES project, thus the necessity of establishing some consensual concepts and standard nomenclature was clearly recognized. Some of the key aspects are commented on in the following.
3.1. Temporal discrimination Power system performance assessment can be accomplished under two temporal stances: past and future. From the point of view of past performance, the temporal discrimination is related to the time period during which the system is under surveillance and data recording is active. There should be coherency between the dynamics or frequency of occurrence of the ob-
M.Th. Schill&g, P. Gomes / Electric Power Systems Research 32 (1995) 145 151
served phenomena and the kind of performance index evaluated. For instance, if the index gives the number of black-outs in a given system area, the corresponding temporal discrimination should cover a period of time compatible with the frequency of occurrence of this event. If that time is too short, the index will almost always be null. On the other hand, if it is too long, the system may suffer significant topological changes and the resulting indices will also be meaningless. Predictive performance assessment also requires strict compatibility between computational simulation times and the orders of magnitude of the time constants associated with the simulated physical phenomena. Since the predictive reliability analysis covers a broad time spectrum, ranging from real-time to expected future scenarios, in each case a skilful combination of analytical models and hypotheses is demanded. For instance, if the predictive analysis is intended to cover only a few hours in advance from the current instant, the analytical models utilized should disregard the influence of slow dynamic phenomena such as those associated with the system hydrological behaviour. While past performance indices are calculated based on the statistics of just one set of actual system measurements, future performance indices can be estimated deterministically or taking uncertainties into account. In the latter case, the predictive performance analysis also requires a precise matching between the analytical representation of physical phenomena and the dynamics of uncertainties. Therefore the measurement expert system is expected to entail a generalized uncertainties emulator [3]. This emulator should be interpreted as an advanced reliability processor comprising many features and designed for large-scale systems analysis. As depicted in Table 1, it will be able to operate in several 'modes' and offer a number of choices regarding representation of uncertainties. 3.2. Spatial discrimination Both in the post- as well as in the pre-operational horizons, the numerical computation of performance indices is highly dependent upon the concept of spatial discrimination. Regarding the post-operational time frame, this concept is commonly translated by the idea of entity levels (ELs). At one extreme, the highest entity level would be represented by the country's entire power network. At the other extreme, the lowest entity levels are associated with single consumers. In between, a varied range of convenient levels may be established encompassing, for instance, large geographical areas, utilities, states, counties, areas, specific parts of a network with a given voltage level, substations, feeders, etc. As many entity levels may be created as deemed convenient. In general, an entity level is established when its performance assessment is sought by the analyst.
147
Table 1 Generalized uncertainties emulator Mode A: predictive reliability (i) Uncertainties representation probabilities stochastic processes fuzzy variables interval variables no uncertainties (deterministic) (ii) Kind of study Static behaviour normal conditions energy sources reliability mode generation reliability mode transmission reliability mode substation reliability mode distribution interface mode composite reliability mode probabilistic load flow mode Static behaviour anomalous conditions probabilistic faults mode Dynamic behaviour probabilistic security mode Mode B: past performance evaluation Temporal aggregation Spatial aggregation Failure modes
Regarding the pre-operational time frame, the spatial discrimination concept is usually translated by the idea of hierarchical levels [9]. These levels provide a general view of the progression of converted energy from primary energy sources to the final customers. The hierarchical levels (HLs) considered are briefly described as follows. HL-O. The main concern at this fundamental level is to balance the energy availabilities and demands of the entire electric power system. Failures are due to energy deficits. Both energy production and transportation are neglected at this level. In some countries (e.g. Sweden, Russia, Brazil, Canada, China), HL-0 studies are strongly influenced by the prevailing hydrological patterns, while in many others the decisive influence stems from the availability of nuclear, fossil and non-conventional energy sources. HL-I. At this level, the main concern is to meet the system power demands by the generation capacity available in the system. This is evaluated by neglecting the network and pooling all sources of generation and all loads together. The main sources of unreliability at this level are due to peak load variations and generation outages. Sometimes interconnections are considered (multi-area studies). In this case a crude representation of intertransmission restrictions is also taken into account. HL-2. The full interaction between primary energy sources, generation, transmission and substations is modelled. HL-2 indices indicate the ability of the system to deliver the required energy to the major load
M.Th. Schill#7~,, P. Gomes
148
EL-I
COUNTRY (NATIONALGRID)
HL-O
EL-E
GEOGRAPHICAL REGIONS (INTERCONNECTED SYSTEM)
HL-1
EL-3
UTILITIES
HL-2
EL-4
ELECTRICALAREAS
EL-6
lflectric Power Sv~stems Restart~1 32 (1995) 145 151
SUBSTATIONS
EL-6
FEEDERS
EL-7
CONSUMERS
HL-3
Fig. 2. Spatial discrimination: H L = hierarchical level; E L = entity level.
