A techno-economic analysis of implementing temperature...
Transcript of A techno-economic analysis of implementing temperature...
A techno-economic analysis of
implementing temperature-maintaining
modifications on the steam turbine of a solar
thermal power plant
Mårten Lundqvist
Master of Science Thesis MJ232X
KTH Sustainable Energy Engineering
Energy and Environment
SE-100 44 STOCKHOLM
EGI_2016-105 MSC EKV1174
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Master of Science Thesis MJ232X
A techno-economic analysis of implementing
temperature-maintaining modifications on
the steam turbine of a solar thermal power
plant
Mårten Lundqvist
Approved
Examiner
Björn Laumert
Supervisor
Monika Topel
Registration number
EGI_2016-105 MSC EKV1174
Company Supervisor
-
Contact person
-
Abstract
This master thesis examines the techno-economic implications of introducing temperature
maintaining modifications on the steam turbine in a direct steam generation solar tower power
plant. More specifically, the impact on the maintenance requirements and other performance
indicators when installing electrical heat blankets as well as increasing the gland steam
temperature, was examined. A model of the Ivanpah plant in southern California was inherited
and further developed within the KTH in-house tool DYESOPT to then be used for sensitivity
studies focusing on examining the effect of the start improvements.
The results show that with the assumptions made, the examined start improvements can be used
to significantly increase the power output of the Ivanpah plant while at the same time reducing
the maintenance requirements. The investment costs of said improvements were also found to be
low in relation to their techno-economic benefits, resulting in a significant reduction of the
levelized cost of electricity. The conducted sensitivity studies also suggested that the assumption
made were not very sensitive, although more accurate assumptions regarding the costs of the
turbine start improvements should be looked at during further development.
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Examensarbete MJ232X
En teknisk-ekonomisk analys kring
implementering av turbinmodifikationer i
syfte att minska värmeförluster hos
ångturbinen i ett solkraftvärmeverk
Mårten Lundqvist
Godkänt
Examinator
Björn Laumert
Handledare
Monika Topel
Registreringsnummer
EGI_2016-105 MSC EKV1174
Företagshandledare
-
Kontaktperson
-
Sammanfattning
Detta examensarbete undersöker de tekniska och ekonomiska konsekvenserna av att
implementera turbinmodifikationer i syfte att undvika värmeförluster på ett termiskt solkraftverk.
Mer specifikt så studerades det hur elektiska värmefiltar samt en ökning av temperaturen på
förseglingsångan påverkar ett kraftverkets underhållsbehov samt andra prestationsindikatorer.
För att åstadkomma detta ärvdes samt utvecklades en existerande modell av Ivanpah, ett
solkraftverk beläget i Kalifornien, USA i KTHs egenutvecklade modelleringsverktyg DYESOPT.
Detta verktyg användes sedan i syfte att undersöka effekten av turbinmodifikationerna genom en
känslighetsanalys.
Resultaten visar att med de antaganden som gjorts så kan de undersökta turbinmodifikationerna
öka den årliga kraftproduktionen och samtidigt sänka underhållsbehoven betydligt. Sett till de
ekonomiska aspekterna leder detta till en minskning av den sammanlagda kostnaden för att
generera elektricitet med de antaganden som gjorts, eftersom investeringskostnaderna relaterade
till modifikationerna är låga i relation till deras fördelar. Känslighetsanalysen pekar dessutom på
att de gjorda antagandena inte var särskilt känsliga, men att fokus bör ligga på bättre underbyggda
antaganden kring turbinmodifikationernas kostnader för att kunna bedöma dess tekno-
ekonomiska effekter än bättre.
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Acknowledgement
Firstly I would like to thank my supervisor Monika Topel for her guidance and advice throughout
the entire work process behind this master thesis.
I would also like to extend my thanks to my good friends Eric Schmidt and Martin Isacsson for
taking their time reading and giving feedback on the written report.
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Table of contents
1 Introduction .................................................................................................................. 1
1.1 Objectives & research question.................................................................................................................... 3
1.2 Methodology ................................................................................................................................................... 3
1.3 Thesis layout .................................................................................................................................................... 3
2 Theoretical framework .................................................................................................. 4
2.1 Solar Irradiance ............................................................................................................................................... 4
2.2 Concentrating solar power ............................................................................................................................ 5
2.3 Existing CSP technologies ............................................................................................................................ 6
2.4 Solar towers ..................................................................................................................................................... 6
2.4.1 Direct steam generation ...................................................................................................................... 7
2.5 CSP energy conversion .................................................................................................................................. 7
2.5.1 The ideal Rankine cycle ....................................................................................................................... 8
2.6 Steam turbines ................................................................................................................................................. 9
2.6.1 Steam turbine start-up procedure ....................................................................................................10
2.7 Power plant outage .......................................................................................................................................12
2.7.1 Scheduled outages ..............................................................................................................................13
2.7.2 Forced outages ....................................................................................................................................13
2.8 Power plant maintenance procedures .......................................................................................................13
2.8.1 Preventive maintenance .....................................................................................................................13
2.8.2 Other types of maintenance strategies ............................................................................................14
2.9 The economics of maintenance .................................................................................................................14
2.10 Equivalent operating hours ....................................................................................................................14
2.11 Start improvements .................................................................................................................................15
2.11.1 Heat blankets ..................................................................................................................................15
2.11.2 Gland steam temperature increase ..............................................................................................16
2.11.3 Start improvement implications ..................................................................................................16
3 Power plant model - Ivanpah ...................................................................................... 17
3.1 The DYESOPT modeling tool ..................................................................................................................18
3.1.1 DYESOPT inputs ..............................................................................................................................18
3.1.2 TRNSYS dynamic simulation model ..............................................................................................19
3.1.3 Performance indicators ......................................................................................................................20
4 Contributions to model ............................................................................................... 23
4.1 Turbine heating and cooling .......................................................................................................................23
4.1.1 Necessary assumptions ......................................................................................................................23
4.1.2 Difference from previous model .....................................................................................................25
4.2 Increased accuracy during turbine ramp-up .............................................................................................26
4.2.1 Difference from previous model .....................................................................................................27
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4.3 Start improvements ......................................................................................................................................28
4.3.1 Start improvement strategies ............................................................................................................29
4.3.2 Difference from previous model .....................................................................................................30
