An optimal outcome: power plant life-cycle optimisation10 December 2013
James Lawson speaks to Marc Antoine, R&D programme manager for plant automation systems at ABB Switzerland, to find out how life-cycle optimisation for power plants can lessen risk exposure for utilities while helping to improve financial and operational performance.
Timely and reliable information is critical in making the right decisions when operating a power plant. Having initially been used as a means of maximising the performance and lifetime of critical power generation components, life cycle optimisation has since developed into an online plant-management tool capable of real-time, automatic operation.
"Lifetime optimisation originally developed to allow operators to look at component lifetime as a cost factor in plant operations," says Marc Antoine, ABB Switzerland's R&D programme manager for plant automation systems. "Starting up boilers and turbines quickly puts more stress on them and shortens their service life. Using optimisation tools to work within the allowable margins of the component lets you start something like a boiler up much faster without destroying it."
Model predictive control (MPC) tools lie at the heart of optimisation. MPCs employ a set of algorithms (multivariate mathematical equations) to simulate the complex interactions within plant components. These models can be built in a number of ways but, unlike traditional single-input, single-output controls, MPCs can take into account multiple inputs and outputs as well as the constraints placed upon them.
So, by simultaneously solving the equations for a set of desired future outputs, it's possible to calculate the inputs needed to produce them. When optimising a process in real time, MPCs monitor the output data and, via a closed negative feedback loop, continuously adjust the inputs to move the output closer to the desired result.
That means an MPC tool like ABB's BoilerMax can accurately predict how a boiler will respond to certain inputs, based on its knowledge of the processes and the constraints involved: combustion, fuel turbulence, maximum permissible loads of critical thick-walled components or minimum flow rates in steam tubes to avoid high thermal stresses. It can then calculate and automatically manage the optimum start-up sequence based on the balance of outcomes desired by the operator, such as start-up time versus start-up and lifetime costs for example.
"MPC techniques go way beyond traditional control techniques," says Antoine. "They add a new dimension by being able to predict the consequences of control actions, and so are able to react and correct in an optimal way."
The main goal in using MPC tools at component level is to reduce process variations. This gives improved process stability and reliability, and reduced thermal-cycle stress on high-pressure parts.
With reduced variance, the power generation process can be operated closer to the optimum level, which means safely running closer to a constraint like maximum steam flow or generator power. For example, a higher steam temperature may allow improved heat rate, higher generation capacity and lower emissions - or help start a boiler faster.
ABB quotes savings of 10-20% on normal fuel and auxiliary power costs by using MPC techniques to optimise start-up. Combustion optimisation - distributing fuel and air in a boiler to minimise emissions (particularly NOx) while improving combustion efficiency - is also a common usage of MPC techniques. Other applications include main and reheat temperature control, and boiler-turbine coordination.
To obtain the desired load profile at plant level, optimisation applications manage many MPCs to coordinate the control of multiple boilers, fuels, turbines, steam headers and power flows to and from the grid. According to ABB, coal plants using MPC technology have seen NOx reductions of 8-40%, while generating tens of GWh-year of additional electrical energy with the same fuel consumption.
"Traditional optimisation systems help to formulate day-ahead plans for power production and trading, determining the plant's most economical load profile by balancing generation costs and the costs attributable to decreased service life of critical components against the revenue from energy sales," says Antoine. "Optimisation tools run on top of plant information management systems (PIMS) and interface with trading systems."
Risk can be factored into the optimisation calculation too, typically where keeping a minimum cost is the goal. Examples might be the risk of an unplanned outage or of not being able to connect to the grid after a plant shutdown.
Optimisation systems also support condition monitoring, helping to detect problems early and isolate their causes. That might mean detecting performance losses in thermal equipment or alerting operators to vibration problems in rotating machinery. This practice can improve plant efficiency by reducing fuel costs and avoiding unnecessary shutdowns.
An allied function is component lifetime prediction and monitoring. Rather than adhering to fixed maintenance schedules, these systems can calculate the service life consumption for key components based on their operating modes (for example, by taking account of high temperatures and pressures that reduce service life) and accurately predict when maintenance is required.
The growth of renewables has placed new demands on utilities and their optimisation systems. With an increasing number of smaller generating units on the grid, power production needs to be replanned frequently throughout a day. To support the grid in response to fluctuations, today's optimisation tools must be able to ramp up supply or to shed load in seconds, or even fractions of a second.
"Renewables have changed optimisation from long-term planning to a more real-time, intra-day approach," remarks Antoine. "Quickly controlling load response based on the contributions from sources like wind and solar is the major challenge for optimisation today."
Powerful hardware and improved mathematical techniques allows 'online' optimisation tools like ABB's OPTIMAX PowerFit range to manage extremely complex processes, such as secondary frequency control, in real time. To maximise performance, the entire optimisation system runs on a single PC connected directly to the plant control systems. Human operators now only supervise, rather than manually transferring optimisation results to plant control systems as they did in the past.
"The operator must be able to intervene if required," says Antoine. "They can also adjust bounds or constraints using regular process graphics and immediately see the effect on the optimisation results on the same operator screen."
Today, online optimisation applications are able to control whole fleets that combine different types of generation as well as individual plants. In the former case, there will be an optimisation hierarchy, with local systems for each plant controlled in turn by a master system that optimises the whole network.
With energy storage a critical enabler of efficient renewable generation, the ability of optimisation applications to make decisions based on desired future outcomes is extremely helpful. As part of intra-day optimisation at a municipal heating plant, an application might calculate when it is best to generate power and how much of it to divert to buffers, pump storages or batteries, based on predicted future demand, current supply, lowest overall cost of energy and many other criteria - as conditions change, optimisation tools can quickly re-plan how much energy to produce and to store.
"At a CHP plant, the local optimisation tool might have a target of high efficiency but also to balance the amount of power and heat generated," says Antoine. "For district heating, it needs to predict what to do with the stored energy and when to generate."
Optimisation looks like it will take advantage of the demand management options offered by tomorrow's smart grids, able to adjust supply and demand based on shifting goals like efficiency, cost and minimum supply amounts.
"Optimisation is now a standard requirement from our customers and is used even at the smallest plant," concludes Antoine. "It's a well-proven technology and, with its ability to manage renewable generation, the incentives to fully exploit optimisation are now stronger than ever."