points. This level is usually referred to as composite, overall, bulk or global system reliability and concerns much of the current efforts in research and development. The substations are an important part of HL-2 evaluation. HL-3. At this level the HL-2 problem is extended conceptually to incorporate the distribution system. HL-3 indices indicate the ability of the system to serve actual customers. The existing state-of-the-art techniques are not able to tackle the problem directly. Instead, the influence of HL-2 is evaluated separately and subsequently taken into account as input boundary conditions for the HL-3 problem. This approach is quite acceptable since the 'physical decoupling' between the higher hierarchy and the distribution system is relatively strong. Fig. 2 depicts a combination of the concepts of hierarchical and entity levels. It is seen, for instance, that HL-3, related to distribution systems, can be associated with entities such as single consumers or area feeders representing EL-7 and EL-6, respectively. On the other hand, HL-1 studies may, for instance, address a set of entities ranging from EL-1 to EL-5; in each case, a set of convenient hypotheses and analytical models has to be selected. It is emphasized, therefore, that the importance of attaining a consensus about the spatial discrimination of performance assessment studies appears mainly when forecast results (predictive reliability analysis) are tentatively compared with the post-operational behaviour of the system.
such as Pareto diagrams, control charts, scatter diagrams, etc. The kinds of failures considered are: (i) primary level of continuity (C), (ii) secondary level or adequacy (A), (iii) tertiary level or security (S). At the primary level, only continuity (or integrity) of supply is taken into account, regardless of any consideration of the degree of quality with which the load is supplied. At his level, the typical system conditions that may be identified are: (i) system is intact (continuous), (ii) system is not intact (failure mode). Practical examples of continuity failures are: partial system islanding, loss of interconnections, total loss of supply at specific buses, partial load interruption, etc. From the point of view of equipment, this failure mode is related to the equipment's ability to fulfil the intended mission. At the secondary level the main concern is supply quality or adequacy under (quasi) stationary or static conditions. The concept of power quality is not simple because different loads may have entirely different needs in terms of quality or because equipment under stress may be able to perform its intended function during a limited period of time [10]. The importance of this kind of failure mode has been confirmed in a recent survey [11] where 103 out of 130 utilities (79%) in the USA and Canada have demonstrated a high inerest in adequacy related events. Some of the most typical failures that are recognized at this level are: overloads, undervoltages, overvoltages, voltage distortion, uneconomical operating conditions, frequency excursions beyond accepted limits, etc. Finally, at the tertiary level the dynamic behaviour of the system (security) is of interest. A failure may be defined when the system operating point is such that loss of synchronism may occur due to any system variation or when the system enters a region where the voltage may suddenly collapse. Therefore, security indices give a measure of the system stability margin. 3.4. Measurement function overview Table 2 shows 28 key performance problems which are expected to be tackled when the measurement expert system is fully implemented. These problems arise from the combination of past and future time horizons, three distinct failure modes and a number of possibilities concerning spatial aggregation aspects. In the column addressing failure modes some specific problems are mentioned as examples. 3.5. Performance indices display
3.3. Failure modes One crucial aspect of performance assessment is dependent upon the definition of failure modes. In the IPES project, the cause of these failure modes will be monitored using product quality control techniques [2]
The 28 performance problems identified in Table 2 yield a plethora of numerical indices which are the quintessence of the measurement function. For instance, just the two problems represented by cells A6 and A9 in Table 2 have produced 39 performance
149
M. Th. Schilling, P. Gomes / Electric Power Systems Research 32 (/995) 145-151
Table 2 Twenty-eight key performance problems of the measurement function Type of evaluation
Spatial aggregation
Failure modes
Past performance
Predictive reliability
Systemic
H L-0 primary energy sources
Continuity energy availability Adequacy NUG's influence
Al
B1
A2
B2
H L- l generation
Continuity generation availability Adequacy efficient generation Security spinning reserve
A3
B3
A4
B4
A5
B5
Continuity power supply Adequacy electricity quality Security stability margin
A6
B6
A7
B7
A8
B8
Continuity power supply Adequacy wheeling
A9
B9
A l0
B 10
HL-2 substations
Continuity power supply
AIl
BI 1
HL-I/HL-3 subtransmission distribution
Continuity power supply Adequacy electricity quality
A12
BI2
A 13
B 13
Equipment, components, schemes
Continuity mission fulfilled
A 14
BI4
H L-2 generation and transmission
HL-2 interconnections
Specific
indices which are under test in the current stage of IPES. Similar sets of indices are also produced by the other 26 remaining cells [12]. In order to help the analyst in the task of dealing with such a massive amount of information, an interactive display will be available to present the indices obtained. As shown in Fig. 3, the user will access in each case the set of indices associated with the spatial and temporal discrimination of interest. PERFORMANCE INDICES
MON'n-I
. . . .
...
. . . . . . . . . . . . . . .
...........01. ... = ~ SPA TIAL • DISCRIMINAT/ON .
.
.
.
.
""
Fig. 3. IPES interactive display.
