The N-1 Criterion

The traditional power system planning method used to assure reliability of design at the sub-transmission – substation level is the N-1 criterion. In its purest form, it states that a power system must be able to operate and fully meet expectations for amount (kW) and quality of power (voltage, power factor, etc.) even if any one of its major components is out of service (a single contingency). The system has N components, hence the name N–1.

This criterion makes a lot of sense. Unexpected equipment failures happen. Expected equipment outages (maintenance) are a fact of life. A prudent approach to design reliability should include making certain that the system can perform to its most stressful required level (i.e., serve the maximum demand, the peak load) even if a failure or maintenance outage has occurred. Just how much reliability this assures depends on a number of factors that will be addressed later in this section and the fact that it does not always assure reliability is the major topic of this chapter. However, from the outset it seems clear that this criterion sets a necessary requirement for any power system this is expected to provide reliable power supply.

Application of the Basic Contingency Planning Concept

Typically, this concept is applied as a criterion in the planning and engineering of the transmission/sub-transmission/substation portion of an electric distribution (T&D) system – the portion from 230 kV down to 69 or possibly 34.5 kV. At most electric distribution utilities, it is not applied to the distribution feeder system. Instead, techniques and analytical planning methods devised for application to radial power flow systems are used (see Section 9.3).

Application of N–1 using a Computer Program

As originally conceived, prior to the existence of really powerful digital computers for power system studies, this process was done with an analog computer. Each case was set up and studied on an individual basis by the utility’s power system planners, by adjusting settings and patch-cord connections on the analog computer. For this reason, initially (1950s) often only the 100 or so most important components (out of several thousand in a large power system) could be studied for contingency outage. However, beginning in the late 1960s, programs on digital computers were developed which would automatically check all single contingencies in the system [Daniels, 1967]. These programs became a staple of utility for system planning.

A modern contingency analysis program works along the lines shown in Figure 9.1. It is built around a load-flow program – a digital program that takes a data set describing the power system and the loads it is to serve, and solves a set of simultaneous equations to determine the voltages, flows, and power factors that can be expected in that system. In an automatic contingency analysis program, the basic load flow program is augmented with an outer loop, which automatically cycles through this power system data set, removing each unit of equipment and line, in turn, and solving the load flow analysis for that particularly contingency case.

For each such case, the program checks the results of that contingency case and reports any loadings or voltages that are out of acceptable range. It then “restores” that outaged component in the database, removes the next unit in turn, and runs that contingency case, cycling through all components in the system.

Figure 9.1 Basic approach behind the N–1 contingency planning approach. Engineering studies cycle through all components of the system and outage each one, studying what loadings and voltages would result. The system is considered to meet “N–1” criterion when all such contingency cases result in no out-of-range loadings or voltages.

A system plan was considered acceptable when this type of evaluation showed that no voltage and loading standards violations would occur in every one of these instances of single contingencies. Figure 9.1 illustrates this basic approach.

A Successful Method in the Mid and Late 20th Century

From the early 1960s through the early 1990s, the vast majority of different electric utilities applied this basic approach, with a number of slight variations here and there. Typically, a utility would design its system to completely meet N–1 criterion (the system can perform despite the loss of any one component) and to meet certain N-2 criteria (a set of specific two-failure conditions, which are thought to be critical enough to engineer to tolerate). Systems designed using this approach produced satisfactory performance at costs not considered unreasonable.

Supposedly Good Systems Begin Giving Bad Results

In the late 20th and early 21st century the operating record of large electric utilities in the United States revealed increasing problems in maintaining reliability of service to their customers. During the peak periods in the summers of 1998 and 1999 a number of power systems that fully met the traditional N–1 criterion experienced widespread outages of their power delivery systems. Table

9.2 lists only some of the more significant events in 1999, as identified by the U.S. Department of Energy. In particular, the ComEd system’s (Chicago) inability to provide service was disturbing, because that system was designed to N–1 levels of

Table 9.2 Major System Outages Identified By US DOE in 1999

Contingency standards and even met an N–2 and N–3 criterion in some places. There were a sufficient number of these events to make it clear that ComEd was not an isolated or atypical situation [U.S. DOE, 2000].