4.4 Costs and benefits of start improvements ................................................................................................31
4.4.1 Gland steam temperature increase ...................................................................................................31
4.4.2 Electrical heat blankets ......................................................................................................................33
4.5 Maintenance and EOH ...............................................................................................................................34
5 Results & analysis ....................................................................................................... 36
5.1 Start curve analysis .......................................................................................................................................36
5.2 Performance indicators ................................................................................................................................39
5.3 Sensitivity analysis ........................................................................................................................................42
5.4 Break-even points .........................................................................................................................................43
6 Discussion ................................................................................................................... 45
6.1 Contributions to model ...............................................................................................................................45
6.1.1 Turbine heating and cooling .............................................................................................................45
6.1.2 Turbine start-up modeling ................................................................................................................45
6.1.3 Start improvement costs and benefits .............................................................................................46
6.2 Results & Sensitivity analysis ......................................................................................................................46
6.3 Comparisons with previous work ..............................................................................................................46
6.4 Future Work ..................................................................................................................................................47
7 Conclusions ................................................................................................................. 48
8 Bibliography ................................................................................................................ 49
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Nomenclature
Abbreviations
CAPEX Capital Expenditure
CSP Concentrating Solar Power
DNI Direct Normal Irradiance
DSG Direct Steam Generation
EOH Equivalent Operating Hours
EOHstart Equivalent Operating Hours (due to start-up)
EPC Engineering, Procurement and Construction
HPT High Pressure Turbine
HTF Heat Transfer Fluid
IEA International Energy Agency
IPT Intermediate Pressure Turbine
LCOE Levelized Cost of Electricity
LFR Linear Fresnel Reflector
MATLAB Matrix laboratory
NOH Nominal Operating Hours
O&M Operation and Maintenance
OPEX Operational Expenditures
PB Power Block
PDS Parabolic Dish System
PTC Parabolic Trough Collector
PV Photovoltaic
R&D Research and Development
SF Solar Field
SPT Solar Power Tower
STEC Solar Thermal Electric Components
STPP Solar Tower Power Plant
TRNSYS Transient System simulation tool
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Characters
Capital return factor
Desired temperature difference
Density
Time constant
Turbine casing inner surface area
Turbine casing outer surface area
Auxiliary boiler investment costs
Balance of plant costs
Contingency cost
Auxiliary boiler fuel costs
Heat blanket costs
Labor costs
Land purchase costs
Miscellaneous costs
O&M costs for auxiliary boiler
EPC ownership costs
Power block costs
Receiver costs
Service contracts costs
Site preparation costs
Solar field costs
Tax costs
Solar tower costs
Utility costs
Specific heat
Capital expenditures
Annual electrical net output
Electrical power generation
Parasitic power consumption
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Availability factor
Capacity factor
Beam radiation
Diffusive radiation
Global irradiation
Thermal conductivity
Characteristic length
Mass flow
Operational expenditures
Nominal power output
Prandtl number
Annual heat blanket energy input
Heat input into Rankine cycle
Needed heat output from auxiliary boiler
Heat rejected in Rankine cycle
Reynolds number
Ambient temperature
Turbine initial temperature corresponding to start curve
Initial temperature
Steady state temperature
Turbine temperature after cool down
Time
Cool down time (plant off-line)
Time of one annual simulation
Overall heat transfer coefficient
Volume
Work input into Rankine cycle
Work output from Rankine cycle
Plant lifetime
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1 Introduction Concentrating solar power (CSP) is considered a promising technology to supply renewable
electricity to the world’s warmer regions. In 2014, The international energy agency (IEA)
predicted that CSP would expand rapidly in the near future, from supplying approximately
0,025% of the world’s electricity then to supplying up to 11,3% of the total demand in 2050 if
appropriate support measures are taken [1]. Compared to most other renewable technologies,
CSP applications have the advantage of a lower technological risk for investors, since most of the
equipment used is based on conventional power generation. Although, they have the
disadvantage of being more expensive than other renewable applications such as wind turbines or
solar photovoltaics (PVs) [2].
Even though CSP has many similarities to conventional power plants, it still faces the challenge
of intermittent production associated with most renewables. As a result, the operating steam
cycle in a CSP plant is associated with highly variable working conditions with a high frequency
of starts [3]. Besides the obvious downside of lower electricity production due to offline periods,
this intermittency also subjects the turbines to high thermal stresses due to temperature variations
during the frequent startup phases. This causes the turbine to deteriorate at a quicker pace than
that of a conventional power plant, since the wear from frequent starts adds significantly to the
wear caused by normal operation [4].
The wear caused by turbine starts is highly dependent of the initial temperature; the lower the
initial temperature, the higher the wear caused by excessive temperature differences [5]. To
mitigate the wear caused by startup processes, it is common practice for turbine manufacturers to
specify startup curves that limit the temperature difference between the turbine metal and the
incoming steam and thereby also speed at which the turbine can reach its full load. These curves
show the turbine startup time as a function of the turbine initial temperature. This means that the
initial temperature of the turbine significantly affects both the wear caused by the start-up
procedure as well as the start-up speed [4].
This study aims to examine the techno-economic effect of implementing temperature
maintaining modifications on the steam turbine of a direct steam generation solar tower power
plant. More specifically, these modifications consist of an increase of the gland steam
temperature as well as an implementation of electrical heat blankets on the turbine. In addition to
studying these effects, an existing model of the Ivanpah plant located in California is to be
further developed with respect to these modifications as well as turbine lifetime, service needs
and related maintenance costs. Previous studies have been made regarding the effects of
implementing similar modifications, examining the potential for increased yearly power
production [2, 3]. Additionally, costs related to steam turbine operation and maintenance have
previously been estimated as a percentage of the investment cost [6], been extrapolated from
existing plants based on plant size [7] or been estimated based on parameters such as solar field
area [8].
However, within the scope of this study, a dynamic simulation tool will be used in order to
account for both direct and indirect costs related to the installation of turbine temperature
maintaining modifications with respect to maintenance activities and turbine start-up speed. An
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analysis examining the impact of implementing these modifications on the power plant’s
performance indicators will be conducted, using a configuration corresponding to a retrofit of the
Ivanpah plant.
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1.1 Objectives & research question
The objectives of this study consist of developing and improving an existing solar power plant
model with respect to maintenance requirements and turbine start characteristics. Additionally,
the study will examine the techno-economic implications of temperature maintaining
modifications on the steam turbine of the power plant, determined by a number of performance
indicators such as the levelized cost of electricity. The research question that this thesis aims to
answer can be formulated as the following:
What are the techno-economic implications of implementing start improvements in the
shape of temperature maintaining modifications on the steam turbine of a direct steam
generation solar tower power plant?
o How does the reduction of maintenance costs due to the temperature maintaining
modifications relate to the costs of said modifications?
o What impact do the costs of temperature maintaining modifications and the
reduced maintenance costs have on the levelized cost of electricity?
1.2 Methodology
The work process behind this thesis began with a comprehensive literature review regarding solar
energy and CSP in general. From there additional literature was reviewed regarding steam turbine
maintenance requirements and existing methods of improving the steam turbine start-up process.
