The energy systems efficiency is a great issue confront power plants professionals. Besides using high-tech components in power plants, plant operation optimization can significantly improve energy efficiency and economic performance, as efficiency of plant components generally depends on operating conditions. In addition, system preventive maintenance can reduce plant operation and failure costs, however it is also costly when done frequently. Therefore, optimizing operating conditions and preventive maintenance intervals can minimize the expected total cost of plant due to operation, failures and preventive maintenances. In recent years, the use of optimization models to determine plant optimal schedule has earned popularity. Scheduling is widely used to maintain and establish operating conditions and maintenance intervals of a plant over time. It should be noted that power plant components are degraded through long term operation. Therefore, components performance profile over time varies. To have a reliable and accurate scheduling optimization results it is necessary to consider components ageing models in the optimization procedure. The combination of plant optimal scheduling and aging models is an extended approach that is presented in our study and a framework is developed. In this presentation a literature review on the topic is presented.

1. Degradation Based Framework for Long-Term Analysis
and Optimization of Energy Conversion Systems
By:
Tarannom Parhizkar
1
December 2020

2. 2Introduction
Introduction

3. 3
Performance
Time
Degradation mechanisms
Introduction Literature review Framework Application & results Conclusion
Diagnosing degradation mechanisms, helps to define energy conversion system
performance more accurately.
Degradation effects

4. Type of degradation Typical causes
Recoverable deg Clogging, scaling and build up of deposits on the working
surface
Non-recoverable deg Tear, loss of working surface, corrosion/oxidation, erosion.
A gradual and irreversible accumulation of damage that
occurs during a system’s life cycle. This process is
known as degradation
4Degradation definition
Introduction Literature review Framework Application & results Conclusion

5. 5
Investment cost
Operation cost
Maintenance cost
Income
Income
timet1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
timet1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12
Cost
Introduction Literature review Framework Application & results Conclusion
Degradation long term economic effects

6. 6
Operating conditions have effect on energy conversion
components degradation rate.Performance
Time
Introduction Literature review Framework Application & results Conclusion
Research necessity
Different environ
and operational co

7. 7Research objective
Life time cost
DAMAGE
Life time income
$ $
Introduction Literature review Framework Application & results Conclusion
Developing the framework of “degradation based optimization (DBO)” model by
optimizing system operating conditions

8. 8Literature review
Literature review

9. Literature review classification
• Degradation based process model
Literature review
Process Model
System output
(Power, Heat,…)
System input
( Fuel,…)
9
Introduction Literature review Framework Application & results Conclusion

10. Literature review classification
• Degradation based process model
Literature review
Process Model
System output
(Power, Heat,…)
System input
( Fuel,…)
10
Introduction Literature review Framework Application & results Conclusion
Degradation mechanisms

11. Degradation based process models
R. Zhou1 et al., 2011.
Degradation modeling attempts to characterize
the evolution of degradation signals.
A gradual and irreversible accumulation of damage that
occurs during a system’s life cycle is known as degradation.
The observed condition-based signals from Condition
Monitoring process are known as degradation signals.
1.
2.
3.
11
Introduction Literature review Framework Application & results Conclusion
Degradation definition

12. S. Bae et al., 2008.
Data-driven model Principle based model
Degradation models
12Degradation based process models
Introduction Literature review Framework Application & results Conclusion
Degradation model classification

13. 13
Authors System Purpose Methodology
Gebraeel et al
2005
Framework
development
Residual lifetime prediction Principle based model
Y. Zhao
2005
Gas turbine power
plant
Performance deterioration Data-driven model
Haschka et al
2006
SOFC Voltage degradation Data-driven model
Bae et al
2010
Degradation rate
functions development
Degradation rate functions Data-driven model
Ryana et al
2012
SOFC
Sulfur impurity effect on the
performance
Data-driven model
Kappis
2013
Compressor
Degradation effect on
performance in different
ambient temperatures
Principle based model
Degradation based process models
Introduction Literature review Framework Application & results Conclusion

14. 14
Authors System Purpose Methodology
Rujian Fu
2015
Lithium ion polymer
batteries
Battery capacity fade cause Principle based model
M. Chandesris
2015
PEM water
electrolyzer
Influence of temperature and
current density
Data-driven model
Minggao
Ouyang
2016
Li-ion battery Capacity prediction
Data-driven model
( ) (0) (1 )LT t LT f= ´ -
( , , ,...)f a b g
Degradation based process models
Introduction Literature review Framework Application & results Conclusion