TEMPORAL DISCRIMINATION
4. Preliminary quantitative results The full computational implementation of all the functions depicted in Fig. 1, or even the computational treatment of all 28 cells shown in Table 2, is a formidable task. Therefore, it was decided to start the IPES computational development by attacking problems A6 and A9 of Table 2 simultaneously; these problems are associated with the measurement function. Work has also been initiated to tackle the diagnosis function. A few of the numerical results already attained with a computational prototype will be described here. Table 3 shows some of the continuity indices related to cell A6 of Table 2. They reflect the performance of the four maj,,, electrical utilities in Brazil ( E L E T R O S U L , FURNAS, C H E S F and E L E T R O N O R T E ) , which are responsible for bulk power generation and transmission. Owing to practical hurdles it was not possible, in this preliminary test, to cover the same observation period for all utilities.
M. 771. Schill#lg, P. Gomes
15(1
Electric Pcmer Systems Research 32 (I 995) 145 151
Table 3 Performance indices of the major utilities in Brazil Region
South/southeast
North/northeast
Utility
A
B
C
D
Observed period
1991
1991
2nd sem. 1992
2nd sem. 199 l
Reference values for one m o n t h ~'
Load peak (MW)
4809
15342
5109
2474
Average
Standard deviation
Frequency (1/period) Duration (h) Unavailability
21
13
48
28
I 0.3
14.5
2.1 2.4
13.8 15.7
38.3 87.3
7.4 16.9
8.1 109.1
16.6 223.5
342
109
2956
4520
366
675
4.3 11 0.58
0.4 2 0.17
34.7 91 2.78
109.6
8.5 49.5 0.26
11.4 34.3 0.29
(xlO 4) Energy not supplied (MWh) Severity (min) Fragility (%) Interruption costs
5.78
(10 6 US$) " The unavailability and fragility values are not referred to a specific time period.
For utilities A and B the temporal discrimination was the year, while for utilities C and D this discrimination was taken as a semester. In all cases the spatial aggregation was the utility itself. Utilities A and B, located in the Brazilian south/southeast region, are interconnected and responsible for the larger share of the country's electricity production. Utilities C and D are also interconnected. The following seven performance indices are presented in Table 3. The frequency of interruption indicates how many times the system has suffered any loss of load during the observation period. It gives no hint about the severity of an event. The duration of interruption gives the accumulated time, referred to the observation period, during which any amount of load was not served. The unavailability is a fraction of the observation period during which any amount of load was not served. The energy not supplied is an estimated value of the accumulated sum of energy not supplied due to any load interruption occurring in the system during the observation period. The severity is obtained by the ratio of the energy not supplied to the load peak value, expressed in minutes [12]. The fragility gives a measure of the network's topological weakness. Defining the term 'event' as any occurrence where a topological element (line, transformer) has been electrically disconnected, the fragility index F may be expressed by F=
total no. of events with loss of load total no. of events + total no. of events with loss of load without loss of load
(1)
The interruption costs reflect the monetary losses caused by a restriction in the energy supply suddenly surprising the user. The last two columns of Table 3 register a set of reference values which were obtained by pooling together the results of all 17 utilities. With the exception of the unavailability and fragility indices, all other values are referred to an observation period equivalent to one month. The large standard deviation reflects the broad spectrum of characteristics prevailing in the 17 utilities analysed. It is clearly seen that the overall performance of utilities C and D compares unfavourably with that of utilities A and B. All indices of A and B, when normalized to one month, are lower than the average values. On the other hand, only the frequency, duration and unavailability of C and D are lower (when normalized) than the reference average. The very great fragility of utility C is explained by the predominance of a highly radial structure in this utility.
5. Conclusions This paper has focused on some basic concepts and preliminary results of an integrated power system performance evaluation system (IPES) which is currently being implemented by several utilities in Brazil. The project started in 1990 and, up to the present, the prototype of the measurement module has been finalized and subjected to several tests. Meanwhile, research activities relating to the diagnosis module have also been initiated. Despite a variety of hurdles, the results already attained are encouraging enough to back the
M. Th. Schilling, P. Gomes /Electric Power Systems Research 32 (1995) 145 151
project's advancement and to foster its full implementation in the hopefully near future.
[5]
Acknowledgements [6]
The numerical results presented in this paper have been achieved due to the cooperation of many colleagues. The authors would like to thank all of them and especially Messrs G.P. Caldas (ELETROSUL), C.R. Zani (FURNAS), L.R. Lins (CHESF) and J.F. Lima Filho (ELETRONORTE).