Perhaps more significantly, the level of reliability-related problems on U.S. systems had been growing for several years prior to 1999. Based on analysis of industry survey data, the authors first noticed the trend in 1994. While at that time there were few widespread customer outage events, throughout the 1990s there was a growing “background noise level” of operating emergencies, equipment overloads, and frequent if small customer interruptions on many utility systems. Even without the major events cataloged in the DOE report (Table 9.2) the industry’s record of customer service quality was falling in many regards prior to 1999.

Overall Summary of the Problem

N–1 methods and the N–1 criteria assure power system planners and engineers that there is some feasible way to back up every unit in the system, should it fail. However, they make no assessment of the following:

• How likely is it that such backup will be needed?

• How reasonable is the feasible plan for each contingency situation or is the planner actually building a “house of cards” by expected “too many things to go right” once one thing has gone wrong?

• How much stress might the system be under during such contingency situations, and the long-term implications for both equipment life and operating policy?

• How often will conditions occur which cannot be backed up (e.g., multiple failures) and how bad could the situation become when that is the case?

As a result, systems that meet the N–1 criteria may be far less reliable than needed, even though the N–1 criteria “guarantees” there is a way to back up every unit in the system. This is much more likely to happen in modern power systems than it was in traditional, regulated power systems, due to changes in utilization and design made in the period 1990–2000, and the more volatile operating environment of deregulation.

Utilization Ratio Sensitivity

The major culprit that led to problems that “N–1 could not see” was an increase in the typical equipment utilization ratio used throughout the industry. When it is raised, as it was during the 1980s and 1990s, an N–1 compliant system, which previously gave good service, may no longer give satisfactory reliability of service, even if it continues to meet the N–1 criterion. Similarly, power systems designed using the N–1 criterion and intended to operate at higher than traditional levels of equipment loading are very likely to have higher customer interruption rates than expected.

There is nothing inherently wrong with this trend to higher loading levels. In fact it is desirable because it seeks to make the utility financially efficient, which is potentially beneficial to both stockholders and customers. A power system that operates at 83% or 90% or even 100% utilization of equipment at peak can be designed to operate reliably, but something beyond N–1 methodology is required to assure that it will provide good customer service reliability.

Looking at N–1’s Limitations

In order to understand where the problem with N–1 criterion application lies, it is important to first understand that a power system that meets the N–1 criterion can and routinely does operate with more than one unit of equipment out of service. Consider a power system that has 10,000 elements in it, each with an outage expectation of .16% – a value lower than one would ever expect on a real power system. One can expect that on average, about 16 elements will be out at any one time. Yet the system will usually continue to operate without problems. The reason is that the N–1 criteria has guaranteed that there is a backup for every one of these failed units, as shown in Figure 9.3.

The system will fail to serve its entire load only if two of these multiple outages occur among neighboring equipment. For example, if a transformer and the transformer designated to back it up both fail at the same time, as shown in Figure 9.4, then, and only then, will a customer service interruption occur.

High Utilization Coupled with Aging System Equipment Leads to Greatly Increased Service Problems

Chapter 7 discussed the effect that aging has on equipment failure rates. In aging areas of a power system, the failure rate for equipment is three to five times that of normal areas of the system. Coupled with the high utilization rates common in these aging areas and the result is a ten to one or slightly worse incidence of customer service interruptions due to equipment outages.²

² Here the authors will bring in a real world factor. Chapter 8 discussed why utilization rates are usually above system average in aging infrastructure areas of the system. A system where the average has gone from 66% to 88.5% may have seen only a modest increase in the utilization rate for equipment in newer areas of the system, while increases in aging areas make up for those below-average statistics. Thus, the aging part of the system has much higher utilization than other parts. Its customer service problems stand out both because of the higher failure rate in this area, and the higher likelihood that outages lead to customer interruptions. As a result aging areas often have a customer interruption rate up to twelve times that of newer areas of the system.

Increased High-Stress Levels and Periods

The situation is slightly worse than the perspective developed above when one looks at the stress put on the system’s equipment, and the portion of the year that the system is likely to see: high- and medium-stress events due to equipment outages.

