Gradually Growing Older

America’s electric utility systems are growing older. In many systems, significant portions of the equipment and facilities in service date from the economic boom following WWII, or from the sustained growth period of the 1950s and 1960s that many American cities and towns experienced. A lot of equipment installed then, and still in service today, is between 50 and almost 70 years old.

As electrical equipment gets older, it deteriorates from the wear and tear of service. At some point, it wears out. Then, it may fail outright, or more often, succumb to forces it would have withstood when new but no longer can - the high winds of a storm or the rare electrical spike caused by switching or lightning. Such failures cause interruptions in service to the utility’s customers, and require expensive emergency repairs and system restoration work. Monies will have to be found to pay for that work and the cost of replacement equipment.

All equipment eventually reaches an age where deterioration and increase in failure rate is significant. This is the first fundamental truth of aging power infrastructures. Electric utility equipment is designed and built to be incredibly robust, but eventually time wins: sooner, but usually later, everything fails. A second fundamental truth for most utilities is that it is no longer sooner and that it will soon be later: large portions of old equipment are approaching an age when they are not going to provide dependable service without considerable attention.

A portion of the equipment installed fifty to sixty years ago has already failed in the decades since. Storms, floods and other natural disasters took their toll. Accidents, such as a power pole being hit by a car or an underground cable being dug up by a construction crew, happened. Sometimes equipment just failed for no obvious reason. Regardless, all that equipment was replaced when it failed. As a result, even in the oldest areas of a system, there is a mix of old and new.

But the old predominates. Even in areas built immediately after WWII, rarely has more than 25% of the equipment been replaced in the intervening decades. Well over 70%, and often more than 85%, of the equipment is original in spite of being more than five decades old. That creates a practical operating problem for a utility. Some of the equipment is new. The biggest portion, although having spent decades in service, is still good enough to provide satisfactory service for a decade or more to come. But a tiny fraction of the old is badly deteriorated, perhaps not to the extent that it shows visible signs of imminent failure, but to the extent that it cannot do its original job dependably. Those failures will poison the performance of the system overall.

It might seem that this problem is easy to solve. Just have the utility find and replace all the equipment that is going to fail in the next few years, and do that on a continuing basis. Realistically, that is not easy to do. First, the level of failure considered “bad” is remarkably low. Power systems depend on long chains of transmission lines, substations, transformers, breakers, feeder circuits, reclosers, sectionalizers, fuses, cutouts, service transformers and service drops. All must function well if the utility is to deliver reliable power to its customers. If anything fails in this chain, some utility customers go without power until service can be restored.

Thus, an equipment failure rate of only half a percent per year would be disastrous. It would mean service interruption rates that are completely unacceptable to customers, regulators, and stockholders alike, and emergency field work rates that exceed the utility’s ability to dispatch crews to do the work. Under normal circumstances, less than two tenths of one percent of equipment in good condition fails in a year. Thus, finding the future “bad actors” in a power system is a challenge – the proverbial needle in the haystack. A utility might have 8,000 steel lattice transmission towers in service, and it will be a very bad year indeed if more than four give serious problems.

Finding that flawed equipment is not easy. In many cases, badly deteriorated equipment does not give obvious signs of its undependability. Tests can be done to improve the knowledge the utility has of equipment condition. But those require skilled personnel, always in short supply, and expensive specialized equipment. And many tests are intrusive to customers: utility technicians have to go into backyards and factory property to set up and run their tests. In downtown areas they must cordon off lanes of major streets while they work in equipment vaults under the pavement.

Furthermore, test results are not entirely dependable. A test that is 95% accurate may sound as if it is a good test – and it may be the best available – but its unrestricted use creates problems. Suppose a utility has 10,000 older wood distribution poles, of which 500 (1/2 percent) are truly so deteriorated as to probably fail in the next decade. A 95% accurate test will find 475 of those, leaving 25 bad poles in the field. That alone is a concern, but not the major worry from this test. The real concern is that if it has a 5% false positive rate, too, then it will identify 475 of the 9,500 good poles as bad, for a total of 950 poles it indicates should be replaced. If the utility follows those test results it will replace twice as many poles as necessary – and incur twice the cost. The test may be the best that can be done in the circumstances, but it is not the type of clean, effective solution that inspires confidence among all stakeholders.

Furthermore, replacement of equipment already in place is very costly. Of course, there was a significant cost associated with the original installation – what is often called “greenfield” site work. Money was required to buy a pole or transformer or cable, and skilled labor was needed in every aspect from design to construction. But the cost of replacing that equipment many years later is much greater. First, before the new equipment can be installed the old equipment has to be removed and recycled, etc. Then new equipment has to be put back into place and reconnected to the rest of the system. Further, typically this work must be done while the rest of the system is in operation – i.e., energized – so that service to customers is not interrupted. That means the utility’s personnel must work in and around live electric equipment. Such work can be done safely, but will proceed much slower and cost more than the de-energized site labor that was used in the original installation. As a rough rule of thumb, replacement work is two to three times as expensive as an initial greenfield installation.