[7]
[8] [9]
References [1] J.J. Hudiburg, Winning with Q u a l i t y - The Florida Power & Light Story, Quality Resources, New York, 1991. [2] A.C. Rosander, Application of Quality Control in the Service Industries, Marcel Dekker, New York, 1985. [3] D.S. Ramos, M.Th. Schilling, E.J. Robba and H.P. Schmidt, Uncertainties emulator: a new concept for power systems probabilistic studies, Proc. IEEE LAT1NCON, Santiago, Chile, 1992, pp. 146--150. [4] A.T. Holen et al., Expert systems in power system reliability assessment, Proc. 14th Inter-RAM: Int. Reliability, Availability
[10]
[11]
[12]
151
and Maintainability ConJ~ jbr the Electric Power Industry, Toronto, Canada, 1987, pp. 413 42(I. P. Nitu and T.Y. Wong, An interactive computer program for area reliability evaluation, Proc. 17th Inter-RA M: h+t. Reliability, Availability and Maintainability C(mll lbr the Electrie Power Industry, Hershey, PA, USA, 1990, pp. 131 135. M. Akimoto, T+ Michigami, Y. Kono and H. Suzuki, Development of an expert system for operation planning of bulk power system, C1GRE Proc., 34th Session, Paris, France, 1992, Vol. 2, CIGRE, Paris, 1993, Paper No. 38-103. Y. Luo, W. Deng, Q. Gu and Z, Liu, An expert system for analysing the state and developing trends of a controlled power system. Eleetr. Technol. (UK), (1)(1992) 87 95. G.E. Gonzalez-Urdaneta, Reliability criteria used in South America, IEEE Con[] Power Technoh~gy, Athens, Greece, 1993. A.M. Leite da Silva, M.V.F. Pereira and M.Th. Schilling, Power systems analysis under uncertainties concepts and techniques, 2nd Syrup. Specialists in Electric Operational and Expansion Planning (SEPOPE), S(to Paulo, Brazil, 1989, Invited Paper 1P-21. J.J. Burke, D.C. Griffith and D.J. Ward, Power quality two different perspectives, IEEE PES Winter Meeting, Atlanta, GA, USA, 1990, Paper No. 90 WM 053-9 PWRD. W.R. Prince, E.K. Nielsen and H.D. McNair, A survey of current operational problems, IEEE Trans. Power Svst., 4 (1989) 1492- 1498. W.H. Winter and B.K. LeReverend, Bulk electricity system operational performance: measurement system and survey results, CIGRE Working Group Rep. No. 39.05, CIGRE. Paris, 1989.
Electric Power Systems Research 32 (1995) 145-151
An approach to bulk power system performance assessment M.Th. Schilling, P. Gomes UFF-('AA 'ELETROBR,4S, Division of Technological Development of the Electric Operation (DOLT), Avenida Marechal Floriano 19, 20080-003 Rio de Janeiro, R J, Brazil Received 10 October 1994
Abstract
This paper focuses on the main characteristics, basic concepts and preliminary quantitative results of an integrated power system performance evaluation system (IPES), which is currently being developed in Brazil by several electrical utilities. Ke),words: Performance assessment; Expert systems; Operation monitoring; Reliability indices
1. Introduction
"When you can measure what you are speaking about and express it in numbers, you know something about it, and when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind." Lord Kelvin Striving for quality and enhancing efficiency seem to be hallmarks of the current era. Electricity, seen as a commodity, is not free from this influence. Therefore, electrical utilities all over the world are increasingly trying to incorporate these concepts in their routine activities [1]. In a broad sense, performance evaluation of almost any modern business or enterprise, such as that of power utilities, is a threefold complex problem comprising: (i) performance assessment of managerial aspects, (ii) economic performance and (iii) 'product' performance. Nevertheless, the sole availability of a strategy to evaluate a power utility's overall performance (i.e. measuring system) is not of very much help by itself. The disposal of a set of standards (i.e. criteria) is recognized as essential to allow a fair comparison between results, data and goals. Based on that comparison, sound decision making may be eventually attained [2]. In order to achieve part of this objective, a number of utilities in Brazil, led by the hold!ng company for the electric power sector (ELETROBRAS), launched a multiphase project referred to as IPES (integrated per0378-7796/95/$09.50 ~ 1995 Elsevier Science S.A. All rights reserved
S S D I 0378-7796(94)00907-L
formance evaluation system). Currently, 17 utilities are taking part in the IPES project but other utilities and universities are expected to join the effort in the near future. The project deals essentially with the 'product' performance, interpreted as the electrical performance assessment problem. This paper focuses on the main characteristics, objectives, concepts and features of the IPES project. The stage under current development is addressed and some preliminary practical results are presented and discussed.
2. IPES: an advanced expert system
As a general rule, almost every utility has its own supervisory system which operates as an aid to monitor the real-time system states, perform statistical analysis of data and assist in post-operational studies. Unfortunately, most of these systems are not totally comparable when different utilities are considered. Discrepancies arise as a result of diversity in prevailing operational practices in each utility, such as different techniques of data recording, (un)availability of measurement equipment, lack of standards, staff with non-homogeneous training levels, etc. Therefore, the task of monitoring a large national system composed of several utilities, as is the case in Brazil and other countries, demands special arrangements. Trying, for instance, to combine or integrate the already existing monitoring systems available in each utility does not necessarily render the best solution.
146
M. Th. Schilling, P. Gomes Electric Power Sv~lems Resear<'h 32 (1995) 145 151
MEASUREMENT ]
FUNCTION[
How to evaluate performance?