In a 66% utilization system, every transformer is paired with one other: whenever the unit it is backing up fails, it must support the load of two transformers. Given the transformer failure rate of .0025%, this means each transformer can expect to have its partner out of service and thus be in this “contingency support mode” about 22 hours (.0025 x 8760 hours) per year. Given that the system is at peak demand about 10% of the time, this means that a transformer can expect about two hours of severe loading time per year.

When utilization ratio is 88.5%, each transformer is partnered with two other units: failure of either one will put it in a contingency support mode. Thus, neglecting the slight amount of time when both its partners are out of service (and thus customer service in interrupted), the amount of time it can expect to be in this mode is twice what it was in the 66.5% system: or about 44 hours, 4.4 of that will be high stress. Similarly, in 100% utilization, each transformer will see about 66 and 6.6 contingency support and high-stress hours period year. Stress put on system equipment is much higher in high utilization systems.

“Low standards” Operating Hours are Increased

It is also worth considering that standards on loading, voltage regulation, and other operating factors are relaxed during contingency situations (see Section 9.2). Since the amount of time that the system spends in these “medium stress times” is greater in high utilization systems, which means that the distribution system spends more time in “sub-standard” situations – twice is too much if the utilization rate is 88.5%, three times as much if utilization is 100%.

The Result: Lack of Dependability as Sole Planning Tools

The limitations discussed above can be partly accommodated by modifications to the traditional approaches and changes in N–1 criteria application. But the overall result is that resource requirements (both human and computer) rise dramatically, and the methods become both unwieldy and more sensitive to assumptions and other limitations not covered here. The bottom line is that N–1 and N–2 contingency-enumeration methods were, and still are, sound engineering methods, but ones with a high sensitivity to planning and operating conditions that are more common today than in the mid-1960s when these methods came into prominence as design tools. These limitations reduce the dependability of N–1 analysis, and the use of N–1 as a reliability criterion, as a definition of design sufficiency in power system reliability engineering.

Figure 9.7 Contingency-based analysis (solid lines) determined that a particular power system could sustain the required peak load (10,350 MW) while meeting N–1 everywhere and N–2 criteria in selected places. This means it met roughly a 30 minute-SAIDI capability at the low side bus level of the power delivery system.

Figure 9.3 illustrates this with an example taken from a large utility system in the Midwestern U.S. Traditional N–1 analysis determines that a power system operating at an average 83% utilization factor can serve a certain peak load level while meeting N–1 criteria (defined as sufficient reliability). Basically, the “rectangular” profile on the diagram given by N–1 analysis indicated that the system passed N–1 criteria everywhere, and N–2 criteria at a set of selected critical points, while the demand was set to that of projected design levels for system peak load.³

Actual capability of the system shows a rounded corner to the load versus reliability of service capabilities of the system. It is capable of delivering only 9,850 MW with the required 30-minute SAIDI. If expected to serve a peak load of 10,350 MW, it has an expected SAIDI of four times the target, 120 minutes per year.

By contrast, an analysis of the system’s capability using a reliability computation method that does not start out with an assumed “everything in service” normalcy base, which accommodates analysis of partial failures of tap changers, and which accommodated some (but not all) uncertainties in loads and operating conditions, determined the profile shown by the dotted line. At high loading levels (those near peak load), the system is incapable of providing the reliability required) – the rectangular profile is actually rounded off. The system can provide the peak load but with much less reliability than expected.

³ N–1 analysis does not determine an actual estimated reliability value, but in this case subsequent analysis showed that a valid N–1 criterion was equivalent to about 30-minute SAIDI, and that value is used here as the target reliability figure.

Partial Failures

Traditional N–1 contingency planning methods use “zero-one” enumeration of failures. In the contingency case analysis method (Figure 9.1), every unit of equipment and every line in the system is modeled as completely in service. In each contingency case, a unit is modeled as completely out of service. But modern power systems often encounter partial failures:

• A transformer may be in service but its tap changer has been diagnosed as problematic and is locked in one position, limiting system operation.

• An oil-filled UG cable’s pumps are disabled and the cable has been de-rated, but is still in service.

• Concerns about a ground that failed tests have dictated opening a bus tiebreaker to balance fault duties.

