Timing Your Power Plant Outage Schedule for Enhanced Efficiency and ProfitabilityPeter Kelly-Detwiler
Designing a strategic, data-driven power plant outage schedule can save money and stress.
With most generating plants, the power plant outage schedule is fairly routine and often calendar based. However, the reality is that one size does not fit all. Sometimes, an abbreviated outage is the better choice, even if it costs more. Other times, a longer outage may make more sense. Turning your 30-day outage into a 40-day outage could even be a wise decision if you don't need the power. It all depends on what you need the plant to do at the time.
There have always been significant differentiating factors depending on plant application. Plants selling wholesale power in competitive industries in the Northeast typically schedule for shoulder months when demand is low, and prices are soft. By contrast, a plant providing 24/7 steam and energy to Alberta's oil sands might run harder and provide constant output regardless of the season. Each of these facilities would therefore require a completely different maintenance regime and outage schedule.
To further complicate the issue, labor costs might affect the outage schedule. Depending on the availability of staff, a plant manager might opt for a longer outage using a lower cost single-shift skeleton crew. In a high-priced electricity market, where every hour is critical, double shifts and a more expensive all-hands-on-deck approach may be warranted.
The age and type of gas turbine unit being maintained adds yet another layer of complexity. For example, a 40-year-old Class E turbine is typically over engineered relative to its capacity rating—similar to how the Empire State Building was overbuilt. In both cases, the engineering was not as advanced as it is today. The result is that one can often run a 30- or 40-year-old turbine harder or longer prior to a maintenance break than has traditionally been considered feasible.
You can't confidently put that 40-year-old turbine through its paces or improve outage schedules without reliable data and a platform for providing useful and actionable information. You can only run a given unit harder for more profit if you confidently know the risks involved. That's where big data and digitization come in; pulling data in from sensors across the plant adds enormous value. When properly integrated, this information helps operators and fleet managers know when maintenance requirements are flexible or essential, as well as how to respond to consumer demand. Newer, more finely engineered plants also benefit from more data transparency.
In older units, the existing control system may run the plant perfectly well but may not have the processing power necessary to utilize the data required to make the best decisions. These plants require a retrofit involving a separate central processing unit that handles the data in parallel and feeds appropriate commands into the existing control system.
With the right data and predictive algorithms, operators can determine the best maintenance scheduling in terms of both timing and duration. These data include external information such as projected weather and market prices, fuel costs, contract terms, and maintenance history. They also include large volumes of plant-specific, asset-level data, including temperatures, vibrations, and response times.
At the end of the day, managing the power plant outage schedule is similar to other aspects of operating a power plant in that it is ultimately about risk and reward. To properly make these determinations, an information-rich systemic conversation among operators, fleet managers, and traders must occur.
Today's data-driven evaluation is a far cry from the days when siloed trading groups might call for the plant operator to run a stop–start cycle multiple times in a day with little regard for the wear and tear of the plant (or increased fuel costs). This outdated practice often increased the need for maintenance and advanced the outage schedule. Today, decisions and compromises between parties can be made with fuller knowledge and greater confidence.
More data and better dialogue between stakeholders results in more options for operators. For example, you may be able to run the plant harder and deliver more megawatt-hours into the market when spark spreads are high, while pushing maintenance out for a few weeks. Or you might opt to change the power–steam balance in a combined heat and power unit based on the needs of the business.
Balancing business needs with plant maintenance decisions is critical to mitigating risk while increasing profitability. The results have been significant: Strategic plant outage schedules and more efficient plant operations can easily result in improved efficiencies, especially in plants that are decades old. In large plants, this can potentially translate into millions of dollars on an annual basis.
At the end of the day, flexibility is critical. The more products and megawatt-hours you can sell and the more reliably you can deliver services to the customer, the greater your profits will be. Maintenance is a vital element of this picture, and outage schedules can be flexible, as well, as long as the related decisions are supported by data and the trade-offs are clear.
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