[ ~
[_J DIAGNOSlSI How to iudge
[
I
I I t~eres~"s? I ~, =
FUNCTION What acfiQns should be enforced?
This module also renders an overall conceptual appraisal of the system, ascribing to it qualifiers such as normal, alert, redundant, critical, unsafe, etc. The mathematical techniques and algorithms resorted to by the inference engine of this particular expert system encompass a wide spectrum of alternatives including the application of time series analysis, fuzzy logic, decision theory, pattern recognition, neural network techniques, advanced statistics, genetic algorithms, etc. Some of these are still in the developmental stage and are dependent on specifications related to the measurement module.
2.3. Management Junction Fig. 1. IPES: an advanced expert system.
Although the establishment of an ad hoc system to track the entire system poses a number of serious hurdles to be circumvented, it offers the opportunity for introducing new strategies that may eventually lead to better solutions. The application of artificial intelligence techniques seems to provide a feasible approach to tackle the complexities and intricacies of the bulk power system performance assessment problem. Accordingly, the IPES project is being envisaged as a major expert system [3], as depicted in Fig. 1, combining three other minor expert systems related to the following functions: (ii) measurement, (ii) diagnosis, (iii) management. Each of the three minor expert systems is to be developed independently, but during all stages of the project they are expected to be fully compatible with each other [4-7]. Each of the minor systems will have its own components (i.e. knowledge bases, inference engines and interfaces). The minor modules will be briefly discussed in the following subsections.
2. I. Measurement function The aim of this function is to obtain a comprehensive set of performance indices associated with both the preand post-operational time frames. Since this paper is mainly focused on this function, further details will be presented in Section 3.
2.2. Diagnosis function While the results given by the measurement module are essentially quantitative, those obtained from the diagnosis module have a qualitative nature. They judge the system conditions as expressed by the numerical values evaluated or estimated by the measurement module. After the successful completion of activities expected to be performed by this module, the analyst should be informed about several key aspects of the system behaviour such as the occurrence of operational constraint violations, adequacy levels, security margins, and the degree of discrepancies with regard to established criteria [8].
The aim of this module is to aid the decision-making process, providing the user with the best options with regard to possible corrective, preventive or predictive actions. A ranked list of alternatives should be produced, taking into account reliability and economic constraints. Depending upon the user's interests, several time frames may be selected, covering a broad spectrum of horizons such as real-time operations planning, expansion planning, etc. The knowledge base required by this expert system is rather complex and draws items from a large number of sources, among which the following may be listed: operational rules, data about system and equipment constraints, system behaviour forecasts, existing contracts between utilities, economic constraints, regulation rules, reliability targets (i.e. continuity, adequacy, security), maintenance policies, client satisfaction indicators, wheeling agreements, non-utility generator interaction rules, energy conservation strategies, guidelines for environmental protection, national security policies, etc. The interface of this module will also allow, under special circumstances, the user's direct influence in the decision-making procedures.
3. Key aspects of the measurement function Several utilities with different profiles (e.g. bulk transmission/generation, distribution, etc.) are cooperating with the IPES project, thus the necessity of establishing some consensual concepts and standard nomenclature was clearly recognized. Some of the key aspects are commented on in the following.
3.1. Temporal discrimination Power system performance assessment can be accomplished under two temporal stances: past and future. From the point of view of past performance, the temporal discrimination is related to the time period during which the system is under surveillance and data recording is active. There should be coherency between the dynamics or frequency of occurrence of the ob-
M.Th. Schill&g, P. Gomes / Electric Power Systems Research 32 (1995) 145 151
served phenomena and the kind of performance index evaluated. For instance, if the index gives the number of black-outs in a given system area, the corresponding temporal discrimination should cover a period of time compatible with the frequency of occurrence of this event. If that time is too short, the index will almost always be null. On the other hand, if it is too long, the system may suffer significant topological changes and the resulting indices will also be meaningless. Predictive performance assessment also requires strict compatibility between computational simulation times and the orders of magnitude of the time constants associated with the simulated physical phenomena. Since the predictive reliability analysis covers a broad time spectrum, ranging from real-time to expected future scenarios, in each case a skilful combination of analytical models and hypotheses is demanded. For instance, if the predictive analysis is intended to cover only a few hours in advance from the current instant, the analytical models utilized should disregard the influence of slow dynamic phenomena such as those associated with the system hydrological behaviour. While past performance indices are calculated based on the statistics of just one set of actual system measurements, future performance indices can be estimated deterministically or taking uncertainties into account. In the latter case, the predictive performance analysis also requires a precise matching between the analytical representation of physical phenomena and the dynamics of uncertainties. Therefore the measurement expert system is expected to entail a generalized uncertainties emulator [3]. This emulator should be interpreted as an advanced reliability processor comprising many features and designed for large-scale systems analysis. As depicted in Table 1, it will be able to operate in several 'modes' and offer a number of choices regarding representation of uncertainties. 3.2. Spatial discrimination Both in the post- as well as in the pre-operational horizons, the numerical computation of performance indices is highly dependent upon the concept of spatial discrimination. Regarding the post-operational time frame, this concept is commonly translated by the idea of entity levels (ELs). At one extreme, the highest entity level would be represented by the country's entire power network. At the other extreme, the lowest entity levels are associated with single consumers. In between, a varied range of convenient levels may be established encompassing, for instance, large geographical areas, utilities, states, counties, areas, specific parts of a network with a given voltage level, substations, feeders, etc. As many entity levels may be created as deemed convenient. In general, an entity level is established when its performance assessment is sought by the analyst.