At the traditional loading levels that existed when contingency analysis was developed, such partial equipment failures seldom led to serious operating limitations and were safely ignored, while the contingency analysis still remained valid. In systems operating at higher loading levels, partial failures cause problems under far less extreme situations, and often cannot be ignored in reliability planning. For example, a power transformer loaded to 85% at peak, whose tap changer is locked into one position, is subject to voltage regulation problems that can easily reduce its ability to handle load by close to 15% in some situations.⁴ The contingency margin (100% - 85%) that the typical N–1 method assumes is there may, in fact, be mostly nonexistent.

⁴ Loss of its load tap changer does not cause any lowering of the transformer’s capability to carry load. However, flow through it is now subject to variation in voltage drop – higher flows result in higher voltage drops. It may be unable to do its job, within the electrical confines of its interconnection to other parts of the network, due to this variable voltage drop, which may limit it to partial loading only. Ignoring this and accepting the higher voltage drop that goes with full capability during a contingency would lead to serious problems of another type (unacceptably low service voltages to customers, or higher demands on other transformers).

Connectivity Sensitivity

As mentioned earlier, success in handling a contingency depends on the power system in the “neighborhood of contingency support” around the failed unit being connected in such a way that the neighboring units can provide the support while remaining meeting all electrical standards and satisfying all operational requirements. At higher equipment utilization rates, this neighborhood is generally larger everywhere within the power system.

This greater size is not, per se, the cause of problems for traditional N–1 analysis. Standard contingency-based analysis and the engineering methods that accompany it can fully accommodate the detailed electrical and capacity analysis of any and all contingency support neighborhoods, regardless of their sizes.

But each of these wider contingency neighborhoods involves more equipment and interconnections. Thus, accurate modeling is sensitive to more assumptions about the exact amount and location of loads in the surrounding areas of the system, and the way the system operator has chosen to run the system at that moment, and myriad other details about operating status. There are more assumptions involved in accurately depicting the status of each of the N components’ contingency support neighborhood.

And the analysis of each of these N contingency support neighborhoods is more sensitive to these assumptions. The range of uncertainty in many of these factors about future area loads and operating conditions is ± 5% to ±10%. Such ranges of uncertainty are not critical in the N–1 contingency analysis of a system operating at 66% utilization. The available contingency margin (33%) is considerably larger than the range. But when operating at 90% utilization, the uncertainty ranges of various factors involved often equal the assumed available contingency support capacity, and there is a larger neighborhood, within which it is more likely something will be different than assumed in the N–1 analysis.

Aging High Utilization Systems Are Sensitive to Forecasting Errors

A projection of future need is the first step in power delivery planning. The forecast of future peak load defines requirements for the capability of the system, starts the evaluation of alternatives as to feasibility, value, and cost, and defines the constraints for selecting the alternative which best meet requirements. Poor load forecasting has been a contributor to a significant number of aging infrastructure system problems around the nature – in the authors’ experience roughly half. Two areas of forecasting deserve special attention.

Impact of Mistakes in Weather Normalization on Reliability

The weather normalization method used in planning and the design weather targets set for the system are among the easiest matters for “rationalization” when efforts are being made to cut costs. For example, if planners re-set their design weather conditions from a criterion of once in ten years to once in five, or lower the forecast target in some other manner, the budget requirements that flow out of their system planning process will fall. As an example, the utility whose load is diagrammed in Figure 9.8 has an annual growth rate of nearly 1.0%. Summer peak demand sensitivity is 1.25%/degree F. Reducing the design weather target by one degree Fahrenheit, about the equivalent of going from once-in-ten to once-in-five years, will reduce the load growth forecast for a four year period to that forecast over five years. Assuming for the sake of this analysis that budget directly corresponds to amount of growth, that means it results in an annual budget reduction of 25% over the next four years. For this reason, a number of utilities succumbed to the temptation to change weather normalization too much.

• Weather normalization that targets a “too average” weather condition puts the power system in high-stress situations too often.

A low load forecast results in several detrimental impacts. First, it generally leads to a situation where the system is serving more load than intended. Usually, this does not create severe problems when all equipment is functioning, although it does age equipment somewhat faster than expected (accelerated loss of life). One can view operation at loads above design conditions as a contingency. Poor load forecasts used in the planning or operation of a power delivery system effectively “use up” its contingency capability (see Willis et al, 1985). Poor normalization of weather data for forecasting or poor spatial forecasting (poor correlation of loads with areas and equipment) results in deterioration of a system’s contingency withstands capability. This greatly exacerbates the reliability-of-service problems discussed up to this point in this book.