Thus, replacement of even a small portion of an aged system can be a formidable financial burden to a utility. Going back to the 10,000 wood poles discussed above, the utility would follow up its test results by replacing nearly 1000 poles in order to obtain a failure rate over the next decade only slightly above that for all-new equipment. That may sound like a good bargain. But those replacements will each cost about three times as much as new poles cost when originally installed, and again, it is replacing more than two poles for every failure it will avoid, even though the test procedure is 95% accurate. And those tests, themselves, have a noticeable cost, as does the effort to correlate test results and manage the pro-active replacement in and around all the other activity the utility has keeping the lights on 8,760 hours a year. Altogether, the utility will spend roughly 5% of the cost of the original construction of the entire line for these 10,000 poles (adjusted for inflation, etc.) in order to keep failures and their consequences under control for another decade: or about half a percent per year.

To put that half a percent capital expenditure in perspective, consider that a typical utility might add about 1% new equipment each year for system expansion – as its customer base and energy sales grow. Traditionally, the rates that it charges its customers, the budgets it puts together and manages to each year, the number and types of field crews and materials and equipment supply infrastructure it has, and its management bandwidth, too, are all geared to that level of capital spending and construction. This additional half a percent cost to pro-actively handle aging equipment is a 50% increase above that traditional rate of capital spending on system equipment “additions.” To a company that may have only a single-digit profit margin, that finds it difficult to hire skilled field personnel and managers, this can appear to be an untenable challenge.

Against this, however, is the undeniable fact that O&M costs for repairs, restorations, and replacement for failures are and will continue to rise in the foreseeable future as the system continues to age, and that if the utility manages pro-active programs well, they will ultimately reduce cost as compared to that. The utility has no choice but to adapt with new testing, tracking, evaluation, analysis, planning, and management approaches to control its infrastructure aging.

In any practical sense this issue of aging infrastructures is new to the utility industry. During the last half of the 20^th century, electric utilities in North America, Europe and in many other places around the globe developed and institutionalized incredibly efficient methods and processes to run a utility system while providing high levels of reliability (§ 99.98%), all at costs that regulators accepted as being as low as practically possible. Then, equipment that today is 60 years old and beginning to give problems was only 20 to 40 years old and still quite robust.

Since the problem of aging infrastructures was not a priority then, utilities and the power industry did not set themselves up to address it. Nor was it a focus for equipment manufacturers and service providers that support utilities: they go where the market leads them. Even only twenty years ago – half a career ago for senior managers and the most experienced engineers and operators at a utility – this problem did not exist in any measure serious enough to make it a priority. The challenge the industry now faces is to change this situation in time to effectively control aging and its impacts before the problems they cause become serious.

Delivering Power to Mrs. Watts

Figure 1.1 shows a typical power system from the generation plants to the home of a customer, Mrs. Watts. The power Mrs. Watts uses to heat her home this week came through the wholesale grid from any number of power generation plants and not always from the same ones. But it flows into the same local distribution system all the time. And except during emergencies or contingencies, always takes the same path to her home. This “power delivery system” is owned and operated by the local utility company.

Power Delivery: Getting Power to People

In contrast to the wholesale level, the retail, or power delivery, level exists solely to route power to consumers. Table 1.1 lists criteria that FERC (Federal Energy Regulatory Commission) established to identify the local delivery level. Most important to this discussion is a point mentioned earlier: a particular consumer’s power always flows through the same part of the delivery system, except on those rare occasions when there is a contingency. The power that Mrs. Watts uses each day is intended to always come through the same set of service drops, through the same service transformer, lateral branch, feeder trunk, substation switchgear, substation transformer, high-side switchgear, and substation transmission-level feed line. Only during an equipment failure will an alternate route be used for some part of this supply chain.

Power delivery infrastructures are those portions of the power system whose purpose is solely to deliver power to energy consumers, and do not include those elements associated with the wholesale electric power grid.

Power delivery includes more than just local distribution, although it certainly includes that. It also includes a portion of the transmission-level system in what was traditionally called the “sub-transmission lines – those lines that deliver power to distribution substations. Figure 1.1 indicated the portion of the power system identified as the power delivery system. This is normally the transmission that despite its voltage level, falls under FERC’s distinction of distribution function, as opposed to wholesale transmission (Table 1.1).

Contributing Factor 1: Aging Equipment

As cited above, old equipment is a serious contributing factor to poor reliability and high costs in many utility systems. In fact, there is usually some area in any electric distribution utility’s system where old equipment is a worrisome factor in providing good customer service quality.

All power system equipment will eventually fail. At some point, after a period of service, something will wear out or deteriorate to the point that it no longer adequately performs its role. The equipment will cease to provide electrical power delivery to those customers downstream of it. The nature of electrical equipment is that very often failures lead to a fault, requiring protective equipment action that isolates a larger portion of the system than just the customers served by that failed unit.