15. Literature review classification 15
• Degradation based optimization model
Literature review
• Degradation based process model
Introduction Literature review Framework Application & results Conclusion

16. 16Degradation based optimization models
S. K. Agrawal et al., 1999.
Dynamic optimization involves optimization over time
and state variables depend on time.
E. Bryson et al., 1999.
P. Whittle et al., 1982.
Introduction Literature review Framework Application & results Conclusion
Dynamic optimization definition

17. 17
Authors System Purpose Methodology
Gallestey et al
2002
Gas turbine Minimizing system total cost Data-driven model
Antoine et al
2002
Gas turbine Minimizing system total cost Data-driven model
Trecae et al
2005
Steam and gas
turbine
Minimizing system total cost Data-driven model
ABB Co
2006
Power plant
equipment
Different targets regarding
degradation
Principle based and
Data-driven model
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion

18. 18
Authors System Purpose Methodology
Rasmekomen et al
2013
Framework
development
Optimizing maintenance
interval regarding
degradation(cost)
Data-driven model
Kima et al
2013
PEM
Optimizing system
temperature
Data-driven model
Ziyou Song
2014
battery/supercapa
citor energy
storage system
Optimal sizing
Data-driven model
Nur I. Zulkafli
2016
Framework
development Planning of production
Principle based and
Data-driven model
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion

19. 19
Introduction Literature review Framework Application & results Conclusion
Degradation based optimization models
Overall concept
ABB company., 2002.

20. 20
Model Predictive Control (MPC) and
Mixed Logical Dynamic (MLD) approach
are used to derive the optimal conditions.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion

21. 21
E. Gallestey et al., 2002.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion
( ) ( )[ ]
t T t T
standard aging
t t
J u c q d c c q dt t
+ +
= - = + -ò ò
1
m
aging i i
i
c Value c
=
= ´å
, 1,2,...,i
i
dLT
c i m
dt
= =
( )
( )
(0)
icric i
i
icric i
a a t
LT t
a a
-
=
-
Considering aging cost

22. 22
Model Predictive Control (MPC) and Mixed
Logical Dynamic (MLD) approach are used
to derive the optimal conditions.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion

23. 23
ABB company,2005.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion
( ) ( 1)LTC AV k AV k= - -
Considering aging cost

24. 24
Model Predictive Control (MPC) and Mixed
Logical Dynamic (MLD) approach are used
to derive the optimal conditions.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion

25. 25Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion
C. Bordin et al., 2017.
( )min t t
G B
t
C C+å
B
t
C Pg dg= ´å
Power generation (kWh) Degradation cost ($/kWh)
Considering aging effect

26. 26
OptiMax, ABB company, 2006.
Degradation based optimization models
Introduction Literature review Framework Application & results Conclusion
Software in this field

27. 27Strengths and weaknesses
Introduction Literature review Framework Application & results Conclusion
• Considering aging cost in the objective function
• Considering aging effects in the optimization procedure
Summary of main strengths and weaknesses of other research
Weaknesses
Strength
• Framework
• Any energy conversion system
• Operating strategy and objective function
• Data-driven and model-based degradation model
• The effect of considering degradation in optimization
procedure with different objective functions

28. 28
References:
[1] Parhizkar, T., & Roshandel, R. (2017). Long term performance degradation analysis and
optimization of anode supported solid oxide fuel cell stacks. Energy Conversion and
Management, 133, 20-30.
[2] Roshandel, R., & Parhizkar, T. (2016). Degradation based optimization framework for long term
applications of energy systems, case study: Solid oxide fuel cell stacks. Energy, 107, 172-181.
[3] Parhizkar, T., Mosleh, A., & Roshandel, R. (2017). Aging based optimal scheduling framework
for power plants using equivalent operating hour approach. Applied Energy, 205, 1345-1363.
[4] Roshandel, R., & Parhizgar, T. (2013). A new approach to optimize the operating conditions of a
polymer electrolyte membrane fuel cell based on degradation mechanisms. Energy Systems, 4(3),
219-237.
Chicago
[5] Sotoodeh, A. F., Parhizkar, T., Mehrgoo, M., Ghazi, M., & Amidpour, M. (2019). Aging based
design and operation optimization of organic rankine cycle systems. Energy Conversion and
Management, 199, 111892.
Chicago

29. 29
The key is not to prioritize what's on your
schedule, but to schedule your priorities.
Stephen Covey