Equally important, and far less frequently recognized as a key impact of poor forecasting, the system serving a load above that for which it was designed will operate for many more hours of the year in a state where service quality is at jeopardy if complete or partial failures occur, or if “things don’t go exactly right.” Figure 9.9 compares the annual load duration curves for a “average year” as used in design of the system and 1999 (a one-in-ten year), for a large investor-owned utility in the central United States. The difference in peak demand between an average year and an extreme year is 4.4%. However, as shown, not only peak load changes but, annual load factor. The period of time when the system is above 75% of peak (defined as “high stress” earlier in this chapter) increases by 28%. As a result SAIDI increases significantly.

Figure 9.9 When weather is above average, it is usually somewhat above average for an entire season (summer, winter) or a good portion of it, not just for a day or a week. Shown above are forecast versus actual annual load duration curves for a utility in the mid – United States, for 1998. As a result of the higher temperature summer weather, peak demand was 3.3% higher than the mean-weather peak projected for the system. But in addition and perhaps more serious, the number of hours that the system was in a “high stress” loading situation – where contingency loading and switching are a concern – was 30% higher than for an average year. This, rather than the higher one-time peak load, was the major impact weather that year would have on reliability and operation.

Figure 9.10 Maps of peak annual demand for electricity in a major American city, showing the expected growth in demand during a twenty-year period. Growth in some parts of the urban core increases considerably, but in addition, electric load spreads into currently vacant areas as new suburbs are built to accommodate an expanded population. Forecasts like this, done for two-, four- and similar periods out to twenty years, set the requirements for both short- and long-range power delivery system planning.

Table 9.4 Percent of Utilities in North America Using Some Type of Formally Recognized Spatial or Small Area Load Forecasting Method

A number of methods are in use for spatial forecasting, from simple trending methods (extrapolation of weather-adjusted substation and feeder peak load histories) to quite comprehensive simulations involving analysis and projection of changes in zoning, economic development, land-use, and customer end usage of electricity. Results vary greatly depending on method and resources used, but engineering methods exist to both determine the most appropriate methods and forecast characteristics needed for any utility application, and to evaluate the efficacy of a forecast. The most important factor is that a utility employs some legitimate means of studying and projecting load on a detailed enough location-basis to support its planning needs.

Traditionally, good spatial forecasting required both considerable labor and above-average engineering skills and was considered a “high-expertise” function within state of the art distribution planning methods. The best traditional methods worked very well but had rather high labor and skill costs methods (see Willis and Northcote Green, 1983 and Engel et al, 1996). Many utilities cut back on both the quality of the technique used and the effort devoted to data collection and forecasting study, when they downsized professional staffs during the 1990s. Table 9.4 illustrates the reduction the number of utilities using the best class (simulation-based) spatial forecast methodology, but does not reflect reductions in the data or time put into the forecasting effort. As a result, at a time when spatial forecasting needs are at an all time high (see below), the quality of local-area forecasting done at many utilities deteriorated sharply.⁵

In the very late 1990s, new forecasting methods were developed that reduce labor and skill requirements considerably, but these have limited availability and are not widely used (Brown et al., 1999). However, this means methods that can provide the information needed within reasonable labor and skill limits are available to the industry.

Interconnection Complexity

In the slightly simplified power systems used as examples earlier in this chapter, the small contingency support neighborhoods needed at 66% loading required interconnection with only two neighboring units assured success without overload during N–1 conditions. But at higher utilization ratios, contingency support neighborhoods grew in size and number of mutually supporting components. Interconnection of more equipment, into a scheme where it could support one another during contingencies, was necessary for success of the contingency plans.

In aging areas or where for other reasons planners and engineers have accepted near 100% utilization of equipment, there is a requirement for an even stronger and more widespread interconnection. Everywhere in a high-utilization system, each of its N units must have a strong-enough electrical tie to a wider neighborhood of equipment around it, to support its outage.

At higher equipment utilization rates, the importance of configuration and operating flexibility in the design of the system becomes more critical to reliability.

