POWER June 2022 - 41

PLANT FLEXIBILITY
cal, but tried and true. Hourly operating
profile data from a utility located in the
Midcontinent Independent System Operator
(MISO) footprint provides a reallife
example of
the
1. Predictive load control adjusts actual output to match the desired setpoint more quickly,
which, among other things, can provide additional revenue, resulting in a positive return on
investment (ROI) in less than one year. Courtesy: Emerson
of the control strategy itself, checks all
the boxes. Embedding intelligence within
the control strategy makes it possible
to predict-and proactively control for-
changing operational dynamics. This, in
turn, translates into quicker response to
combustion turbine output changes, and
consequently, improved performance.
This is evident by comparing the challenges
of using conventional PID controls
(above) with intelligent model-based predictive
control as described below.
Advanced Drum Level Control. Model-predictive
control anticipates changes
in the rate of steam production based on
energy changes from the combustion turbine
or duct burners. It allows the control
solution to steer predictively through transient
shrink or swell dynamics while maintaining
a high degree of robustness, even
in single element and startup modes. Implementing
advanced drum level strategy
supports higher overall unit ramp rates
with improved stability since the control
solution is no longer constrained to chasing
transient level impacts.
Steam Temperature Optimization.
Advanced steam temperature control
anticipates changes in steam production
from the combustion turbine or duct
burner exhaust energy, and proactively
responds to changing process conditions
for more stable superheater and
reheater temperature control. This helps
optimize efficiency by keeping steam
temperature closer to its maximum efficiency
point during transient and cycling
operations while minimizing risk of high
temperature
excursions. Steam temperature
optimization has a significant
impact on operations and resiliency, typically
offering a return on investment in
less than one year.
SCR Optimization. There is often significant
dead time when measuring NOx
,
which poses a major challenge for traditional
PID control. However, predictive
models can adjust for this by determining
the quantity and timing of ammonia
injection. This approach better maintains
the desired NOx
removal and helps to ensure
emissions compliance throughout a
more dynamic load profile. When optimiJune
2022 | POWER
zation software is layered on the predictive
control, the resulting solution steers
to hourly or daily constraints, minimizing
overall reagent usage.
Response Optimization. Predictive
load control recognizes how and when
steam turbine megawatts will be produced
based on changes to combustion
turbine exhaust energy and duct burner
output. This makes it possible to achieve
the desired overall unit output to setpoint
more quickly while minimizing the control
actions necessary on the combustion turbines
or duct burner output. This reduces
wear on plant equipment, better supports
grid operations, and can provide additional
revenue in markets where applicable.
Implementing response optimization software
also enables units to better engage
and disengage with a market based on
economic conditions, often allowing utilities
to realize a return on investment in
less than a year (Figure 1).
Duct Burner AGC. Predictive control
strategies enhance duct burner operational
flexibility. Its multivariable control ability
intelligently couples both the combustion
turbine and duct burner load management
into a single optimal load controller.
Duct burner AGC software provides both
plant operations and power marketing the
flexibility to make economic dispatch decisions
with a single AGC front end.
Ramping Up Results
One of the derived benefits being realized
from plant operators engaging in
these solutions is the overall support they
provide toward achieving a more autonomous
level of plant operations. Applying
a better technical solution in these critical
areas allows the process to remain under
automatic control over a wider operational
range with improved stability,
reducing the level of necessary operator
interactions. This frees up limited plant
operations staff to focus less on the moment-to-moment
conditions and more on
higher-level objectives, thereby leading to
improved overall plant reliability.
It's important to remember that the
benefits that can be derived from modelpredictive
control are not simply theoretiwww.powermag.com
operating
profile
changes
experienced by some sites that
can be positively impacted by adopting
advanced control solutions, such as
model-predictive control.
The online version of this article includes
an illustration showing the operating profile
for a combined cycle unit that experienced
significant changes between August 2020
and December 2021. The graphs show
the unit was mainly baseloaded in August
2020, but that changed and a " new normal "
was witnessed in December 2021,
which included an increased need to respond
to changes in system demand due
to the penetration of new variable types
of generation and evolving market conditions.
Through adoption of the solutions
described in this article, the site adapted
to the operating profile changes while improving
the overall control performance in
critical process areas.
Today's traditional power plants are
actively asked to increase and decrease
load in real time in response to renewable
energy variability on the grid. In this
energy landscape, it's imperative to find
ways to increase the flexibility of these
units. However, achieving better load
flexibility requires accurate management
of combustion turbine energy and emissions
by the HRSG-something that can
be difficult to optimize with traditional
PID control schemes. Model-predictive
control is a proven solution that:
■ Responds predictively to dynamic conditions
caused by constant load changes.
■ Balances-in real time-multi-dimensional
objectives and tradeoffs that are
inherent to plant operation.
■ Monitors model drift to understand
if the underlying process is evolving,
and if so, when it needs to be addressed.
Ultimately,
this embedded understanding
of response characteristics and leveraging
them can help power plant
operators improve operational flexibility;
optimize dispatch; increase reliability and
availability; and optimize environmental
performance of combined cycle and other
traditional power generating units. ■
-Jim Nyenhuis is manager of Performance
Consulting for the Power Industry and
Ranjit Rao is manager of Advanced
Applications and Optimization for the
Power Industry, both with Emerson.
41

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POWER June 2022

Table of Contents for the Digital Edition of POWER June 2022