Regardless, electrical equipment does not have a uniform probability of failure with age. Usually electrical equipment fails only after many years of completely satisfactory service. Thus, old equipment is much more likely to fail than new equipment (roughly three to ten times as likely). As a result, a system or area of a system composed of old equipment will have a much higher failure rate than one composed of new equipment. This has three consequences, all of which are undesirable:

1a. Equipment failure causes lower customer service quality. Customers are more likely to have interruptions of their electrical service due to the unexpected failures of this older equipment. The frequency of interruption rises – SAIFI increases.¹

1b. Higher repair and restoration efforts. More failures mean more and more unpredicted events requiring emergency or contingency mitigation efforts. The impact of this on all but the most efficiently managed and staffed organizations is to increase duration of equipment outages (and hence customer interruptions). There are more numerous outages to repair, taxing the restoration and repair resources to the limit and often requiring repair of one to wait on another. SAIDI increases slightly as a result.

¹ SAIFI, System Average Interruption Frequency Index, the average number of interruptions of service experienced annually per customer. For a discussion of this and other reliability-related indices, see Chapter 4.

Also quite important for the utility’s bottom line, these higher repair and restoration needs increase overall costs.

1c. More inspection and testing efforts required. Equipment more prone to fail often benefits from more frequent and comprehensive inspection and testing, which in many cases can reveal developing problems before they cause failures. Catching trouble early allows what would have become unscheduled emergency repair work to be done in a more efficient, less costly scheduled manner. This reduces the utility’s costs, but the inspection and testing increases required increased costs. Compared to newer systems, costs are higher.

Chapters 6 and 7 will focus on various aspects of equipment aging, its management, and the mitigation of its effects in more detail. Usually, a utility cannot afford to replace all of its aging equipment. As a result, the solution to this particular problem source is a combination of partial measures that seek only to reduce failure rate slightly and control its effects:

a) Careful assessment and tracking of condition,

b) Good utilization of capacity and expected reliability of equipment,

c) Artful management of restoration and replacement policies,

d) Design of the system for failure – planning that assumes that things will go wrong.

Chapters 10 and 11 will discuss these in more detail.

Contributing Factor 2: Obsolete System Layouts

This, not aging equipment per se, is perhaps the strongest contributor to poor reliability in a sizeable portion of aging infrastructure cases. This is often not recognized or identified as an aging issue, but the facts speak for themselves.

Usually, the design of the power system in an area of a power system, its layout of substations and rights of way and the configuration of how they are interconnected and supported by one another during contingencies, dates from the time the equipment now in service was installed, or before. For example: the central part of the power system in many urban areas of the U.S. The “downtown power system” in most large cities is based on substation sites and rights of way that were laid out in the 1940s through the 1960s, when the post WWII boom created a period of sustained economic growth. These designs were executed with great skill to meet both the needs of the moment and reasonable amounts of load growth two or more decades into the future.

But between four and six (not just two) decades have passed since these systems were first laid out and built. In many urban areas throughout the United States, the electric demand has doubled or tripled since the power system last had a major revision in layout. By default, these systems had their layout “locked in:” As the city continued to grow, the utility found it increasingly difficult to obtain new substation sites or rights of way in the crowded, growing downtown area. Both economic and political pressures stood in the way of expanding its existing sites or adding new ones. Adding capacity at existing sites instead accommodated load growth. The utility often had to compromise preferred configuration (switchgear type and interconnection) and/or maintainability (quick accessibility) in order to squeeze every last MVA of capacity possible into crowded spaces.

At some point, equipment additions and renewals notwithstanding, the power system reached the limits of the existing sites and rights of way. Every bit of room had been used, every innovative trick within design standards had been exercised, and there was simply no more room. From thereon out, the utility either had to find some way to get the “unattainable” addition sites, or face the only other choice left: load the existing equipment in its sites and rights of way to higher levels than it would prefer.

In many cases, the rather extreme situations lead the utilities to overcome significant economic and political pressures and obtain some additional sites or space. But in all cases, the major manner of accommodation of load growth was by simply allowing loading on existing equipment to rise above traditional limits. A certain amount of this increase is by design, and does not necessarily have a negative impact. Since the mid 1980s, equipment utilization ratios (the ratio of peak loading to equipment capability) has risen through the power industry as electric distribution utilities sought to increase efficiency of equipment usage and cut back on expenses. It was not uncommon to see an electric distribution utility raise its target utilization ratio for transformers from 66%, about typical for the mid-1970s, to 70 – 83% during the last 15 years of the 20^th century. This increase was by design. But the increases in loading forced on aging systems due to a combination of obsolete system layouts and other factors (to be discussed below) went far beyond these limits. In the central core of many cities in the U.S., power delivery equipment such as sub-transmission cables, substation power transformers, and primary distribution circuits are all loaded to their maximum rating at time of peak demand, leaving next to nothing for contingencies or emergencies. In a few cases, loadings at peak average close 100% throughout the downtown area and exceed that at a few places. Several of the utilities that experience serious problems during the summer of 1999 had some major delivery equipment in their central systems that was loaded to over 110% of nameplate rating during peak periods, not by accident, but by intention.

