USING COMMERCIAL AVAILABILITY
In my last posting I examined why traditional measures of a power plant’s availability/reliability (EAF, FOF, UCF, UCLF and EFOR) were inadequate in today’s increasingly competitive business environment. I also indicated that a new measure, EFOR(demand), while an improvement, did not totally address the problem. In this posting I will review a different measure, Commercial Availability (CA), which has begun to be used by some generating companies around the world. This statistic attempts to measure the impact a plant’s availability has on the company’s cost of generating electricity (and its profitability when the company operates in a market-type environment). I will also discuss some of the implications that result from the adoption of Commercial Availability as the primary availability measure as well as how to calculate and benchmark CA.
The term Commercial Availability originated in the United Kingdom in the early 1990’s following the deregulation of its power industry into a “market” system. Since a plant’s availability only had value to its company if it could generate power at a profit, its availability was only measured during the times the market price was above the plant’s variable cost. Initially CA was not “weighted” with respect to the magnitude of this price/cost gap so that each hour when the unit was economically viable (its cost was below the market price) had the same influence on CA. Over time some users of CA have evolved the term to include the influence of the price/cost gap magnitude so that it can be a more accurate indicator of the plant’s impact on the company’s profitability. (E.g. during hours when the gap is $20/MW-HR the plant’s actual availability would have ten times the influence as an hour in which the gap was only $2/MW-HR). Therefore, CA attempts to measure the actual profit delivered by the plant relative to its potential profit if it had been able to deliver every MW-HR required of it at the actual market price (profit here is defined as gross margin, generally the difference between the plant’s variable production cost and the market price, or the system marginal cost in the case of regulated companies).
Although numerous companies in many countries have begun using Commercial Availability as one of their primary measures of availability, there is currently no standard definition for its calculation. In fact, at a recent meeting of an industry group, a survey of those attending revealed that about 1/3 of the companies represented were using Commercial Availability at some level, but none calculated CA in exactly the same way. Clearly the industry is in great need of a standard definition, but until then each company is free to define CA in any way that they choose.
Implications of using Commercial Availability
There will be a wide range of impacts on the way a company evaluates and manages its power plants resulting from the adoption of Commercial Availability and other tools/processes required to address market dynamics. This requires a different mindset and approach in applying data and new tools in both day-to-day and performance assessment decisions. Measures and actions must consider ways to quantify and respond to different situations with differing economics. Yet the fundamentals of benchmarking remain relevant, although in new, modified forms as discussed below.
•Benchmarking – peer group selection – Over the past few decades benchmarking has become a key tool in most top performing generating companies performance improvement efforts. Good technique is to first identify other “peer” plants whose design and operational characteristics are similar to the one we wish to benchmark. NERC and I have used this advanced statistical technique, simultaneously analyzing over 50 plant design and operational features, to identify peer units from and then to compare their “traditional” reliability indices (I will be discussing this technique in more detail in future posts). Benchmarking Commercial Availability will require a new aspect of the plant to be included in the analysis to determine the optimal peer group. That new aspect is some indicator of the plant’s economic incentive to generate at different times. Since the greater the economic incentive to generate is, the better the plant’s reliability can be managed to meet the demand (I will be posting a future case study indicating that management is the largest influence on the plant’s reliability), then we will need a statistic that measures the unit demand and incorporate that into the peer group analysis.
•Benchmarking – comparisons – After we have selected the best peer group for our benchmarking analysis, what will we compare? The actual calculation of Commercial Availability (whichever definition is finally adopted) is likely to be highly dependent on the precise market price (or marginal cost for a regulated or controlled business environment) that exists per hour (or parts of each hour) and matched against the unit’s availability in those hours. Since that price (or cost) can and does fluctuate widely over the course of each day, week, month or year we would have to create a massive new database containing market prices in order to make the CA calculations. Furthermore, even if we did create such a database the actual CA’s will probably not be appropriate to compare since the actual market prices in different regions would be likely to be very different. One of the approaches I advocate is to calculate a term called Conditional Probability (CP). CP represents the likelihood (that’s the probability part) that the unit can deliver the requested amount of energy during a specified time period corresponding to that unit’s demand profile (that’s the conditional part). CP, then, would be similar to the Equivalent Forced Outage Rate (demand) statistic but would likely be different during different demand periods. So what we would be doing would be to “benchmark” Conditional Probabilities of peer units and then select a goal CP as perhaps the best quartile or best decile or “Optimal Economic Availability” from the CP distributions of the peer units. Combining the goal CP and our unit’s unique economics we can then arrive at a “goal” Commercial Availability objectively without having to create any new data collection processes. In a future case study I will describe the specific steps to develop this process.
