Maximizing availability may not optimize plant economics
Conventional wisdom says you’ve got to spend more O&M dollars to improve your plant’s availability and reliability. But today’s plant managers should focus on optimizing the plant’s overall economics and avoid being sidetracked by jockeying performance stats. The approach: Benchmark your plant, and then optimize its economics using the latest in statistical methods.
When setting power plant performance goals, sometimes you can be your own worst enemy. For example, if your stretch goal for this year were to increase your equivalent availability factor (EAF) by 1%, then being the good plant operator that you are, you would find a way to make it happen. Power plants exist to produce electricity, but their business is to make as much money as possible. Unfortunately, attaching incentives to indirect measures of performance can often lead to suboptimal plant economics. In other words, achieving your stretch goal may actually negatively affect your balance sheet.
The industry’s new math
In regulated environments, the general equation for electricity pricing is (or was):
Price = Cost + Profit
Here, cost represents actual outlays for construction, operation and maintenance (O&M), and fuel that were deemed prudent by the regulator, which also sets the profit and therefore the price. Over time, cost-plus regimes fostered a risk-avoidance mindset. If successful, a high-risk investment could only yield a maximum allowable profit margin, while any unsuccessful investments that were judged imprudent were disallowed. Pavlov could not have trained his dogs any better: Taking risks was not a good corporate strategy because there were limited rewards for doing so.
When electricity markets began to be deregulated/liberalized, the equation governing utilities competing in them changed to:
Profit = (Market) Price – Cost
Here, profit is the dependent variable, price is determined by supply and demand, and cost represents the net impact on a utility’s bottom line of all its decisions, good and bad. Risk avoidance became a less effective strategy because the high returns from a series of successful high-risk investments now could be used to offset the losses from unsuccessful ventures.
Some utilities figured out this new math earlier than others; they evolved from risk-averse organizations to businesses in which decision-makers identify, quantify, and manage risk (excluding safety). As part of that group, plant managers now must choose from a range of best practices solutions whose cost is an important selection criterion. Under competition, the best solution is the one that is economically superior or most cost-effective, rather than the one with the least technical risk.
Room for improvement
Even a cursory look at available U.S. power plant cost data reveals that there is a wide variation in the O&M costs of top-performing plants and the rest of the pack. Those data show that the lowest-cost plants spend only half as much on O&M as the average plant, and that the highest-cost plants are 50% above the average. This spread also exists when only units with good technical performance are considered. Therefore, there is substantial opportunity for significantly reducing cost at many U.S. plants. Some utilities have already seized that opportunity (see box). A recent U.S. industry study showed that seven nuclear plants with a total capacity of 8,300 MW moved from the category of “not cost-competitive” to “cost-competitive” by reducing their O&M costs.
Improving the overall economics of a power plant requires a comprehensive understanding of the relationship between O&M spending and plant performance. It should be recognized that, in addition to the obvious—higher O&M costs—poor performance also leads to lost revenue opportunities and higher-than-necessary generation costs. There is a mutual interaction between O&M spending and plant performance. For the purpose of the following discussion, O&M costs are defined as outlays for operations and maintenance, including refurbishment capital, but not for fuel.
This article argues that plant managers shouldn’t strive to minimize their plant’s O&M costs or to maximize its performance (using availability and reliability metrics). Rather, their goal should be to minimize their plant’s total cost (O&M plus performance) by optimizing its O&M costs. Achieving that goal will produce maximum profits—the holy grail of any business.
New math, new frontier
A new statistical technique called Frontier Analysis (FA) makes it possible to do two things: estimate the point at which a plant’s total cost is minimized and set aggressive yet achievable cost goals for a plant. Although the following example does not hint at the rigorous statistics required for developing a true FA, it nonetheless is useful for demonstration purposes.
When EAF is plotted against O&M costs for a group of properly benchmarked plants, the result is often a wide scatter of data points (Figure 1). Such cost data can be obtained either from the Federal Energy Regulatory Commission, EUCG (see page 24), or other data sources and availability data reported to the North American Electric Reliability Council through its Generating Availability Data System.
1. Frontier Analysis. The “frontier curves” pass through the benchmarked data points and visually show the best quartile and best decile performers. The plant being benchmarked must also determine its proactive and reactive maintenance costs as part of the analysis. Source: Robert R. Richwine
Naturally, when benchmarking cost and availability it is vital to select as appropriate a peer group as possible. Studies done by the author and others reveal that for benchmarking purposes a plant’s design and operational factors are often more statistically important than its size and fuel type. For example, for fossil steam units it’s far more significant to know whether a unit is supercritical or subcritical, or operated in baseload or cycling mode. Whatever comparisons are made, cost data may have to be normalized to account for differences in labor rates and productivity, material costs, and local tax rates and environmental constraints. When benchmarking against overseas plants, exchange rates (and sometimes government subsidies) also should be taken into account.
Referring again to Figure 1, note that the two “frontier curves” go through the data points of those benchmarked plants that are achieving the highest availability for various levels of spending. Typically, these plants have incorporated best practices O&M techniques into their day-to-day management decision-making and are getting superior results. The best quartile and best decile frontiers are shown, which are often used to establish cost goals, plotted where 25% or 10%, respectively, of the data points lie below them.
By contrast, the top quartile and top decile frontier curves reflect the total of proactive costs (preventive maintenance) and reactive costs (corrective maintenance). Typically, low proactive costs result in low availability and high reactive costs, whereas high proactive costs (if the preventive maintenance efforts are effective) lead to high availability. Of course, as a plant’s EAF approaches 100% its reactive costs move toward zero (no unavailability means few breakdowns), but its proactive costs become exponentially higher.