There is plenty of evidence that this trend is widespread. Figure 1.3 compares 1999 utilization ratios in the central core and the suburbs of five major metropolitan systems, all among the largest power delivery systems in the U.S. Not all urban utilities follow this pattern, but a majority seems to match this profile. This is not to say that the utility’s engineers preferred this higher loading level or that management and operators were entirely comfortable with the high loadings. But it was accepted, not as an emergency situation, but as the “normal” plan for serving peak demand.

Distribution Designs That Are Not Obsolete, But Are Not Entirely Modern

At the distribution feeder and service level there are similar issues of design and configuration that interact with aging infrastructure impacts. Traditional distribution systems are nearly all radial in operation and serial in their sectionalization. As a result individual equipment failures cause noticeable customer power interruptions, and events involving multiple failures (such as during storms or bizarre events) tend to cascade to large numbers of customers out of service.

Considerable improvement in a distribution systems SAIDI and SAIFI, even in the presence of a slowly rising tide of equipment breakdowns and failures, can be made by using a combination of smart equipment and changes in the circuit layouts and operating configuration. The margin cost of reliability for such changes to a system is often far less than a the cost of dealing directly with the aging equipment failures via a replacement program, and is often a first step in mitigating the continued effects of system and equipment deterioration.

Figure 1.3 Substation transformer utilization ratios in the central core and the suburbs of five major metropolitan areas. Dotted line shows the utility’s own design guidelines.

Contributing Cause Number 3: Old Engineering Methods

Many T&D utilities are using engineering methods that worked well in the 1970s, but cannot fully guarantee reliability operation in a world where substation and line loadings are pushing beyond traditional levels. As a result, engineering planning functions at many utilities cannot identify precisely what the problems are or prescribe the most effective and economical remedies. This particular subsection will provide a particularly comprehensive summary of this subject. Engineering tools are in an area where utilities can make immediate improvements and one where improvement must be made if the aging infrastructure issue is to be properly addressed. Chapters 9, 12, and 13 provide more detail on this subject.

Other Engineering and Planning Related Problems

Power systems that operate with either an obsolete system layout and/or at high utilization rates are sensitive to more than just probability-related increases in interruption rates. The sensitivity of the system service quality to other problems increases exponentially. In particular, problems are encountered with partial failures, configuration complexity, and load forecast sensitivity, which are discussed in further detail in the following paragraphs.

Contributed Cause 4: Non-Optimum Use Of The Distribution Level

Averaged over the entire power industry, the primary distribution system is without a doubt both the most important and least well-used part of the power delivery chain, for several reasons. First, it accomplishes more of the power delivery function than any other level of the system, in the sense that power is routed into a few locations in fairly large chunks. Then the distribution system routes and sub-divides that power into very small allotments delivered to many different locations. No other layer (See Figure 2.3) of the power system accomplishes so much dispersion over area or so much sub-division of power.

Secondly, the primary distribution level has more impact on customer power quality than any other layer. In the vast majority of power systems, it is both immediately downstream of the lowest voltage regulation equipment (substation voltage regulators or load-tap changers) in the system, and by the nature of its topography, the source of the majority of voltage drop from that regulation point to the customer. That topography also guarantees that the primary distribution system sees the majority of outages that lead to customer service interruptions. Thus, in most systems, the distribution system is the source of the majority of both the voltage-related and availability-related power quality problems seen by energy consumers.

Contributing Cause 5: Old Cultural Identities and Ideas

Section 1.1 stressed the changes that aging power delivery infrastructures will require of utilities, their organizations, and their personnel. Simply put, the problem did not exist to any noticeable degree in past decades, and methods to address it were not part of the utility’s operations or culture.

Old habits die hard. At some utilities, the managers, planners, engineers and operators still tend to think in terms of the culture and goals that were appropriate for a purely regulated, vertically integrated industry. New goals may have been articulated, but for various reasons a change in thinking, in priorities, and in method has not followed, one which makes the planning, engineering, and operating compatible with those new goals. The use of the out-dated engineering methods cited earlier could be characterized as part of this problem. Particularly, the considerable resistance to new ideas that prevents many utilities from fully adopting new reliability approaches (Contributed Cause 3, above) and distribution planning practices and guidelines (Contributing Cause 4, above) is mostly due to cultural resistance to a change in institutionalized values and methods. However, the authors have identified those as separate issues – methodology, rather than culture -- in order to focus attention on broader issues. In a number of cases, perhaps half of the aging infrastructure problems the authors have analyzed – retention of outmoded priorities, corporate values, and concepts -has been a contributing factor in the problems the utilities experience with aging infrastructures. This is often not a result of resisting change as much as one of recognizing when, how and where to change.