Following the literature review, the work process continued within the KTH in-house modeling
tool DYESOPT. A power plant model representing a direct steam generation solar tower power
plant, similar to the ones described in [2] and [9], was inherited. This inherited model was then
further developed with a focus on steam turbine start-ups and maintenance requirements. In
addition to this, the existing model for implementing turbine start improvements in the form of
temperature maintaining modifications was developed in order to be able to estimate the costs of
implementing them. The further development of the model is described more in detail in Section
4.
With the inherited model being developed, sensitivity studies were conducted on some of the
implemented features with the purpose of studying the effects of these and illustrate the interplay
between the suggested improvements and the power plant’s performance indicators.
1.3 Thesis layout
Following this introductory chapter, a theoretical framework chapter will outline the fundamental
theory required for the reader to fully understand the methods used and the results obtained in
the thesis. From there, the power plant model that was used for simulations will be described,
mostly focusing on its’ most important performance indicators. The contributions to said model
made by the author will then be introduced, including justifications for the methods used and
descriptions how these contributions affect the model as a whole. Finally, results will be
presented, discussed and analyzed leading up to the conclusions.
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2 Theoretical framework Mankind has been harnessing the energy of the sun to serve its’ purposes since ancient times.
Historical finds date the first solar energy applications back to 700 BC, when mirrors were used
in order to concentrate the sunlight to make fire. However, solar power as we know in the power
generation of today was first introduced in the mid-19th century with the discovery of the
photovoltaic effect and the construction of the first solar-powered steam turbine. Following this,
research and development of solar power application accelerated rapidly from the 1950’s
following the first commercial PV cells in 1954 [10]. However, the first operational power plant
based on CSP was brought into operation in 1968 in Sant'llario, Italy [11]. Following the oil crises
in the 1970’s and 80’s, CSP also saw a rapid development within the United States which led to
the construction of the CSP-plant “Solar One” in Dagett, California in 1981. Since then, CSP
Research & Development (R&D) has been progressing at a rather quick pace and as of year 2014,
CSP- plants had reached a cumulative global capacity of 4 GW with a large predicted future
potential [1].
2.1 Solar Irradiance
The energy contained in the solar rays striking the earth for one hour exceeds the annual energy
consumption of all mankind. Which naturally makes the theoretical potential of solar power
applications immensely high [12]. The solar irradiance emitted from the sun to the earth is at a
fixed level of intensity before entering the earth’s atmosphere, where the intensity is lessened
by approximately 52,6% due to reflection and absorbtion in the atmosphere, as well as reflection
at the earth’s surface. Besides lessening the intensity of the irradiance, the process of passing
through the earth’s atmosphere also splits the irradiance into two parts, which are denoted as
beam radiation ( ) and diffusive radiation ( ) with the sum of these being denoted as the global
irradiance ( ). The relationship between these three types of solar radiation is shown in (1).
(1)
The beam radiation is defined as direct radiation having travelled straigth from the sun, whereas
the diffusive radiation consists of radiation that has been scattered in the atmosphere [13]. The
intensity at which the direct solar radiation ( ), which is usful in solar power applications, strikes
the earth’s surface is sometimes also refered to as the direct normal irradiance (DNI). The global
distribution of this solar irradiance is illustrated in Figure 1.
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Figure 1. Map of annual direct normal irradiation (DNI) around the world, taken from [14]
As one might imagine, solar power applications that harness the solar irradiance to generate
useful energy will yield more energy the higher the solar irradiance is. Hence, Figure 1 highlights
the very apparent location-dependency that characterizes these applications. It is apparent that
most sun-abundant areas are found close to the equator and in the tropical areas of the world,
which make these areas suitable for power applications from a strict irradiance point of view.
Naturally, irradiance also correlates positively with the economic viability of CSP-plants.
According to [15], a DNI of above 2000
is more or less required in order for a CSP-
plant to be cost-effective.
2.2 Concentrating solar power
CSP-plants utilize heat gathered by the means of solar collectors to generate power through a
conventional power cycle. Thereby, they differ greatly from the more common kind of solar
power that is based on PVs. Instead of using the energy in the photons to induce a current in a
semiconductor material, the CSP concept is based on concentrating the sunlight in order to
achieve high temperature heat. This heat is then used to power a Rankine cycle, which makes
CSP-plants resemble conventional power plants that utilize fossil fuels, with the main difference
being that heat source is based on solar irradiation rather than fuel combustion.
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2.3 Existing CSP technologies
Although all CSP plants are based on the same basic idea of collecting sunlight to gain high
temperature heat, the means of doing so can differ between different types of applications. As of
today, four types of CSP-plants can be distinguished, these are:
Parabolic Trough Collectors (PTCs)
Parabolic Dish Systems (PDS’s)
Solar Power Towers (SPTs)
Linear Fresnel Reflectors (LFRs)
These types differ in the method of sunlight collection, and the choice of which one to
implement largely depends on the economics of the specific case. Zhang et al. (2013) reviewed
the present CSP technologies, and their data is presented in Table 1 [12].
Table 1. Comparison between different CSP technologies, data taken from [12]
Relative
Cost Land
occupancy
Cooling water req. (L/MWh)
Thermodynamic efficiency
Operating range
(degC)
Outlook for improvements
PTC Low Large 3000 or dry Low 20-400 Limited
PDS Very high
Small None High 120-1500 High potential through mass
production
SPT High Medium 1500 or dry High 300-565 Very Significant
LFR Very low
Medium 3000 or dry Low 50-300 Significant
PTCs are currently by far the most common and most developed of the four, and are in vast
majority when comparing the installed capacity of all CSP types, followed by solar towers. But as
Table 1 shows, it is in fact solar towers that are predicted to have the best future outlook for
improvements [12]. Within the scope of this study, the modeling work will be based on a solar
tower configuration.
2.4 Solar towers
Solar towers are based on collecting heat by the means of a large number of heliostats (sun-
tracking mirrors) surrounding a tower. These heliostats reflect the incoming sunlight in order to
focus it on a solar receiver located at the top of the tower. Steam is then generated, either by the
means of a heat transfer fluid (HTF) and a heat exchanger or directly in the receiver. The steam
that is generated is then used in a Rankine cycle in order to generate electricity, which is then
transmitted to the grid. Due to the light being concentrated into one focal point, solar towers
reach significantly higher temperatures compared to the other types of CSP plants such as
parabolic troughs [16].
The higher steam temperature achieved then relates to a higher overall efficiency of the plant. A
simplified illustration of a solar tower power plant is shown in Figure 2.