147
Table 1 Generalized uncertainties emulator Mode A: predictive reliability (i) Uncertainties representation probabilities stochastic processes fuzzy variables interval variables no uncertainties (deterministic) (ii) Kind of study Static behaviour normal conditions energy sources reliability mode generation reliability mode transmission reliability mode substation reliability mode distribution interface mode composite reliability mode probabilistic load flow mode Static behaviour anomalous conditions probabilistic faults mode Dynamic behaviour probabilistic security mode Mode B: past performance evaluation Temporal aggregation Spatial aggregation Failure modes
Regarding the pre-operational time frame, the spatial discrimination concept is usually translated by the idea of hierarchical levels [9]. These levels provide a general view of the progression of converted energy from primary energy sources to the final customers. The hierarchical levels (HLs) considered are briefly described as follows. HL-O. The main concern at this fundamental level is to balance the energy availabilities and demands of the entire electric power system. Failures are due to energy deficits. Both energy production and transportation are neglected at this level. In some countries (e.g. Sweden, Russia, Brazil, Canada, China), HL-0 studies are strongly influenced by the prevailing hydrological patterns, while in many others the decisive influence stems from the availability of nuclear, fossil and non-conventional energy sources. HL-I. At this level, the main concern is to meet the system power demands by the generation capacity available in the system. This is evaluated by neglecting the network and pooling all sources of generation and all loads together. The main sources of unreliability at this level are due to peak load variations and generation outages. Sometimes interconnections are considered (multi-area studies). In this case a crude representation of intertransmission restrictions is also taken into account. HL-2. The full interaction between primary energy sources, generation, transmission and substations is modelled. HL-2 indices indicate the ability of the system to deliver the required energy to the major load
M.Th. Schill#7~,, P. Gomes
148
EL-I
COUNTRY (NATIONALGRID)
HL-O
EL-E
GEOGRAPHICAL REGIONS (INTERCONNECTED SYSTEM)
HL-1
EL-3
UTILITIES
HL-2
EL-4
ELECTRICALAREAS
EL-6
lflectric Power Sv~stems Restart~1 32 (1995) 145 151
SUBSTATIONS
EL-6
FEEDERS
EL-7
CONSUMERS
HL-3
Fig. 2. Spatial discrimination: H L = hierarchical level; E L = entity level.
points. This level is usually referred to as composite, overall, bulk or global system reliability and concerns much of the current efforts in research and development. The substations are an important part of HL-2 evaluation. HL-3. At this level the HL-2 problem is extended conceptually to incorporate the distribution system. HL-3 indices indicate the ability of the system to serve actual customers. The existing state-of-the-art techniques are not able to tackle the problem directly. Instead, the influence of HL-2 is evaluated separately and subsequently taken into account as input boundary conditions for the HL-3 problem. This approach is quite acceptable since the 'physical decoupling' between the higher hierarchy and the distribution system is relatively strong. Fig. 2 depicts a combination of the concepts of hierarchical and entity levels. It is seen, for instance, that HL-3, related to distribution systems, can be associated with entities such as single consumers or area feeders representing EL-7 and EL-6, respectively. On the other hand, HL-1 studies may, for instance, address a set of entities ranging from EL-1 to EL-5; in each case, a set of convenient hypotheses and analytical models has to be selected. It is emphasized, therefore, that the importance of attaining a consensus about the spatial discrimination of performance assessment studies appears mainly when forecast results (predictive reliability analysis) are tentatively compared with the post-operational behaviour of the system.
