Digital transformation for the betterment of your power plant can be a daunting task. Check out our key insights from a digital leader and industry expert
By Eric Kauffman
In our last article we talked about driving business value rather than digital jargon. Digital technologies for power plants, and in almost any application, will generally take 3 forms:
- Decision support … analytics and data insights that help humans make better decisions. These are computer-to-human interactions. Examples include navigation systems, payment reminders, and even online dating applications.
- Closed loop controls … plant automation and optimization. These are computer-to-computer interactions. Examples include your car’s engine control system or your home’s HVAC control.
- Protections … cyber security, safety systems. While these can fit into the above as computer-to-human or computer-to-computer, this article will consider these as a cost of doing business. Like Wi-Fi-enabled doorbells, credit monitoring, and anti-malware, we wish we lived in a world where we didn’t need these things, but we accept them as part of modern life.
Power plant economics are unique
For power plants, it’s important to consider economics that are specific to the industry. Both regulated and deregulated power plants will make money in at least one of these ways:
- Energy payments in terms of dollars per MWh produced, tolling would be included here for plants that are paid to produce electricity and fuel is provided by the off-taker
- Capacity payments in terms of MW capacity and hours of availability
- Ancillary payments in terms of flexibility to respond to emergent grid stability needs
So how do the above digital technologies connect to the ways that power plants make money? Right off the bat, protections in and of themselves only have an indirect connection to plant economics. They keep the plant safe and can avoid larger downtimes caused by reliability issues or nefarious sources.
Closed loop controls can make the plant more flexible and run at a more optimal point saving fuel and positioning the plant for a greater share of the market. They can also reduce the risk of human error and associated risk of trips or undue wear and tear.
Decision support analytics can be used by traders, operators, buyers, and executives. Analytics can be used to determine the right amount of fuel to purchase, provide a more accurate projection of plant capacity and heat rate at different load points and ambient temperatures, estimate day-ahead pricing, startup duration, and projected load profiles. All of these can be used to eke out additional profits for each MWh produced and help position the plant for a greater share of the energy and ancillary markets.
Start with Sutton’s Law
Oft quoted, Sutton’s Law says, “Go where they money is.” This begs the question of, “How does your plant make money?” If your plant has a power purchase agreement (PPA), then participating in the day-ahead market is not a priority. If your plant is in an area of overcapacity, however, it can provide critical grid-response, then perhaps improving flexibility is critical.
It’s also important to note that there is no dividing line between regulated and deregulated utilities when it comes to how the plant makes money. Many readers will immediately balk at this because they are familiar with regulated utilities that have the luxury of putting their capital investment into the rate base while selling the electricity at basically cost of production. This is true for many but certainly not for all. Many regulated utilities pay for their own fuel and are expected to cover their costs to remain solvent. Because their base costs are fixed, they must have competitive efficiency in order for the price of sold electricity to exceed the fuel cost. Further, they must run a sufficient number of hours in order for the total electricity revenues minus the fuel costs to cover their base costs. Essentially, they will behave like a deregulated utility.
How Digital can play
Given knowledge of how a plant makes money, the next step is to understand the plant itself as an asset. By what metric is that plant deemed “good at its job”? Further, what business processes are involved in the use of that plant that can affect the plant’s ability to make money?
Ultimately, the answers to these questions will lead to a valuable investigation on how automation, decision support, and even cyber security can have a positive effect on the economic value of that power plant. Here are a few examples:
- Automation – to reduce startup times and fuel costs
- Decision support – to improve day-ahead offers, optimize the use of power augmentation, reduce downtime, or provide diagnostic information on thermal efficiency
- Cyber security – to help ensure downtime is minimized in a critical operation
In all of these examples, the key is to match the investment with the return. A valid business case that takes into account the plant economics, and the expected trends in plant operation, is critical to the decision to move forward with a digital transformation – even if it’s only for one business process. One of the reasons for this is that all process improvements begin with a disruption of the previous process. Human factors and change management will come into play.
In the absence of a strong and easily explained business case, these initiatives will fizzle out and create lasting impressions potentially hindering any future transformation efforts. However, combined with a strong and easy-to-understand business case, a targeted digital transformation will give your plant a competitive advantage and a longer, more profitable economic life.