Plant optimization

Proven ways to improve operational and financial performance

Timely and reliable information is critical to making the right decisions when operating a power plant. During recent years, optimization tools have matured and are used to create solutions that improve financial and operational performance and reduce risk exposure for utilities.

Optimization allows operators to look at component lifetime as a cost factor in plant operations. For instance, starting up boilers and turbines quickly puts more stress on them and reduces their operating life. Using optimization tools to work within the allowed margins of the component makes it possible to  start a boiler or turbine much faster without damaging it.

Balancing input and output
Model predictive control (MPC) tools lie at the heart of optimization. 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. Unlike traditional single-input, single-output controls, MPCs can take into account multiple inputs and outputs as well as the constraints on them. So by simultaneously solving the complex equations for a set of desired future outputs, it is possible to calculate the inputs needed to produce them. When optimizing a process in real time, MPCs monitor the output data and continuously adjust the inputs to move the output closer to the desired result.

That means that 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, maximum permissible loads of critical thick-walled components, or minimum flow rates in steam tubes to avoid high thermal stresses. As a result, it can 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 lifetime costs.

Reducing process variations
MPC techniques go way beyond traditional control techniques. They add a new dimension by being able to predict the consequences of control actions, and 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 optimal 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 through using MPC techniques to optimize start-up.

Combustion optimization – that is, distributing fuel and air in a boiler to minimize emissions (particularly NOx), while improving combustion efficiency - is also a common application. Other applications include main and reheat temperature control and boiler-turbine coordination.

Multi-MPC management
To obtain the desired load profile at plant level, optimization applications manage many MPCs to coordinate the control of multiple boilers, fuels, turbines, steam headers and power flows to and from the grid.

Load scheduling optimization determines day-ahead plans for power production and trading. It calculates 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.

Risk can be factored into the optimization calculation too, typically where minimal 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.

Condition  monitoring  supported
Optimization 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. Condition monitoring improves plant availability by avoiding unnecessary shutdowns.

An allied function is component life-time prediction and monitoring. Rather than adhering to fixed maintenance schedules, optimization systems can calculate the reduction in service life for key components based on their operating modes (for example, taking account of high temperatures and pressures that reduce service life) and so accurately predict when maintenance is required.

Integrating renewables
The growth of renewables has placed new demands on utilities and their optimization systems. With many  more small generating units on the grid, power production needs to be continually re-planned. To support the grid in response to fluctuations, optimization tools must be able to ramp up supply or to shed load in seconds or even fractions of a second.

Powerful hardware and improved mathematical techniques allow ‘online’ optimization tools like ABB’s OPTIMAX PowerFit to manage extremely complex processes like secondary frequency control in real time. To maximize performance, the entire optimization system runs on a single PC which is connected directly to the plant control systems. Operators now only supervise, rather than transfer optimization results to plant control systems manually as they did in the past.

Operators, however, must be able to intervene if required. They can also adjust bounds or constraints using regular process graphics and immediately see the effect on the optimization results on their operator screen.

Today, online optimization applications can control individual plants or whole fleets that combine different types of generation. Optimization is now a standard requirement from customers and is used even at small plants. It is a well-proven technology and, with its ability to manage renewable generation, the incentives to fully exploit optimization are now stronger than ever.

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