such as Pareto diagrams, control charts, scatter diagrams, etc. The kinds of failures considered are: (i) primary level of continuity (C), (ii) secondary level or adequacy (A), (iii) tertiary level or security (S). At the primary level, only continuity (or integrity) of supply is taken into account, regardless of any consideration of the degree of quality with which the load is supplied. At his level, the typical system conditions that may be identified are: (i) system is intact (continuous), (ii) system is not intact (failure mode). Practical examples of continuity failures are: partial system islanding, loss of interconnections, total loss of supply at specific buses, partial load interruption, etc. From the point of view of equipment, this failure mode is related to the equipment's ability to fulfil the intended mission. At the secondary level the main concern is supply quality or adequacy under (quasi) stationary or static conditions. The concept of power quality is not simple because different loads may have entirely different needs in terms of quality or because equipment under stress may be able to perform its intended function during a limited period of time [10]. The importance of this kind of failure mode has been confirmed in a recent survey [11] where 103 out of 130 utilities (79%) in the USA and Canada have demonstrated a high inerest in adequacy related events. Some of the most typical failures that are recognized at this level are: overloads, undervoltages, overvoltages, voltage distortion, uneconomical operating conditions, frequency excursions beyond accepted limits, etc. Finally, at the tertiary level the dynamic behaviour of the system (security) is of interest. A failure may be defined when the system operating point is such that loss of synchronism may occur due to any system variation or when the system enters a region where the voltage may suddenly collapse. Therefore, security indices give a measure of the system stability margin. 3.4. Measurement function overview Table 2 shows 28 key performance problems which are expected to be tackled when the measurement expert system is fully implemented. These problems arise from the combination of past and future time horizons, three distinct failure modes and a number of possibilities concerning spatial aggregation aspects. In the column addressing failure modes some specific problems are mentioned as examples. 3.5. Performance indices display
3.3. Failure modes One crucial aspect of performance assessment is dependent upon the definition of failure modes. In the IPES project, the cause of these failure modes will be monitored using product quality control techniques [2]
The 28 performance problems identified in Table 2 yield a plethora of numerical indices which are the quintessence of the measurement function. For instance, just the two problems represented by cells A6 and A9 in Table 2 have produced 39 performance
149
M. Th. Schilling, P. Gomes / Electric Power Systems Research 32 (/995) 145-151
Table 2 Twenty-eight key performance problems of the measurement function Type of evaluation
Spatial aggregation
Failure modes
Past performance
Predictive reliability
Systemic
H L-0 primary energy sources
Continuity energy availability Adequacy NUG's influence
Al
B1
A2
B2
H L- l generation
Continuity generation availability Adequacy efficient generation Security spinning reserve
A3
B3
A4
B4
A5
B5
Continuity power supply Adequacy electricity quality Security stability margin
A6
B6
A7
B7
A8
B8
Continuity power supply Adequacy wheeling
A9
B9
A l0
B 10
HL-2 substations
Continuity power supply
AIl
BI 1
HL-I/HL-3 subtransmission distribution
Continuity power supply Adequacy electricity quality
A12
BI2
A 13
B 13
Equipment, components, schemes
Continuity mission fulfilled
A 14
BI4
H L-2 generation and transmission
HL-2 interconnections
Specific
indices which are under test in the current stage of IPES. Similar sets of indices are also produced by the other 26 remaining cells [12]. In order to help the analyst in the task of dealing with such a massive amount of information, an interactive display will be available to present the indices obtained. As shown in Fig. 3, the user will access in each case the set of indices associated with the spatial and temporal discrimination of interest. PERFORMANCE INDICES
MON'n-I
. . . .
...
. . . . . . . . . . . . . . .
...........01. ... = ~ SPA TIAL • DISCRIMINAT/ON .
.
.
.
.
""
Fig. 3. IPES interactive display.
TEMPORAL DISCRIMINATION
4. Preliminary quantitative results The full computational implementation of all the functions depicted in Fig. 1, or even the computational treatment of all 28 cells shown in Table 2, is a formidable task. Therefore, it was decided to start the IPES computational development by attacking problems A6 and A9 of Table 2 simultaneously; these problems are associated with the measurement function. Work has also been initiated to tackle the diagnosis function. A few of the numerical results already attained with a computational prototype will be described here. Table 3 shows some of the continuity indices related to cell A6 of Table 2. They reflect the performance of the four maj,,, electrical utilities in Brazil ( E L E T R O S U L , FURNAS, C H E S F and E L E T R O N O R T E ) , which are responsible for bulk power generation and transmission. Owing to practical hurdles it was not possible, in this preliminary test, to cover the same observation period for all utilities.
M. 771. Schill#lg, P. Gomes
15(1
Electric Pcmer Systems Research 32 (I 995) 145 151
Table 3 Performance indices of the major utilities in Brazil Region
South/southeast
North/northeast
Utility
A
B
C
D
Observed period
1991
1991
2nd sem. 1992
2nd sem. 199 l
Reference values for one m o n t h ~'
Load peak (MW)
4809
15342
5109
2474
Average
Standard deviation
Frequency (1/period) Duration (h) Unavailability
21
13
48
28
I 0.3
14.5
2.1 2.4
13.8 15.7
38.3 87.3
7.4 16.9
8.1 109.1
16.6 223.5
342
109
2956
4520
366
675
4.3 11 0.58
0.4 2 0.17
34.7 91 2.78
109.6
8.5 49.5 0.26
11.4 34.3 0.29
(xlO 4) Energy not supplied (MWh) Severity (min) Fragility (%) Interruption costs
5.78
(10 6 US$) " The unavailability and fragility values are not referred to a specific time period.