Conditions Have Consequences

Conditions have consequences – they mean something with respect to the utility’s goals. The goal is not to develop a scoring system that puts equipment into “bins” on the basis of available data. The purpose is to find equipment that helps ferret out equipment that is counterproductive to corporate goals.

As an example, a condition scoring system based on DGA (dissolved gas analysis) data could be devised that would group the 900 power transformers a utility had into five bins of 180 each. The 15 transformers (1.7% of the total) in most need of attention are in bin F – but so are 165 other units. A three-tiered condition method that would perhaps put 850 units in class A (good to go as is, no special attention needed), 35 of them in class D (inspect very frequently so that developing problems can probably be caught in time) and 15 in class F (has a high probability of failure or breakdown or other problems).

Criticality

Condition measures should be based on the status and characteristics of the equipment itself: its DGA readings, its impulse test results, the evaluation of inspectors training in visual classification of defects, and its age, model type, and installation details. A separate measure is criticality, which recognizes that a utility might care about one potential failure or problem due to deterioration more than another of equal condition and likelihood, because it was more critical to achieving the utility’s goals.

For example, in Chapter 16’s case study, among a utility’s wood distribution poles is one that would cause 67,800 customer minutes of service interruption if it failed: among all 723,000 poles in the system it is the most critical to service, in the sense that it would create the most customer disruption and SAIDI increase if it failed. At the other end of the criticality scale are thousands of poles supporting single-phase laterals and service lines that lead to only one or a few customers and thus will have far less impact on customer service and reliability, creating perhaps fewer than 400 customer minutes of outage if they fail. Clearly, the utility wants to put more focus on tracking and controlling the condition of the most critical poles and prioritize these poles of lesser potential impact on operations to a lower level of attention.

Condition versus Criticality

Generally, a first step in the study on any particular class of equipment (e.g., medium voltage breakers) in an aging infrastructure is to classify the equipment in that class in two dimensions -- by both condition and criticality. Equipment in the top ranking (that of most concern) in both categories will get the most attention. Figure 1.2 is a matrix for the 894 medium voltage breakers in a particular power system, showing how many are in each combination of four categories of condition and four categories of criticality.

Fuzzy Evaluations and Staged Prioritization

A reality of aging infrastructure management is that nearly everything that must be analyzed and evaluated in development of a condition estimate is “fuzzy.” Data and models might provide good results and significant statistical fits when applied to an entire class of equipment (e.g., 900 breakers over twenty years of operation, all 723,000 poles in the system). But the data and evaluations for any one breaker’s condition or one pole’s level of deterioration and losses of strength are subject to a good deal of uncertainty. Experience has led the authors to strongly favor aging-asset management approaches that acknowledge the uncertainty and that cannot be practically reduced beyond a certain amount. Such approaches are therefore designed to deal with it efficiently.

From a practical standpoint the uncertainty or fuzziness means that the units assigned to each box in Figure 1.4 have some likelihood of belonging in a nearby box instead, particularly as regards the vertical (condition evaluation) ranking. There is little doubt that the three units in the upper right most box are in need of very focused attention: anything scoring that high in the initial screening will definitely “make the playoffs.” But the twelve units in the box right below are an example of the conundrum the utility faces due to uncertainty. A few of these units probably belong be in the box above and should receive attention now. Several probably should be in the box below and are not really a priority this year. For this reason, condition versus criticality matrices are only a first step in staged or hierarchical process as depicted in Figure 1.3. As shown, the sixteen breakers in the two categories closest to the upper right are scheduled for immediately inspection and evaluation. Based on those results several may be moved into the upper category and would be assigned for refurbishment or replacement. In this way, every one of the 894 medium voltage breakers in the system is assigned to an appropriate category of work to support the aging asset management program.

Figure 1.4 Condition versus Criticality Matrix for 894 medium voltage (feeder) breakers at a medium size utility in the central US identifies three transformers as having both a poor condition and high criticality of failure/breakdown consequences, and 16 others of high concern.

Figure 1.5 Condition and Criticality leads to actions that recognize the underlying uncertainty in condition evaluation and seeks to clarify condition in all but the most urgent situations before prescribing further action.

Manage Both Effects and Causes

Managing an aging infrastructure is about both reducing the effects of aging equipment as much as possible (if you can’t control the disease, control the symptoms) and controlling the deterioration of the system as much as possible (but keep the disease under control as much as you can). Some combination of effort and budget split between these two will be optimum. That is the primary focal point for executive management. For example, a distributed secondary network design renders a power system almost completely immune to a high incidence of small random failures through the system. Compared to the traditional radial central trunk feeder system, they are an order of magnitude more tolerant of failures. Should a utility with aging equipment shift to such a design so the system and the customer base “just doesn’t care” about increasing failure rates? Of course not. Making that move would be very expensive, and distribution networks carry their own operating characteristics that would be difficult to live with en masse over an entire system.