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Figure 2. Simplified layout of a solar tower power plant, taken from [17]
As stated earlier, there are two main ways of generating the steam in an STPP. Either the steam is
generated directly in the receiver, or by the means of a HTF and a heat exchanger. This study will
focus on a model based on direct steam generation (DSG), which is described below.
2.4.1 Direct steam generation
Solar towers utilizing DSG utilize the mirrors to, as the name suggests, directly generate steam in
the receiver. Thereby, no other HTF is needed for the heat exchange, since the receiver itself
exchanges heat between the incoming irradiation and the water/steam. The DSG configuration
both has its’ advantages and disadvantages. On one hand, it allows for higher steam temperatures
compared to STPP’s utilizing other HTF’s, since limitations related to the thermal stability of any
intermediate fluid are removed [18]. This allows for higher efficiencies during nominal operation
as well as a simpler plant layout which, in turn, relates to lower investments costs. On the other
hand however, DSG configurations are more susceptible to transients related to solar irradiation
intermittency. This is due to the fact that no commercially available technology for storing the
absorbed heat exists for DSG plants, whereas other HTF configurations can utilize measures
such as storage tanks to increase the overall availability of the plant. Although, methods of
storing heat within DSG plants are currently being researched, one method being storing energy
as latent heat by using phase change materials [19].
2.5 CSP energy conversion
All kinds of CSP-plants can be said to consist of multiple blocks, each fulfilling a different
function related to the plant operation as a whole. In the case of a DSG-STPP, the plant can be
divided into two main blocks, a heat energy source block and a power block. The heat energy
source block consists of the heliostats that harness the solar irradiance, as well as the tower itself
and the receiver. The power block, on the other hand, consists of a steam turbine, a condenser
and pumps, which transport the condensed water back to the receiver within the power cycle.
This study will mostly focus on the latter of the blocks described, with a main focus on the
turbine of the DSG-STPP.
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2.5.1 The ideal Rankine cycle
As earlier stated, electricity is generated by the means of a so called Rankine cycle in most CSP
configurations. The Rankine power cycle is known as the ideal cycle for power plants utilizing
steam, partly due to it being a good way to handle the phase changes which will inevitably occur
when using steam within a power cycle [20, 21]. From a thermodynamic point of view, the
simplest of Rankine cycles consist of four thermodynamic states, connected by four different
reversible thermodynamic processes. Figure 3 illustrates an ideal Rankine cycle with vapor
superheating, showing these states and processes in a T-s diagram and a cycle schematic.
Figure 3. T-s diagram and cycle schematic of an ideal Rankine cycle with superheating, taken from [20]
In Figure 3, the points 1-4 denote the different thermodynamic states attained within the ideal
Rankine power cycle. The reversible processes between these states, and the power plant
components related to these processes, are listed in Table 2.
Table 2. Thermodynamic processes in an ideal Rankine cycle
States Type of Process Process description
1-2 Isentropic compression Water enters pump in liquid form and is compressed to the
operating pressure for the boiler
2-3 Isobaric heating Water from the pumps is heated at constant pressure to the
desired superheated steam conditions
3-4 Isentropic expansion Superheated steam is expanded at constant entropy in a
turbine and work is produced
4-1 Isobaric cooling Steam leaving the turbine is condensed in the condenser
The output from the cycle consists of the work produced by the turbine between states 3-4
( ) and the heat rejected in the condenser between states 4-1 ( ). The energy input for the
compression between states 1-2, , is made out of the pump work, whereas the heat input
is due to the heat needed to produce the superheated steam. It is also, for a Rankine cycle
without any reheating, possible to define two distinct pressure lines. One high pressure for the
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heating process prior to the expansion in the turbine (between states 2-3) and one low pressure
line between the turbine exit and the pump (between states 4-1).
Naturally, a fully ideal Rankine cycle is not possible to achieve in practice. To account for this,
isentropic efficiencies are usually defined for both the pump and the turbine. Additionally,
pressure drops within the boiler and condenser are usually accounted for as well.
In order to increase the efficiency of a simple Rankine cycle, several modifications to the simple
cycle layout are often implemented in real-world applications today.
The vapor used within the cycle can be heated to superheated conditions in order to
increase the turbine efficiency (already shown in Figure 3) [22]
The vapor can be reheated in order to process the steam in an additional turbine section
at a lower pressure, which increases the overall cycle efficiency
The liquid that is to be heated in the boiler can be preheated by the means of heat
exchangers for example, reducing the amount of heat needed to be supplied to the boiler
The boiler pressure can be increased, which for an isobaric process also means that the
vapor leaving the boiler will be at a higher temperature. As earlier mentioned, this
correlates positively with turbine efficiency [22]
The condenser pressure can be reduced, allowing the turbine section to expand the steam
to a greater extent. This increases the work output from the turbine section [22].
2.6 Steam turbines
As explained in Section 2.5.1, the turbine component of a Rankine power cycle produces the
actual work output that, in turn, can be used to generate electricity. And even though the steam
engine dates all the way back to the industrial revolution, it remains as the primary mean of
producing electricity today due to its’ wide scope of possible applications. A steam turbine can, in
simple terms, be described as a machine that converts steam enthalpy into rotational energy
through the use of rotor and stator blades. For electricity generation purposes, the rotational
energy is then used to rotate a shaft connected to a generator [23]. In Figure 4, a schematic of the
first steam turbine of the so called Parsons model, invented by Charles Parsons in 1884, is
illustrated.
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Figure 4. Schematic of the first Parsons turbine, taken from [24]
Even though Figure 4 represents an old turbine model, a number of fundamental components
that are still in use today can be distinguished. For instance the turbine cylinders, which are the
components which extract work from the steam, are denoted by “A”, “B” and “C” in the figure.
These three sections of turbine blades are constructed differently in order to efficiently extract
steam at different pressure and temperature levels. Section “A” extracts work from the highest
pressure steam close to the inlet and section “C” does the same for the low pressure steam close
to the outlet. It is also apparent that all turbine cylinders are connected to the same shaft, to
which the rotational energy extracted from the steam is transferred.
In order to prevent steam leakage from turbines, the turbine cylinders are enclosed within a
turbine casing. Besides this casing, so called labyrinth seals are generally employed to prevent
leakages in today’s turbines. These seals are constructed so that they can prevent leakage between
areas of high pressure and areas of low pressure, without the need for contact with other
mechanical components by the means of inducing controlled vortices [25]. The ability to seal
leakages without having contact with the rotating shaft is highly advantageous from a durability
point of view, since no friction in the sealing system increases the equipment lifetime. Although,
a labyrinth sealing system requires a supply of steam in order for it to work properly. This steam
supplied to the sealing system is known as gland steam. During times of nominal operation, the
gland seal systems of today are self-sustaining from the steam present within the turbine.