Traditional contingency-based study methods can deal with the analysis of these issues relating to the wider neighborhood of support around every one of the N units in the system and its great complexity. They can determine if the required number of neighbors are there, if they have enough margin of capacity to accept the load without overloads, and if the system’s electrical configuration makes it possible for them to pick up the demand that was being served by the failed unit. Basically, modern N–1 methods can and will determine if the failure of each unit in the system is “covered” by some plausible means to handle its failure and still provide service.

Again, N-1 methods do not work with probabilities nor determine the system’s sensitivity to multiple failures, so they cannot determine the failure sensitivity or failure likelihood of these complicated interconnected schemes. At higher utilization ratios, complexity of the contingency backup cases has increased. Determining if a particular contingency backup plan is really feasible, if it is really connected with sufficient strength to survive the likely failure states, or if it depends on too much equipment operating in exactly the wrong way, is something N–1 methods do not fully address. Configuration needs to be studied from a probabilistic basis – is this entire scheme of rollover and re-switching likely to really solve the problem?

The problem is not high utilization rates

A point the authors want to stress again is that high equipment utilization is not the cause of poor reliability in aging infrastructure areas of a power system. It is possible to design and operate power systems that have very high (e.g., 100%+) utilization factors and provide very high levels of service reliability. This is accomplished by designing a system with the configuration to spread contingency burden among multiple equipment units, with the flexibility to react to multiple contingencies, and that can apply capacity well in all the situations most likely to develop. Such designs require detailed analysis of capacity, configuration, configuration flexibility, failure probabilities, and the interaction of all these variables, and careful arrangement of circuits to build in sufficient reliability.

Equipment utilization is only one factor in the design of a power system. In some cases, the best way to “buy” reliability along with satisfactory electrical (power flow) performance is to use capacity – to build a system with low utilization ratios. But in other cases, particularly where the cost of capacity is very high (as it is in many aging infrastructure areas), good performance comes from using the equipment to its utmost. Achieving high reliability of service even in these situations where equipment is highly stressed and contingency margins are small or non-existent requires using configuration and interconnection flexibility in an artful manner.

Traditional Tools Have Shortcomings With Respect to Modern Needs

Many of the problems faced by a utility owner/operator of an aging power T&D infrastructure are compounded by the fact that the tools being used by its planners and engineers cannot directly address one vital aspect of the required performance: reliability. Traditionally, reliability was addressed in power system design by engineering contingency backup capability into the system: every major unit of equipment could be completely backed up should it fail. This was termed the “N–1” criterion and methods that engineer a system based on this criterion were often referred to as “N–1” or “N–X” methods.

Such methods addressed a key and necessary quality for reliability: there must be a feasible way to do without every unit in the system, some way of switching it around during its outage and/or picking up the burden it was serving during its outage. Realistically, no power system can be expected to provide reliable service to its energy consumers unless it possesses this necessary qualification of having complete N–1 contingency capability.

But through many years of use during periods when equipment loading levels of equipment were lower than is typical in the 1990s and 2000s, the power industry came to view the N–1 criterion as necessary and sufficient. For power systems with roughly a 33% redundancy (contingency margin), the criterion is effectively a necessary and sufficient criterion to assure reasonable levels of reliability. However, when a power system is pushed to higher levels of equipment utilization efficiency, N–1 is still a necessary criterion, but no longer sufficient to assure satisfactory levels of reliability.

Basically, the shortcoming of the N–1 criterion, as well as engineering methods based upon it, is that they do not “see” (respond to, identify problems with, or measure) anything with respect to reliability of service except the “yes/no” satisfaction of this one criterion. Therefore, they cannot alert engineers to a flaw in the design of a power system with respect to its reliability, due to either the likelihood that events may get worse than the system can stand. “Yes, you have a feasible backup plan, but for this system it is very likely that while this unit of equipment is outaged, something else will go wrong, too.” Or, because some parts of the system are key elements for reliability of the system around them, to the extent that they are “important enough” that they need more reliability built into their design. “This unit is more important than you realized: A second backup is really needed for the reliability level you want to achieve.”

Furthermore, they cannot provide quantitative guidance to planners and engineers on what to fix and how to fix it in a satisfactorily effective but economical manner.