But a move in that direction might be a good idea. There is a huge range of distribution configurations between the distributed secondary network approach and the traditional central trunk radial feeder approach, such as multi-branch feeder/highly sectionalizable feeder layouts, open and closed loop systems of various types. Particularly in combination with “smart” equipment and control systems, these can provide a certain amount of immunity from increasing rates of equipment failure. The failures still occur, but they lead to far lower SAIDI and SAIFI increases.

Table 1.4 Characteristics of the Typical Aging Infrastructure Area

But ultimately, the utility has to address, or at least control, the root cause, which is deterioration of equipment due to the wear and tear of service. This correlates with age more than anything else, but age is not the direct cause. There is also a noticeable element of random luck involved: a brand new transformer can be struck by lightning and greatly weakened, too. As much as justifiable based on cost and results, the utility needs to find a way to address this.

Condition Data, Modeling, and Tracking is the Key to Success

Regardless of the concepts and approach taken in managing an aging infrastructure, condition estimation is the foundation of any aging infrastructure management program. The extent to which the utility can, in an affordable and practicable manner, separate the bad equipment (poor condition) from the good (otherwise) determines the potential it has to manage its infrastructure and achieve economical success. Ultimately, any path to success requires the utility to separate the equipment that is in a condition to give satisfactory service, regardless of age, from equipment, old or new, that is deteriorated or flawed to the point that it will not provide satisfactory service.

The Future System Will Not Necessarily Be Good, Just “Good Enough”

Power system engineers and operators dream of a system in which all of the equipment is in first class shape: in top operating condition, clean, nicely painted, shiny, and new. That is only a dream. It is not the practical future of any power system (even one that is brand new today). Any well-managed aging power delivery infrastructure will have high portions of old equipment in it. Quality and condition will not be uniformly first-class throughout, but only “barely good enough” in places.

The service transformers shown in Figure 1.6 are old, have a patina of rust and mildew, and do not live up to that dream. They are a bit noisy, too, indicating their cores are a tad loose. If tested, it is likely they would return mediocre results – within specifications, but not near the “new” end of the test range. Yet for all that, these transformers are in place and doing their job, and their condition and criticality does justify replacement. Replacing them makes little economic sense, to particularly when money is needed to replace and repair equipment that is in much more need of attention.

Figure 1.6 A bank of service transformers does not look new, because they aren’t, but they are good to go in a system that is trying to get the most from its past investments in equipment. The utility’s challenge is to separate these units, which can be expected to give satisfactory service for many years to come, from other units that won’t.

An Optimum Path Toward an Optimum Sustainable Point

In Section 1.1, the authors introduced the term sustainable point to mean an operating status at which the infrastructure no longer deteriorates and performance no longer worsens each year. Ideally, a utility would find an affordable and effective way to manage aging equipment to achieve a good sustainable point. It would put the resources, tools and management processes needed in place to affect that program. The system and its performance would then be controlled. The system would be an infrastructure with controlled aging, managed to a set of cost and results goals.

A good deal of this book is devoted to the sustainable point concept and its application to the management of the aging power delivery infrastructures. The final chapters of this book deal almost exclusively with determining what is the best sustainable point and then managing to it. The Appendices describe how to model it from operating and historical data and give details about the characteristics of using it in practical application. Key features of sustainable points are summarized here. They will be presented in more detail, proven, discussed and then used later in this book.

A sustainable point exists from every power delivery infrastructure. The sustainable point for a particular infrastructure is a function of the power system’s layout and design, the equipment it is composed of and the ages and conditions of that equipment, and the ownership strategy, operating rules, guidelines and standards being used to care for and operate the system made up of that equipment.

A different sustainable point can exist, if changes are made. Change anything within this infrastructure – change the system layout and the way circuits are configured, or the equipment or its condition, or how it is loaded or how it is inspected and maintained, or the ownership strategy and operating rules for refurbishment and replacement, etc., and the sustainable point will change.

A power delivery infrastructure trends toward its sustainable point – slowly. As a mental exercise, suppose one installs 100,000 wood distribution poles, all new, and puts all of them into service on January 1 of particular a year. Ignore infant mortality failure rates in this example and operate them as most utilities do (basically, do nothing until they fall over or a period of five to ten years, when they are inspected and treated with an anti-fungal spray).

Very few will be replaced in the following 365 days of operation because they are all new and in good condition and few will fail. As a result, nearly all the poles will make it into year two. The “system” will age almost exactly 1.0 year in its first year of operation, and enter the following year with average age just slightly less than one year.² In that following, second year of operation, the poles will again exhibit a good failure rate, etc.