However, during times of start-up, shutdown and low-load operation, the gland steam has to be
supplied externally [26].
2.6.1 Steam turbine start-up procedure
The start-up phase of a steam turbine is fundamental to understand when studying DSG-STPP’s,
due to the intermittency issues mentioned in Section 2.4.1. The start procedure of a steam
turbine can be divided into three main steps; Pre-start warming, running-up and loading-up. The
pre-start warming consists, as the name suggests, of warming up components such as the turbine
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steam-lines, crossover pipes, valve steam-chests and turbine cylinders while the turbine is kept
rotating at a low speed by a turning gear. The warming up phase is then followed by a running-up
phase. This phase consists of a controlled raise of the turbine’s rotational speed up until it
reaches the synchronous speed with respect to the generator. The loading-up then follows, which
consists of a steady increase of the steam temperature and turbine load up to the nominal
operating conditions [27, 28].
The speed at which the steam turbine can reach nominal load and power output is highly limited
by the thermal stresses that occur due to thermal transients. In order to not damage any critical
components within the turbine, the start procedure is therefore adapted to not allow higher
temperature differences than what is deemed permissible with respect to not overstressing any
components [29]. Since the thermal stresses experienced in the turbine are a result of temperature
differences between the incoming steam and the turbine components, having a higher
temperature of said components before commencing the start-up procedure leads to less thermal
stress and thereby a faster ramp-up to nominal power [27].
The characteristic of a turbine start with respect to the load increase over time is usually
described using a so called start-up curve. These curves can relate the percentage of maximum
load to the time spent in the starting phase and are different depending on the turbine’s initial
temperature; the warmer the turbine, the faster the start. In general, turbine manufacturers
specify a number of start curves for their turbines, each curve corresponding to an interval of the
turbine initial temperature. An example of a turbine start-up curve for a cold start is shown in
Figure 5.
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Figure 5. Sample steam turbine startup curve for a cold start illustrating the turbine rotational speed, the steam pressure and the turbine load.
Besides leading to a faster ramp-up in power, reducing the turbine start time while staying within
permissible temperature limits might also lead to less turbine wear. This is due to the fact that the
thermal stresses associated with turbine start-up are significantly reduced due to lower
temperature differences during less amount of time. In addition to this, fast turbine startup times
have an added economic advantage when it comes to adapting to load changes within the energy
system in which the plant is operating. For instance, fast startup times for a plants’ steam turbine
may lead to a faster dispatch during periods when the electricity price is high, yielding more
electricity that can be sold at a high price [30].
2.7 Power plant outage
Power plants are not able to be run for an infinite amount of time before stopping, which make
power plant outages necessary to consider when assessing their techno-economic performance.
Outages in power plants can be attributed to many things such as maintenance procedures, stops
due to malfunctioning components or, within a CSP context, stops due to not having sufficient
solar resources to run the plant. Roughly speaking, these outages can be differentiated by
scheduled and forced outages, which attributes will be described below.
13
2.7.1 Scheduled outages
Scheduled outages are, as the name suggests, outage times which are planned by the power plant
operator. These outages can mostly be attributed to regular maintenance procedures or in the
case of nuclear and fossil fired plants stops due to refueling. Due to scheduled outages being
planned, their expected implications with respect to outage times as well as costs are thus rather
well known beforehand. Additionally, the economic losses stemming from electricity production
can be minimized for scheduled outages since the downtime can be planned to occur during
times of low demand or in the case of CSP applications during times of low solar irradiance.
2.7.2 Forced outages
Forced outages are attributed to unforeseen events such as sudden power plant failures. These
kinds of outages, as opposed to the scheduled ones, are hard to account for beforehand and
result in unpredictable costs whose magnitudes depend on factors such as when the outages
occur. For instance, a forced outage occurring during a period of high demand and electricity
price will lead to a larger economic loss compared to one occurring in a period of low demand,
even though the repair costs related to the component damage is equal in magnitude. From a
power plant owner’s perspective, these outages should be avoided, which is possible through the
use of a well-structured maintenance plan.
2.8 Power plant maintenance procedures
Maintenance procedures are crucial to ensure long-term reliability of a power plant, since all
equipment that is associated with power plant operation wear out over time. Besides preventing
forced outages resulting from equipment failure, maintenance within the power plant context is
also performed in order to ensure that the plant runs efficiently over the entire course of its’
expected lifetime. This is due to the fact that continuous maintenance hinders the gradual
deterioration of the power plant equipment significantly. Additionally, most equipment has
periodic maintenance as a requirement [31]. For instance, the rotating equipment within a power
plant context needs to be properly lubricated, which in turn requires addition of new lubricant
every now and then. Within the area of power plant maintenance and equipment maintenance in
general, there are many different approaches and strategies regarding how and when maintenance
procedures should be performed. These different approaches are described in short below.
2.8.1 Preventive maintenance
Preventive maintenance is performed on a time-based schedule in order to mitigate the
degradation of the power plant equipment and thus also extend its’ lifetime. This yields several
advantages compared to a case of where maintenance is carried out at a time after equipment
malfunction. For example, the lifetime of the equipment maintained is extended, with the added
benefit of running more efficiently. As a rough estimate, preventive maintenance programs yield
economic savings 12% to 18% on average compared to reactive maintenance strategies over the
course of equipment lifetime. Although, preventive maintenance strategies also have their
disadvantages in their labor intensity in relation to the unneeded maintenance that is inevitably
performed on well-functioning equipment. Moreover, major failures may still occur under a
strictly preventive maintenance program since the set time based schedule may not always match
the deterioration of all components perfectly, and unforeseen damage to components goes by
undetected between the scheduled maintenance outages [31]. Within the scope of this study, a
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case of preventive maintenance will be modeled as the method of choice, due to its simplicity to
model over longer periods of time.
2.8.2 Other types of maintenance strategies
Besides preventive maintenance three other distinct forms of maintenance strategies can be
distinguished. These are reactive maintenance, predictive maintenance as well as reliability
centered maintenance. A reactive maintenance strategy is based on running the power plant
equipment until it malfunctions. This has some economic short-term advantages, but performs
poorly in the long run, which is why this kind of strategy was chosen not to be included within
this study. Predictive and reliability centered maintenance strategies, on the other hand, are highly
complex. Predictive maintenance for instance, is based on continuously measuring equipment
performance in order to adapt maintenance procedures to match equipment deterioration.
Reliability centered maintenance is complex due to the fact that it tries to optimize resource
allocation to match the different maintenance needs for different pieces of equipment. Both of
these strategies are rather effective, but their complex nature makes them very difficult to model
on a yearly basis [32]. Hence, these strategies were not chosen to be modeled within the scope of
this thesis.