Also note in Figure 1 that plants on the left side of each curve have lower availability factors with the same O&M costs as frontier plants on the right side. These plants are said to be “not on the frontier” because they operate inefficiently. Plants with the same availability factor but higher O&M costs are not achieving their full potential and are said to be in the interior. Potentially, such plants could decrease their total costs without decreasing their EAF, increase their EAF without increasing their costs, or some combination.
Studying the best O&M practices used by plants “on the frontier” provides valuable insights into methods that could be employed to move a plant there. However, any attempt to put a plant on the frontier requires locating its optimum economic performance point—the point of diminishing returns at which extra expenditures do not generate equal value.
Optimizing economic performance
Locating a plant’s optimum economic performance point requires superimposing the value of an increase in EAF (or the cost of a decrease in EAF) on Figure 1. Whereas units of similar design have similar frontier costs, each individual unit has a unique value (or cost) that depends on the economic conditions of the system in which it operates.
Glancing at Figure 2, you might conclude that the optimum economic performance point should be at the lowest point of the total O&M frontier cost curve (for quartile data, the yellow line). However, one also must consider another cost—the cost of unavailability. For a generating unit in a large regulated system, the cost of unavailability (typically a straight line, like the green one) can be estimated by calculating replacement energy costs. For a merchant plant in a competitive environment, it represents lost opportunities, lost profitability, or both. For a plant with a power purchase agreement, the cost of unavailability is determined by the terms and conditions of the contract.
2. Optimum economic availability. The costs related to a plant’s unavailability can be added to its total O&M costs to produce a curve that enables determination of its optimum economic availability. Source: Robert R. Richwine
Adding the frontier O&M cost curve (the yellow line) to the cost of unavailability (the green line) yields the total cost curve (the blue line). The unit’s economic goal should be to operate at the bottom of this curve. By dropping a vertical line from this point until it intersects the frontier O&M cost curve, one can determine the minimum cost necessary to achieve this goal, called the total O&M cost target.
The lowest point of a plant’s total O&M cost curve is known as the plant’s optimum economic availability (OEA). It is also the point of diminishing returns, at which an incremental increase in O&M spending (if spent as efficiently as a top quartile or top decile plant would spend it) would serve to increase EAF by exactly the same value.
If we now drop the vertical line further down until it intersects the proactive costs and reactive costs curves (shown in Figure 3), we can determine the optimal relationship between these two costs for our example plant.
3. Optimal costs. After a plant’s optimum economic availability is determined, optimal cost targets for proactive and reactive maintenance can be calculated. Source: Robert R. Richwine
By doing so, we can see that the plant’s OEA is a dynamic goal that changes as a function of its technical, operational, and economic environment. Figure 4 illustrates what might happen to the OEA if the plant becomes less valuable (its cost of unavailability decreases). The total cost curve will shift, as will the OEA. In other words, you may not be able to justify spending to maintain as high a level of EAF as in the past.
4. A moving target. The optimum economic availability (OEA) is not a constant. If the cost of unavailability is reduced, so are the OEA and the reactive and proactive maintenance cost targets. Any complete analysis must consider a range of unavailability costs. Source: Robert R. Richwine
And therein lies perhaps the biggest challenge to implementing a plant performance system driven by pure economics: How do you convince senior management that a lower EAF can mean better plant economics?
Since the above article was published by Power magazine in 2004, I have had the opportunity to apply the principles described at several US and international companies. As a result I have gained several practical insights that can be categorized into two primary areas: 1) cost issues and 2) availability/reliability issues.
1) Cost issues – The biggest issue with cost is to gain access to a reliable, consistent cost database, an issue which has become especially difficult in an increasingly competitive business environment (except for the nuclear industry). There are private cost databases that some have used but the only public domain cost database that I am aware of is the US Federal Energy Regulatory Commission’s (FERC) cost database where only regulated US generating companies use FERC form 1 to report their cost. Even here there are substantial inconsistencies in the cost reporting as well as regional labor rate and equipment cost differences. Furthermore, not all cost are spent on the plant’s availability/reliability but with an increasing percentage used for efficiency and environmental requirements. The problem is further compounded when international companies seek to use the US cost database with even greater labor rate and equipment cost differences and often radically different environmental regulations in addition to monetary exchange rate issues.
2) Availability/Reliability issues – The diagrams shown in the article imply that the annual spending results in a constant availability over the entire year. Of course we know that planned outages lead directly to seasonal differences in availability, but even when we convert the X axis from availability to reliability, usually using the term 1-EFOR (Equivalent Forced Outage Rate), there are normally substantial seasonal differences in EFOR. Studies have shown that for most companies their plants’ reliabilities are better during their peak seasons when they are most valuable (this will be the topic for a future case study). Many companies have established higher reliability goals during peak times reflecting that increased value and have in fact achieved those goals. I personally am convinced that if a plant’s goals actually reflect its value to its company, plant management will find a way to achieve those goals. Unfortunately, in my opinion, the number one problem worldwide for a plant in reaching its potential performance is the disconnect between its goals and the company’s goals. In future case studies I will be describing the studies I and others have undertaken that have led to this conclusion.
Although there are difficulties with the practical application of the concept of Optimum Economic Availability/Reliability (OEA) using statistical frontier analysis it remains in my mind a concept that should be well understood and incorporated into every company’s thinking when establishing goals for its generating plants. Some companies that have applied these concepts have reported to me that it has been “transformative” in establishing a better goals structure and expectations between their executive management, their generating plants and their trading/marketing organization as well as their other stakeholders. So even in the light of the practical limitations I have described, I would encourage you to explore the OEA concepts and techniques and consider applying them at your company. I would also encourage you to post any questions or comments you may have so as to open a dialogue on this and related subjects or contact me directly.