Over time the average age continues to climb at about one year per year, but as the age increases, the average failure rate begins to increase – very slowly at first because deterioration rates are very gradual initially. More and more failures will occur each year, meaning that more and more new poles are installed each year, until at some point the new poles counteract the continued aging, at least on average. To make the sustainable point and this trend understandable for the moment, suppose that this balance occurs when average age reaches 50 years, at which failure rate is 2%. This means in this year, 2% of poles will be replaced with new (because they fail) and 98% will not fail and will be a year older going into the next year. Average age will then be 98% x 51 years + 2% x 0 years = very nearly exactly 50 years again. The system will not have aged: it is at its sustainable point. The reader should note that this is a very simplified example, far too simple for useful application or generalization. It is used here only to quickly communicate the concept that eventually a balance point – the sustainable point – is reached. The fact that this point exists for any and all power infrastructures can be proven mathematically and demonstrated by example based on just about any power system. But in the real world, the sustainable point is more complicated than this example makes out. In particular, in the real world failure rate and age are never reciprocal (in this example, the age, is equal to the inverse of the failure rate of 2%, i.e., 50 = 1/.02).

Furthermore, due to non-linearities in just about every relationship and factor involved, and non-uniformities in nearly all the statistical distributions involved, it is much more common for a sustainable point for a set of wood utility poles to be something like 64 years at .61%. In addition, the sustainable point is reached when not just average, but distributions about the mean are stable. This is often quite important to a utility. All this will be discussed as needed in later chapters, It is covered in detail in Appendix B’s tutorials.

Practical Implications: the Good and the Bad

From a practical standpoint, what is important to electric utilities and the people who manage and operate them about the sustainable point is:

Good: Most power systems are not at their sustainable point – yet. With no significant exceptions, all utility systems in the US are not yet at their sustainable point. Despite age and aging, including perhaps pockets of seriously old equipment and facilities (80+ years old) even the oldest systems in the US are still getting older ever year.

²Perhaps the failure rate for new poles is .25%, meaning that 250 poles failed. Treating the new poles as age = 0 and the originals as age =1, at the beginning of the next year the average age is .9975 years.

Bad: The sustainable point is much worse than now. In almost every power system throughout North America, the sustainable point for the power delivery infrastructure (power system in combination with current operating and management rules) would be completely unacceptable to customers, utility, and regulators alike. It represents a combination of failure rate, reliability, and cost that is far worse than present.

Good: The system will take time to get there: one can count on the slow, steady trend. Power systems do not fall “off the edge” with regard to aging and its effects, nor does aging typically lead to catastrophic “system meltdowns” when failure rates and costs skyrocket over a short period of several years. Aging power delivery infrastructures instead are very similar to quicksand: the situation slowly but inexorably worsens, perhaps until it is very bad. This is a particularly apt analogy in other ways, too. First, often measures taken to get out of the mess are counter-productive, and only worsen the situation in the long run. Second, only a sustained steady pull will extract one from the situation.

Bad: all this means it’s going to get worse every year. Things move slowly, and storms and year-to-year randomness mean volatility will be seen in annual results, but the long term trend is inevitable: things will gradually get a lot worse.

Good: the sustainable point can be moved. The utility can change its strategy, its ownership and operating policies, its maintenance and service guidelines, etc., and move the sustainable point to something more to its liking.

Bad: it takes a good deal of time for systemic changes to have a significant impact. The “sluggish” response of the system to aging and its effects works the other way, too. Perhaps a utility is at an uncomfortable point with regard to particular equipment (i.e., failure rates are 3X that of new equipment due to significant deterioration of condition) in a system on its way to a sustainable point that is much worse than that, perhaps 10X). A change in inspection, maintenance, repair and refurbishment policies that results in a sustainable point that has a failure rate “as good as new” will not make a huge impact overnight. Even targeted as effectively as possible, these changes might take five years or more to drive failure rates back down to only 2X.

Effective programs cost a lot of money. As discussed in Section 1.1, it costs more money. The industry will ultimately have no choice but to either accept worsening reliability or somewhat higher costs.

Good: changes in design and configuration along with the addition of “smart equipment” reduce the system’s vulnerability and sensitivity to its aging and deterioration-related effects. Basically they mitigate the consequences of failure. Old, deteriorated equipment still fails as frequently as before, it just does not matter as much or have as much effect.

More good: these measures lower the cost all around. Actually, these changes move the sustainable point themselves, but the way many people chose to look at them is that they make changes to operating guidelines, maintenance and inspection programs, etc., more cost effective (less is needed).

The Optimum Plan Does Not Quickly Reach Its Sustainable Point

The sustainable point is a useful concept because it makes the basic behaviors and trends of an aging power delivery infrastructure (see above) more understandable and intuitive. It also can help in some statistical analytics applications and numerical modeling steps (knowledge of the characteristics of an answer can help solve for it with minimal error). However, most often the optimum business strategy is to not quickly jump to the sustainable point. Examples later in this book will demonstrate this, but generally, given the “sluggish” response of aging systems and the effectiveness of methods like circuit configuration revision and the addition of smart equipment in reducing system sensitivity to equipment failures, the optimum business strategy is to start now, on a path that optimizes results but might takes as long as twenty years to reach a sustainable point: as the system continues to age and deterioration continues to take its toll each year, the utility makes staged changes that keep the effects of aging just barely at bay: the program just barely erases the increases in reliability problems and operating costs that would have occurred.