2.9 The economics of maintenance
Maintenance costs come in different shapes. For example, new parts have to be purchased to
repair any damaged power plant equipment and in order to replace these damaged parts,
contractors will have to be hired. In addition to this, power plant maintenance also causes
indirect costs from the revenue losses during periods of maintenance work being carried out.
Although, all in all it is beneficial for a power plant to conduct maintenance procedures due to
the many benefits associated with it. The benefits of a well-structured maintenance system
include things such as less forced outages, longer equipment lifetime and a preserved high overall
efficiency of the power plant.
2.10 Equivalent operating hours
The concept of Equivalent Operating Hours (EOH) is a common method to measure the wear
caused on both gas and steam turbines in order to schedule maintenance and service activities. It
is widely used by turbine manufacturers in order to specify the recommended maintenance needs
for their products. According to [33], the standard turbine maintenance recommendations are
based on this concept. These recommendations are presented in Table 3.
Table 3. Typical recommendation from European turbine manufacturers concerning maintenance frequency. Taken from [33].
EOH Years after commissioning Type of Overhaul
10 000 Maximum of 4 Minor 25 000 Maximum of 8 Minor
50 000 Maximum of 15 Major
75 000 Maximum of 20 Minor
100 000 Maximum of 25 Major
15
Examining Table 3, it is clear that the general maintenance recommendations are based on a
preventive maintenance schedule, which was presented in Section 2.8.1. The types of turbine
overhauls, which are also presented in Table 3, include different procedures which are listed in
[33]. The minor and major outages differ not only in the procedures that are gone through, but
also in the amount of time that the maintenance procedures consume. In general, it can be
approximated that minor maintenance overhauls take up to 2-4 weeks, whereas major overhauls
stretch over 6-8 weeks [33].
The EOH concept expands the concept of Nominal Operating Hours (NOH), which are the
hours at which the plant is operating at nominal capacity, by including the increased wear on the
turbine caused due to startups and off-design operation, illustrated in (2), where EOH_s denotes
the accumulated operating hours due to plant starts.
(2)
The inclusion of plant starts makes EOH fitting in CSP applications, since the frequency of
startups is very high and thus in need to be accounted for. The wear caused by turbine starts is, as
earlier mentioned, highly dependent on the turbine initial temperature before each start. The
lower the initial temperature, the higher the wear caused on the turbine. Therefore, reducing the
temperature drop of the turbine during off-line hours results in a lower amount of EOH
attributed to the next start-up sequence [2, 4].
2.11 Start improvements
Expanding on the EOH concept, it can be stated that the amount of equivalent operating hours
per unit of produced electricity is best kept as low as possible in order to minimize turbine
downtime due to maintenance operations. Since the amount of EOH accumulated is highly
dependent on the turbine temperature before each start, improving the turbine starting
temperature has the potential of significantly reducing the amount of annual EOH for all kinds
of cycling plants. In addition to reducing the amount of EOH related to each start, the turbine
has a significantly faster ramp-up to nominal power at higher temperatures. This means that
increasing the temperature for each start results in both higher annual electricity production and
less annual EOH, which has been observed by [2] & [3]. Within the scope of this study, two types
of start improvements will be examined. The first is the addition of external heat blankets and the
second is an increase of the gland steam temperature within the turbine. These were shown in
[34] to be an efficient combination of temperature maintaining modifications to maintain the
temperature of both the rotor and the turbine casing during downtime.
2.11.1 Heat blankets
Electrically powered heat blankets supplies the turbine with heat externally. This means that the
effect of these blankets mainly heat the outer casing of the turbine, while not being as effective at
heating the internal parts such as the rotor. According to [34], this can mostly be attributed to the
low rate of heat exchange across the turbine flow passage. Although limited to mainly heating the
exterior of the steam turbine, electric heat blankets have been tried in various power plants to
increase steam turbine flexibility. According to [35], applying blankets to the steam turbine of a
combined cycle plant in Faribault, USA almost eliminated the amount of cold starts completely in
addition to significantly speeding up the plants’ warm starts.
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2.11.2 Gland steam temperature increase
Since a steam turbine consists of a rotating shaft with mounted blades, the turbine cannot be
designed to be completely air tight since this would prevent rotation of said shaft. Instead, steam
is used to seal the turbine. This steam is known as gland steam, and is externally supplied to a
steam turbine’s labyrinth joints in times of idleness or low-load operation in order to prevent
infiltration of air into the turbine on the LP side or leakage of steam on the HP side. In addition
to this, the LP-side labyrinth joint closest to the condenser must be supplied with gland steam
even at full load operation in order to prevent the condenser vacuum conditions to interfere with
turbine operation [34]. The gland steam used to seal these labyrinth joints are supplied by an
auxiliary boiler [36]. In the case of a CSP-plant, this means that the gland steam has to be
produced from using an auxiliary fuel to power the gland steam boiler. This is due to the fact that
the gland steam needs to be produced during times when the plant is offline which naturally
means that the plant does not have sufficient solar resources to produce steam in the receiver.
Increasing the externally supplied gland steam temperature during idle periods helps maintaining
the temperature of both the casing and the rotor of the turbine. Although, unlike the heat
blankets, the increase of the gland steam temperature does a better job of maintaining the rotor
temperature than that of the casing. Since significant thermal expansion of the turbine parts
occur during startup, it is of interest to heat the interior and exterior of the turbine in a uniform
manner. This makes an increase of the gland steam temperature a good complement to the heat
blankets, since heating of the rotor provides an interior heating, whereas the heat blankets
provide heat from the exterior.
2.11.3 Start improvement implications
As stated earlier, the objective of introducing these start improvements in a CSP plant is to
increase the temperature of the steam turbine before each start-up sequence which, in turn, yields
a higher annual power production as well as less accumulated EOH. However, implementing
these start improvements also comes with some thermodynamic and economic penalties. In [3],
the authors examined the annual increased power output of a CSP plant using heat blankets and
additional heating of the gland steam. Within the scope of that study, electrical heating of the
gland steam and heat blankets was assumed, penalizing the annual electrical power output for
implementing the start improvements, much like the parasitic consumption of a power plant
pump.
Although, when considering all economic aspects of start improvements, the reduction of
electricity production is not the only penalizing aspect that has to be taken into consideration.
For instance, purchasing the necessary equipment to operate these improvements needs to be
accounted for. In the case of an increase of the gland steam temperature for example, there are
investment costs related to increasing the capacity of the power plant’s auxiliary boiler as well as
operating costs related to fuel purchases and a slight increase in maintenance. For electrical heat
blankets, there are of course also investment costs to be considered, depending on the capacity of
said blankets.