For utilities A and B the temporal discrimination was the year, while for utilities C and D this discrimination was taken as a semester. In all cases the spatial aggregation was the utility itself. Utilities A and B, located in the Brazilian south/southeast region, are interconnected and responsible for the larger share of the country's electricity production. Utilities C and D are also interconnected. The following seven performance indices are presented in Table 3. The frequency of interruption indicates how many times the system has suffered any loss of load during the observation period. It gives no hint about the severity of an event. The duration of interruption gives the accumulated time, referred to the observation period, during which any amount of load was not served. The unavailability is a fraction of the observation period during which any amount of load was not served. The energy not supplied is an estimated value of the accumulated sum of energy not supplied due to any load interruption occurring in the system during the observation period. The severity is obtained by the ratio of the energy not supplied to the load peak value, expressed in minutes [12]. The fragility gives a measure of the network's topological weakness. Defining the term 'event' as any occurrence where a topological element (line, transformer) has been electrically disconnected, the fragility index F may be expressed by F=
total no. of events with loss of load total no. of events + total no. of events with loss of load without loss of load
(1)
The interruption costs reflect the monetary losses caused by a restriction in the energy supply suddenly surprising the user. The last two columns of Table 3 register a set of reference values which were obtained by pooling together the results of all 17 utilities. With the exception of the unavailability and fragility indices, all other values are referred to an observation period equivalent to one month. The large standard deviation reflects the broad spectrum of characteristics prevailing in the 17 utilities analysed. It is clearly seen that the overall performance of utilities C and D compares unfavourably with that of utilities A and B. All indices of A and B, when normalized to one month, are lower than the average values. On the other hand, only the frequency, duration and unavailability of C and D are lower (when normalized) than the reference average. The very great fragility of utility C is explained by the predominance of a highly radial structure in this utility.
5. Conclusions This paper has focused on some basic concepts and preliminary results of an integrated power system performance evaluation system (IPES) which is currently being implemented by several utilities in Brazil. The project started in 1990 and, up to the present, the prototype of the measurement module has been finalized and subjected to several tests. Meanwhile, research activities relating to the diagnosis module have also been initiated. Despite a variety of hurdles, the results already attained are encouraging enough to back the
M. Th. Schilling, P. Gomes /Electric Power Systems Research 32 (1995) 145 151
project's advancement and to foster its full implementation in the hopefully near future.
[5]
Acknowledgements [6]
The numerical results presented in this paper have been achieved due to the cooperation of many colleagues. The authors would like to thank all of them and especially Messrs G.P. Caldas (ELETROSUL), C.R. Zani (FURNAS), L.R. Lins (CHESF) and J.F. Lima Filho (ELETRONORTE).
[7]
[8] [9]
References [1] J.J. Hudiburg, Winning with Q u a l i t y - The Florida Power & Light Story, Quality Resources, New York, 1991. [2] A.C. Rosander, Application of Quality Control in the Service Industries, Marcel Dekker, New York, 1985. [3] D.S. Ramos, M.Th. Schilling, E.J. Robba and H.P. Schmidt, Uncertainties emulator: a new concept for power systems probabilistic studies, Proc. IEEE LAT1NCON, Santiago, Chile, 1992, pp. 146--150. [4] A.T. Holen et al., Expert systems in power system reliability assessment, Proc. 14th Inter-RAM: Int. Reliability, Availability
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and Maintainability ConJ~ jbr the Electric Power Industry, Toronto, Canada, 1987, pp. 413 42(I. P. Nitu and T.Y. Wong, An interactive computer program for area reliability evaluation, Proc. 17th Inter-RA M: h+t. Reliability, Availability and Maintainability C(mll lbr the Electrie Power Industry, Hershey, PA, USA, 1990, pp. 131 135. M. Akimoto, T+ Michigami, Y. Kono and H. Suzuki, Development of an expert system for operation planning of bulk power system, C1GRE Proc., 34th Session, Paris, France, 1992, Vol. 2, CIGRE, Paris, 1993, Paper No. 38-103. Y. Luo, W. Deng, Q. Gu and Z, Liu, An expert system for analysing the state and developing trends of a controlled power system. Eleetr. Technol. (UK), (1)(1992) 87 95. G.E. Gonzalez-Urdaneta, Reliability criteria used in South America, IEEE Con[] Power Technoh~gy, Athens, Greece, 1993. A.M. Leite da Silva, M.V.F. Pereira and M.Th. Schilling, Power systems analysis under uncertainties concepts and techniques, 2nd Syrup. Specialists in Electric Operational and Expansion Planning (SEPOPE), S(to Paulo, Brazil, 1989, Invited Paper 1P-21. J.J. Burke, D.C. Griffith and D.J. Ward, Power quality two different perspectives, IEEE PES Winter Meeting, Atlanta, GA, USA, 1990, Paper No. 90 WM 053-9 PWRD. W.R. Prince, E.K. Nielsen and H.D. McNair, A survey of current operational problems, IEEE Trans. Power Svst., 4 (1989) 1492- 1498. W.H. Winter and B.K. LeReverend, Bulk electricity system operational performance: measurement system and survey results, CIGRE Working Group Rep. No. 39.05, CIGRE. Paris, 1989.