Thus, every year, as the system’s failure rates increase just a small amount, the utility spends a little more on smart equipment and targeted circuit configuration changes to make the system a little less susceptible to failures than it was the year before. It begins to remove the most worrisome equipment and replace it with new or refurbished equipment. It applies life extension in areas and to equipment where it makes economic sense. It starts to apply inspection and maintenance programs that will have an effect over the long run. All of this is done in a coordinated manner, with the most effective measures being “used up” first, and generally only to an extent needed each year.

Thus, when all is said and done, a lot is changing each year, but what is not changing are the key performance indicators. SAIFI, SAIDI, safety, system performance, utility profit margin, and business results basically stay where they have been and were traditionally. The utility is on a long-term, gradual path that will take years but will gradually change the sustainable point to an acceptable status. Perhaps the original sustainable point represented reliability and cost problems that are three times what last year’s were. And perhaps it would take twenty years for the quicksand-like pace of deterioration to take its toll, (these values are representative of many real systems). Then the roll out of the program can be be viewed as taking about that long. The utility gradually moves the sustainable point to that operating point it wants.

This works: performance stays where it was. It is often optimum or close to it from just about any and all standpoints. But it is necessary for one practical reason. Any solution to the management of an aging power delivery infrastructure will involve increasing spending. This can be done optimally but it will still represent a significant increase – the discussion in Section 1.1 mentioned a 50% increase and that is representative of what most utilities will face. Rolling that out gradually, a few percent a year, is really the only option that will be viable to the utility, to regulators, and to the utility’s customers. That said, there are two final points executive management will need to consider:

1. The best sustainable point may not have today’s performance results. When all is considered, a gradual change to less reliability, or different reliability (higher SAIFI, lower SAIDI, or vice versa) may be best. Given historical trends, the authors expect our society will expect better reliability, particularly with respect to storm damage. Regardless, executives and planners should not assume that the only answer lies with keeping results where they are now.

It is never too early to start. The sustainable point might be 20 years in the future. The point where the continuing gradual trend becomes untenably unreliable and costly serious might be eight to ten years in the future. Regardless, the time to begin putting a solution in place is now: a 50% increase in spending spread over 20 years is much more palatable and manageable than a similar increase spread over only 15 years.

What Can and Should Be Done

The most easily identifiable characteristic of an aging delivery infrastructure is that a lot the equipment is old. However, “old” alone is not enough to cause concern and age is not the real issue. A good deal of that old equipment is very likely in satisfactory condition – good enough to remain in service for at least a few and perhaps many more years. The problem in an aging infrastructure system or area is that an unacceptably high portion of the old equipment is not dependable enough for continued service. It is deteriorated to the point that the failure rate or capability is seriously degraded, and from a practical and economic standpoint, it is incapable of providing satisfactory service in a dependable manner in the future.

Major Challenges Need to Be Overcome

The first major challenge to a utility is finding that equipment with poor condition amidst the greater mass of merely old equipment, determining what it faces overall (when it looks at all old equipment, its entire system, and its options) and then doing something affordable and effective about the situation. The second major challenge a utility faces is to determine what its viable alternatives are and which to pick. This usually involves folding aging infrastructure management into its long-tern business strategy and mainstream processes.

Table 1.5 Summary of Suggested Fixes for Aging Infrastructure Problems

The final challenge for a utility is in finding the money to implement its plan. Even at its best, any successful program to keep an aging infrastructure at a stable, sustainable and acceptable operating point will cost more than the utility was spending prior to aging having become a problem. A big part of the effort will be communicating this to other stakeholders and arranging cost escalation to be slow enough that it is palatable to others.

Table 1.5 gives ten key recommendations for how to proceed. Much this book will be about these recommendations and the details of their application. The central theme is a condition-based, sustainable-point focused, multi-attribute asset management approach. This complicated-sounding approach is straight-forward in concept even if rather intricate to apply well in many of its technical steps:

• Condition based. It is condition, not age, that is the major focus. Old equipment actually preferable: it means the utility is getting its value from what it spent – long ago.

• Sustainable point means that the utility does focus on the long-term aging trends and where they are going, and in determining how to manage them best in the long run.

• Multi-attribute means the utility is considering all its important goals and targets, such as customer reliability, system efficiency and cost, safety, public image, capital spending limits, and overall business risk.

• Asset management is a method of performing such work in a way that prioritize work and planning alternatives so that spending is as cost effective as possible (Willis and Brown, 2012).