POWER June 2022 - 40

PLANT FLEXIBILITY
Optimizing Power Plant
Load Flexibility
The operating profiles of traditional generators has changed to manage the variability
of renewable resources. Several critical processes were not engineered to manage
these highly variable operating profiles and the associated transient conditions in
an ideal manner, which has a negative impact on efficiency and reliability. Operators
should consider applying field-proven advanced model-predictive control solutions
to these process areas.
Jim Nyenhuis and Ranjit Rao
tilities today are seeking to
U
diversify
their energy portfolios by
increasing production from wind,
solar, and other renewable sources.
However, the intermittent nature of renewable
generation can be challenging
for traditional generating assets, as they
must operate differently than intended at
the time they went into service.
Take combined cycle units, for example.
They are typically designed around steadystate
conditions, meaning that operating
performance targets are met when all
the major components of the process are
aligned. Initial design efforts typically focus
on a unit's full-load output condition; unit
dynamic control is a secondary priority.
This is problematic. With an increase of
renewables being added to the generating
mix, these units are now spending more
of their operating life in a transient or dynamic
condition, often operating at lower
minimum loads and higher ramp rates.
In this new reality, there is a need to improve
operational flexibility so that plants
can more efficiently move through load
changes while maximizing dynamic performance
of plant processes and equipment.
Traditional control has relied on the
ubiquitous PID (proportional-integral-derivative)
controller algorithm, sometimes
combined with a feed-forward mechanism.
This type of control, when tuned
and calibrated, provided a reasonable response
to changing power demand, usually
requested as long ramps between
minimum and maximum limits.
However, the evolving operation profiles
are often unpredictable and require
these plants to ramp faster and " turn on
a dime. " Due to the high-order nature and
inherent transport delays of some of the
key processes, such as steam temperature
control and duct burner load generation,
a PID controller is often unable to
provide the robust control required. This
is due to the nature of the mathematics
40
associated with the algorithm and its subsequent
ability to only generate a very approximate
" model " of the process. Even
with feed-forward mechanisms, PIDs lack
the predictive, multivariable, and optimal
control nature of modern model-predictive
control algorithms and are often detuned
to ensure system stability at the
cost of dynamic response.
PID-Based Control Limitations
The key process areas where PID-based
control limitations have been identified
follow.
Drum Level. HRSG (heat recovery
steam generator) drum level control has
always been a challenge due to shortterm
dynamics like level shrink and swell
related to quick load (steam flow) changes
that cause sudden pressure transients. If
the PID controller is tuned using conventional
level control methodologies, shrink
and swell can cause severe controller upsets
because these dynamics are counterintuitive
to the level controller, forcing
the controller to be de-tuned. Three element
control is often cited as the solution,
where the mass balance between inflow
and outflow is the primary control element
and the level control PID acts only
as a trim and is de-tuned to ensure stability.
However, for three element control to
be used, there needs to be consistently
measurable steam and feedwater flows.
This, unfortunately,
is not always the
case, especially on the intermediate pressure
drum (smaller volume) of the HRSG
as it often displays large disturbances of
steam flow, in some cases even zero, during
startups and load direction changes.
Steam Temperature Control. Operators
sometimes run at lower-thandesign
steam
temperature
setpoints
or
lower ramp rates to avoid exceeding
high steam temperature limits that could
stress or damage the metallurgy in the
piping, steam turbine, and other components.
Operating heat rate is negatively
www.powermag.com
impacted because the process is not
running consistently at design temperatures
and maximum efficiency. Steam
temperature
process
time
constants,
often on the order of minutes, challenge
conventional controls as they seek to
chase the temperature impact of energy
as it ebbs and flows to and from the process
as load demand changes.
NOx Control. Traditional process
control implementations for NOx
rely on
open loop design basis calculations following
HRSG inlet NOx
with a PID controller
to provide trim control on the stack
NOx
. The inherent NOx
measurement delay,
potentially higher order reaction dynamics,
and process noise all challenge
traditional tuning and PID control.
Load Control. Long process delays
change the combustion turbine exhaust
energy, duct burner output, and the associated
steam production, which increases
steam turbine megawatt output.
This can force either additional demand
changes to the combustion turbine or detuning
of the overall load management
scheme to maintain the desired stability.
Duct Burners. Often, duct burner
management is not closely coupled in
automatic with the overall AGC (automatic
generation control) scheme. This is
because in addition to the high order nature
of the load response, a multivariable
control problem is presented in the form
of managing HRSG steam/tube temperatures
alongside the associated steam
production, which is driven by duct burner
modulation response to load demand.
Finding Flexibility with
Model-Predictive Control
With plants operating differently, it
makes operational and economic sense
to consider a solution that overcomes
the inherent limitations of conventional
PID-based control. Model-predictive control
software, in which a model of the
transient response of the process is part
POWER | June 2022
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POWER June 2022

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