•Maximizing Commercial Availability – this focuses ones attention on being available to generate when required by the market and when the income and profit potential is highest. Generating units are only maintained and manned to meet market need. The logical converse of this is that stations need not be maintained and manned at the same levels for periods when they are not required by the market. The daily, weekly, and annual variations in demand for electricity means that it may be possible to reduce generating costs by allowing the units to remain unavailable overnight, at weekends, and for certain parts of the year. The plant is not required by the market and although technically unavailable, such periods have no effect on Commercial Availability.
•Design – New plant design is likely to be affected since we are no longer concerned with maximizing traditional measures of availability or reliability, but in maximizing profitability (or minimizing cost). One outcome of this different design philosophy in some cases will be to reduce the dependency on expensive equipment redundancy and instead install advanced equipment monitoring equipment. Since we are only interested in being available “when the plant is needed”, being able to better anticipate imminent equipment problems will give needed flexibility to plant management. Furthermore, even if we cannot control the timing of the event, communication of the increased likelihood of an outage will allow others in the organization (dispatch, trading, marketing, etc.) to take appropriate steps to minimize the financial impact of the outage. Operational “flexibility” also needs to be considered in design. With the addition of advanced control systems and online performance optimization tools it is possible to increase the plant’s capability to meet demanding load schedules, ramp rates, etc., thereby increasing the potential for sale of additional MW-HRS without compromising plant availability. In addition, since different regions have different economic conditions, the optimal economic design is likely to be different.
•Other implications – There will be many other implications associated with adopting Commercial Availability including modifying the overall goals system for the plant to include the financial impacts of other performance parameters such as efficiency, Operations and Maintenance costs and environmental impacts, fuel quality, capital costs, etc. Decision analysis tools using information scattered throughout the organization are needed that combine the technical consequences of various courses of action with their economic impact on the corporate bottom line to give the decision maker all relevant information they need to make the best decision. Finally, it is necessary for the industry to recognize one likely result of using Commercial Availability in place of the traditional indices; that is, these traditional measures will almost surely appear different. All stakeholders including regulatory agencies, financial institutions, insurance carriers and even the company’s own executives, board members, stockholders and customers must be included in the change process and “buy into” the new metric. Otherwise, how can we expect them to believe that although the measures they are used to monitoring are no longer important the company is actually delivering a lower cost and more profitable product?
•Goals Systems using Traditional metrics or Commercial Availability – It is my opinion that any company considering using Commercial Availability, however it is calculated, must decide to use either the traditional metrics or CA. You can’t use both in a goals system as there will often be conflicting decision options that will give different (sometimes radically different) performance results (my next posting will demonstrate these differences in a sample case study). Of course you might monitor and compare both sets of metrics before deciding which one to use but once you decide you will have to commit to one or the other.
•Calculating Commercial Availability – There are many different versions of calculating Commercial Availability (CA) and the industry has not yet settled on one definition. However, most companies measuring Commercial Availability use some version of the ratio of actual gross margin (or reduced cost) that the plant delivered relative to the total potential gross margin (or reduced cost) if the plant had been able to deliver every MW-HR that was required of it. I will use the following equation to demonstrate the concept:
CA = ((Actual Value) / (Potential Value)) X 100%
The following example is for a random sample of 10 hours during a typical year for a mid-merit generating unit. Hours 1, 2, 3 &4 are hours of mid expected value (perhaps weekdays during the non-peak season), hours 4, 5, 6, &7 are hours of high expected value (weekdays during the peak season) and hours 9 & 10 are hours of low expected value (perhaps weekends). These values actually reflect the magnitude of the “gap” between the market price of power (or the marginal cost for regulated companies) and the unit’s variable cost to produce power. So in addition to seasonal variations in the gap due to demand conditions there will also be variations due to supply conditions (e.g. many units suffering unplanned outages, etc.). Plus we can expect substantial volatility in the value during many of the hours in any particular season.
EXAMPLE 1 – 300MW mid-merit fossil steam unit
Hour Market Unit Cost Gross Margin EAF Gross Margin
Price Potential Actual
$/mwh $/mwh $ % $
1 40 30 3000 100 3000
2 25 30 0 100 0
3 35 30 1500 0 0
4 50 30 6000 100 6000
5 70 30 12000 100 12000
6 110 30 24000 100 24000
7 90 30 18000 100 18000
8 60 30 9000 100 9000
9 20 30 0 0 0
10 25 30 0 0 0
Total 73500 72000
During these 10 hours the unit was available for 7 hours so that its EAF was EAF = (7/10) X 100 = 70% and its EFOR was EFOR = (1/8) X 100 = 14.3%, indicating poor performance.