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3 Power plant model - Ivanpah This chapter treats the characteristics of the inherited power plant model, as well as the software
and methods used to obtain the results. Ivanpah is a solar tower power plant (STPP) located in
the Mojave Desert, California that has been operating since December 2013. It utilizes a DSG
configuration to drive its’ Rankine cycles with a cumulative gross capacity of 392 MW divided
over three separate solar towers [15]. The steam generation part of each tower consists of a
receiver with an evaporator (EV), a superheater (SH), and a reheater (RH) that are supplying
steam to a turbine with both high pressure and intermediate pressure stages. As with other DSG
plants, Ivanpah lacks methods to store any excess heat, which limits the choices of operating
strategies since the electricity production is controlled solely by the solar irradiation. The layout
of the Ivanpah plant, as seen by looking at only one out of three solar towers, is illustrated in
Figure 6.
Figure 6. Simplified Ivanpah plant layout
Figure 6 shows the Ivanpah plant layout. As sunlight strikes the solar field, steam starts to
generate within the EV before being superheated in the SH, which is also part of the receiver.
The steam then expands within the high pressure turbine (HPT), where a portion of steam is
extracted for regeneration whereas the remainder is reheated before being expanded once more
in an intermediate pressure turbine (IPT). These two turbines drive the shaft which, in turn,
supplies power to the electrical generator which delivers electricity to the grid. The accumulator
(ACC), gathers the steam that leaves the IPT and serves as a smaller form of thermal storage. The
18
deaerator (DEA) also serves an important purpose, as it removes oxygen and other gases from
the feed water.
Something apparent when examining the figure that the Ivanpah plant runs with a slightly more
complex power cycle than the simple ideal Rankine cycle described in Section 2.5.1. For instance,
the steam is superheated before expansion in the HPT, and then reheated before a second
expansion in an IPT turbine section. In addition to this, steam is extracted from both the HPT
and IPT turbines in order to pre-heat the liquid coming from the steam accumulator in several
steps before reaching the evaporator once again, which characterizes this as a regenerative
Rankine cycle with reheating.
The existing model of the Ivanpah plant was accessible within the software tool DYESOPT. This
tool will be further described in the following sections.
3.1 The DYESOPT modeling tool
The modeling tool used in this project is the KTH in-house tool Dynamic Energy System
Optimizer (DYESOPT). It is a tool used for techno-economic modeling on a power plant level
based on MATLAB and TRNSYS. Inputs such as meteorological data, economic indicators,
power plant specifications and component-level data are used and processed in MATLAB to run
annual performance simulations in TRNSYS. The achieved performance data from TRNSYS is
then read back into MATLAB code for post processing purposes. Figure 7 illustrates DYESOPT
as a flowchart. Worth mentioning is the fact that the dynamic simulation part illustrated is
handled in TRNSYS, whereas the other parts are handled in MATLAB.
Figure 7. Flowchart describing the DYESOPT model
3.1.1 DYESOPT inputs
As Figure 7 suggests, DYESOPT handles multiple input variables before and after running its’
dynamic simulation. The different input types are colored depending on their characteristics.
Inputs in blue denote location-specific values, inputs in green are cost functions and inputs with
yellow color are related to the power plant design. All the inputs and their function in the model
as a whole are listed shortly below.
3.1.1.1 Plant design parameters
The plant design parameters inputs dimension the fictive power plant that aims to be studied
during the dynamic simulation. The design parameters can, in turn, be divided into different
19
categories corresponding to the different parts of the plant. For instance, the design parameters
related to the power generation block consist of factors such as gross power output, pressure and
temperature levels of the different turbine stages and turbine efficiency based on specified steam
conditions. This input also consists of the sizing of the heat source block, containing parameters
such as the solar field area based on the solar multiple and design location, the receiver sizing,
and the sizing of all tubes related to the steam generation. For models other than the direct steam
generation, this input also sizes any eventual thermal or electrical storage.
3.1.1.2 Meteorological data
As stated in Section 2.1, solar energy applications are very location dependent with respect to the
local annual DNI. Thus, the meteorological data serves as a vital input in order to simulate the
annual performance of a solar power plant. This input consists of a file containing the weather
data for the location in which the fictive power plant that is studied should be placed. This
information is then used in the power plant steady state design, mostly in order to scale the heat
energy source block of the plant.
3.1.1.3 Demand – Price data
The demand and price data and DYESOPT are of little consequence in this study, since DSG
plants are dispatched at all times when solar resources are available, and the LCOE is what is of
interest. Thus, this part is mostly of interest when looking at things that are outside the scope of
this study.
3.1.1.4 Operating strategy
When defining the plant design parameters, it is also possible to use existing operating strategies,
as well as introduce new operating strategies within DYESOPT. These might include strategies
on how to use an eventual source of thermal energy storage (as implemented in [4]), or as in this
study strategies on how to operate any eventual turbine start improvements.
3.1.1.5 Cost functions
While a lot of factors and performance indicators regarding the fictive solar power plant are
interesting, the costs related to the construction and operation of said plant might be the most
important one. The cost functions in DYESOPT are divided in to capital expenses (CAPEX) and
operational expenses (OPEX) which change depending on the location of the plant as well as the
plant size and power output.
3.1.1.6 Economics of location
Depending on where in the world a CSP plant is located, different economic factors apply. For
instance, the costs of labor and property, as well as tax rates are significantly different depending
on where the plant is set. Therefore, cost figures for a number of locations around the world are
included in DYESOPT, which are easily changed as a part of the default economic parameters.
3.1.2 TRNSYS dynamic simulation model
Following the plant steady state design done in MATLAB, DYESOPT creates an input file
containing all default parameters for the modeled plant and sends it to TRNSYS for a dynamic
simulation stretching over a pre-defined time period, which within this study was chosen as one
year. Within TRNSYS, the default parameters from the input files are then distributed in a pre-
defined layout for the plant and its components.
20
When running a simulation in the TRNSYS model, a pre-defined weather file controls the heat
output from the solar field to the receiver. Once a sufficient amount of steam of adequate quality
is able to be produced from the solar field input, the steam is superheated and sent to the HPT
turbine. As steam reaches the turbine, a turbine control component, referred to as “turbcontrol”,
should account for the delays associated with reaching synchronous speed and loading up.
Following these delays in the turbine sections due to the turbcontrol component, the cycle
eventually reaches nominal operating conditions.
All components within the Ivanpah TRNSYS model, such as the turbcontrol component already
mentioned in the paragraph above, are part of the so called Solar Thermal Electric Components
(STEC) library, which is a model library of TRNSYS components that can be read about in [37].