A Deeper Problem, Too

Finally, there is a more subtle but perhaps more fundamental problem associated with the use of N–1, one that is difficult to fully demonstrate in a textbook, but nonetheless real. N–1 engineering procedures solve reliability problems by using capacity: It is a criterion and method that uses contingency margin as the means to achieve reliability. This is partly the nature of the criterion and the tool, but also the fault of the paradigm – the way of thinking – built for the engineers around the N–1 tools.

By contrast, experience with planning and engineering tools that directly address reliability will quickly show any engineer that very often configuration is the key to reliability. In fact, capacity and configuration are both key factors in achieving reliability and artful engineering of power system reliability requires combining both in a synergistic manner.

Traditional N–1 analysis tools (Figure 9.1) can analyze the functional and electrical characteristics of configuration. However, they cannot do so on a probabilistic basis, analyzing how the likelihood that the interconnection will be there and determining how that interacts with the probabilities that the capacity will have failed. Table 9.5 summarizes the limitations of N–1 analysis that need augmentation in order to meet modern power system reliability planning needs. Such methods will be discussed in Chapter 13.

Table 9.5 Seven Desired Traits of the Ideal Power System Reliability Planning Method (that N-1 Methods Do Not Have)

Explicit Reliability-Based Engineering Methods

What is needed to assure the sufficient requirement in power system reliability engineering along with the necessary, is a method that addresses capacity, configuration with an explicit, quantitative evaluation of the reliability of service they provide. Ideally, this must be a procedure that computes the reliability of service delivered to every element of the system, in much the same manner that a load flow computes the current flow and voltage delivered to every point in the system. It should be a procedure that can then be used by planning engineers to explore the reliability performance of different candidate designs, and that, in the same manner that load flows identify equipment that is heavily loaded (i.e., key to electrical performance), would identify equipment that is heavily loaded from a reliability standpoint (i.e., key to reliable performance).

Modern planning needs are best met using power system design and planning techniques that directly address reliability performance: Explicit, rather than implicit, methods. Such techniques have been used for decades in the design of systems where reliability is of paramount importance – nuclear power plants for commercial and shipboard use, spacecraft design, and throughout the aircraft industry. Adaptation of these methods to power systems provided a much more dependable design method to achieve operating reliability.

Such analysis begins with the same “base case” model of the power system as traditional techniques did. But probabilistic analysis starts out with a true normalcy base – a recognition that the natural condition of the system is “some equipment out” and that that condition will always be in a state of flux, with equipment being repaired and put back in service, and other equipment out of service. Like contingency enumeration methods, probabilistic analysis determines the consequences of every failure or combination of failures – can the system continue to operate and how close to the edge will it be during that time? But unlike traditional methods, probabilistic analysis determines if and how every combination of two or more simultaneous failures could interrelate to create problems, whether they are nearby or not in the immediate neighborhood, and it determines if that combination of failures is likely enough to be of concern. By tracing reliability through the configuration of the system while analyzing expectation of failure-to-operate, it effectively analyzes configuration and its relationship to reliability, too.

Depending on the exact method used (Chapter 14 will cover three basic types), partial failures can be accommodated using conditional capacity levels, partial failure states, or by recognizing sub-units within each main unit. Assumptions about loads, operating conditions, and other aspects of system operation can be modeled using probability distributions with respect to those variables.

Table 9.6 Overall Recommendations for Planning Methods

Evaluations determine and prioritize problems in the system as they will occur in the actual operation. Areas of the system, or operating conditions, that are especially at risk are identified. A big plus is that the method can be fitted with optimization engines to solve the key identified cases – “Find the lowest cost way to make sure this potential problem won’t be a real problem.”

Such methodology for reliability-based design of a power system can be created using a highly modified form of N–1 (Figure 9.1) analysis in which probability analysis is used at every stage, or by combining N–1 with reliability-analysis methods. What is best for a particular utility or situation depends on a number of factors specific to each case. But the important point is that there are proven, widely used methods to perform this type of work available to the power industry, and in use by selected utilities. Such methods are a key aspect of solving aging infrastructure problems: the reliability of such system areas must be well analyzed and solutions to it well engineered. These types of methods will be covered in Chapter 14. Table 9.6 summarizes the overall recommendations on planning method improvement needed to meet modern reliability engineering needs.