However, its CA was CA= ($72000/$73500) X 100 = 98% with only $1500 of lost margin, indicating great performance.
If you were the plant manager how would you want to be measured and evaluated, especially since your actual economic performance connects directly to the company’s bottom line?
If I were to assume that all 10 hours were available except hour 6 where the margin is highest then EAF would have been EAF = (9/10) X 100 = 90%); a good “looking” result. But CA would have been
CA= ($73500 – $24000)/$73500) X 100=67.3% and the lost margin would have been $24000, a very bad result.
We can easily see that CA is much more closely linked to the company’s bottom line financial goals that the traditional metrics of either EAF or EFOR and the plant management must find ways to maximize the chance that the unit is available when it has the most value to the company such as using condition monitoring equipment to give forewarning of imminent outages and to use other data analysis techniques such as programs to avoid, detect and mitigate High Impact – Low Probability (HILP) events as I discussed in my second case study posted earlier on my website.
Benchmarking Commercial Availability – One of the problems of Commercial Availability is that the resulting numbers are not comparable between all similarly designed units due to the fact that each individual unit will likely be operating in different economic business environments. Therefore, how will we know if a plant manager that achieves a certain level of CA deserves a pat on the back or a kick in the pants?
We can benchmark CA using a term called Conditional Probability (CP). By dividing the year into different demand periods when there are likely to be different optimum economic levels of reliability (see my first case study posted on my website), we can develop probability distributions of peer units being able to deliver generation. By superimposing our own actual (or forecast) economics onto a CP goal, we will then be able to develop a CA benchmark (or goal) for our unit operating in our unique business environment.
Conditional Probability (CP) can be defined as
1) When required (that’s the conditional part)
2) What is the likelihood (that’s the probability part) that the unit will be able to generate at its rated capacity?
These Conditional Probabilities (CP) can be used, regardless of the definition of Commercial Availability you choose to use.
Selecting a goal from these distributions (companies often choose the best quartile reliabilities of their units’ peers) during different demand periods is the starting point for benchmarking Commercial Availability. We can get these distributions from the NERC-GADS database for our unit’s technical peers by using 1 – EFORd (demand EFOR).
For our sample 10 hours I have chosen as reliability goals:
1) 92% for hours 1, 2, 3 & 4;
2) 98% for hours 5, 6, 7 &8;
3) 90% for hours 9 & 10
By multiplying each hour’s Conditional Probability Goal (CPG) by that hour’s Gross Margin Potential we can calculate that hour’s Gross Margin Goal (GMG). Summing each hour’s GMG will give us the total GMG for that time period.
Note: If the unit provides other value to the company in addition to its gross margin (such as ancillary power, etc.) that value should be included.
Hour Gross Margin Conditional Probability Gross Margin
Potential Goal Goal
$ % $
1 3000 92 2760
2 0 92 0
3 1500 92 1380
4 6000 92 5520
5 12000 98 11760
6 24000 98 23520
7 18000 98 17640
8 9000 98 8820
9 0 90 0
10 0 90 0
Total 73500 71400
For the example the Commercial Availability Goal would be
CA Goal = ($71400 / $73500) X 100 = 97.1%
For the first example the CA actual of 98% is above the CA goal of 97.1% but for the second example the CA actual of 67.3% is far below the CA goal. Of course this example uses only a few hours of times when the market price is high. In actual conditions with 8760 hours in the year there will be many other hours with high prices to “make up” for the times the unit was unavailable.
Setting CA goals for your power plants
1) Use some statistically valid process to identify each unit’s design and operational peers (I will be posting a case study on this subject soon).
2) Determine the peak season periods for your plants’ peer units. Then develop Conditional Probability (CP) distributions (I recommend 1-EFORd) during demand periods that are similar to yours).
3) Estimate your units’ Optimum Economic CP (discussed in my first case study posting on my website) during each demand period (many companies use the top quartile or top decile from the CP distributions of their peers).
4) Using the template shown earlier, apply those CP goals to first your unit’s forecasted economics (for planning purposes) and then backcasted economics (for actual evaluations), using whatever definition of CA you choose.
For many years the electric generating industry has been aware that traditional measures or plant reliability need improvement, especially for cycling and peaking types of technologies. However, it has usually remained of academic interest to those of us closely involved in Reliability Engineering. However, new times are requiring new, more appropriate measures that link technical performance with financial results. The catalyst for this new interest in reliability measures is the evolving market-based business environment brought on by the need of our customers for lower electricity prices to help them meet the demands of the competitive global economy. In my opinion Commercial Availability, coupled with advanced decision support tools that accurately forecast future your plant’s value of availability, will result in better decision making leading to lower generation cost and higher profitability.