The components used in the Ivanpah model gets their input data from the previously mentioned
input file created in MATLAB, which allows the dynamic model to be run in accordance with the
pre-defined component sizing.
3.1.3 Performance indicators
In order to be able to evaluate the performance of a specific power plant, DYESOPT provides a
number of performance indicators as outputs. These indicators can be strictly thermodynamic,
but may also include economic and environmental factors. Below you can find a description of
some of the most important performance indicators within the scope of this study.
3.1.3.1 Thermodynamic indicators
The first, and perhaps most basic performance indicator introduced is the annual net electricity
generation of the modeled power plant. For each simulation time step, this output consists of the
difference between the electricity generated by the plant and the parasitic consumption of
components such as pumps and fans . Summing up this difference throughout the year
yields the annual net output as described by (3).
(3)
Following the net power output, another strictly thermodynamic performance indicator, called
capacity factor, can be calculated. The capacity factor is an indicator of how the annual output of
the plant relates to its maximum theoretical output. The maximum theoretical output is in this
case defined as the power produced with the plant operating at nominal power output
over the course of an entire year . With this being stated, the capacity factor is computed
as (4).
(4)
3.1.3.2 Maintenance-related indicators
As described in Section 2.10, nominal and equivalent operating hours are crucial concepts when it
comes to maintenance scheduling of power plants utilizing steam turbines. The nominal
operating hours are, rather basically, defined as the amount of hours that the turbines of the plant
generate power. The amount of throughout the course of a year is thus given by (5).
21
(5)
In order to account for additional turbine wear caused by starts, the are transformed into
by the means of (6).
(6)
In this equation, represents the amount of that are accumulated due to plant starts.
In turn, each start has different characteristics dependent on the initial temperature as described
in Section 2.6.1. Therefore, each start is categorized into cold, warm or hot depending on the
turbine initial temperature. Following this, the starts are assigned a number of that are
accumulated during the starting procedure denoted as , or . In accordance with [4], cold
warm and hot starts were set to represent 30, 20 and 10 , respectively. The amount of
accumulated during the simulation were thus given by (7).
(7)
Due to turbine overhauls, plant downtime will inevitably occur due to maintenance procedures.
Therefore, an overall availability factor is calculated in order to account for this. This factor
takes the maintenance schedules described in section 2.10 and assumes an even annual
distribution of turbine overhauls over a full maintenance cycle of 100000 EOH. These
assumptions result in an expression for said availability factor presented in (8).
(8)
3.1.3.3 Economic indicators
The economic indicators in DYESOPT are summed up in two categories, capital expenditures
(CAPEX) and operational expenditures (OPEX). For the CAPEX, these are, in turn, divided into
direct and indirect capital costs, as computed in (9).
(9)
The direct costs are the sum of a number of investment costs, including site preparation costs,
investment costs of the power block, solar field, solar tower and receiver as well as the balance of
plant costs and the contingency cost to cover uncertainties, the calculation as a whole is done as
expressed in (10).
(10)
The indirect costs, on the other hand, are made out of expenses due to taxes, land purchases and
costs related to EPC (Engineering, Procurement and Construction) ownership sharing, like with
the direct costs the indirect ones are summed together using (11).
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(11)
The OPEX costs can be summed up using (12).
(12)
These different parts of the total OPEX can, in turn, be dissected. The utility costs
are attributed to purchases of externally supplied power and water. The costs of all service
contracts are made up of costs related to control systems, office equipment and
O&M of the solar field such as grounds keeping and mirror washing. The labor costs
represents all operations and maintenance labor of all power plant components related to the
solar field and power block. Finally, the miscellaneous costs include overhead costs
related to all plant components such as purchases of spare parts and repairs.
With the and calculated, the equation for the can be computed. The
is defined as the average cost of generating electricity over the lifetime of a power plant
and is often used as a measure to evaluate different methods of generating electricity on a
comparable basis. It is computed in accordance with (13).
(13)
The factor α is known as the capital return factor, taking the interest of the total investment cost
into account. It assumes a constant annuity at an interest rate , during the course over the plant
lifetime and is written down as (14).
(14)
Where represents insurance costs, which are assumed to be scaled with the total
investment cost of the solar plant.
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4 Contributions to model This chapter explains what changes were made to the inherited version of DYESOPT, and
discusses their advantages and possible disadvantages. A number of changes were made, most of
them concerning the post processing in MATLAB, but some changes were also made within the
context of the dynamic simulation in TRNSYS.
4.1 Turbine heating and cooling
In order to simulate the process of heating and cooling down of the turbine during operation and
offline hours, the lumped capacitance method was applied. The lumped capacitance method is a
common method used to model the temperature of a thermal mass over time under the
assumption that the temperature of said thermal mass is spatially uniform, without any
temperature gradients. Using this method, the final temperature after a certain time can be
expressed as (15).
(15)
Where is the steady state temperature (corresponding to the temperature of the ambient fluid
in a simple cooling case), is the initial temperature of the thermal mass and is the time. is a
time constant defined by the material properties of the thermal mass as well as the heat transfer
coefficient U between the thermal mass and the ambient fluid. For a simple case of a thermal
mass cooling down, this constant can be expressed in (16).
(16)
Before this study was conducted, the turbine heating and cool down was handled in MATLAB as
a post-processing measure. The temperatures of the turbines were then assumed to be equal to
the steam inlet temperature at the time of each shutdown. Additionally, the cool down process
was done by assuming a time constant τ for both turbines. Within the scope of this study
however, the application of the lumped capacitance method was done dynamically in TRNSYS
by using the capacity component (type 306) from the STEC library.
Two of these capacity components were introduced in the DSG model, one for the HPT section
of the turbine and one for the IPT section. These were connected to the superheater and reheater
respectively, with an equation component as an intermediate. The equation component made
sure that the mass flow of steam through the capacity components took the turbine bypass
function that was introduced in [9] into account.
4.1.1 Necessary assumptions
The Ivanpah plant uses turbines of the SST-900 model, manufactured by Siemens [38]. In order
to apply the lumped capacitance method, an assumption was made so that the turbines were
modeled as cylinders made of cast iron, somewhat similar to the assumptions made in [9].
Although, apart from modeling a strict cooling case, the new model also considered a steam flow
through the turbine that heated it up during operating hours. The modeled situation in TRNSYS
is illustrated in Figure 8.
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This way of modeling required two different heat transfer coefficients to be used, one for the
natural convection between the casing (Tc) and the ambient temperature (T∞) as in [9], and one
for the forced convection caused by the steam mass flow during turbine operation. This addition
of a steam flow heating up the turbine changed the value of the time constant τ to be expressed
in accordance with (17).
(17)
Additionally, the steady state temperature, is changed when adding a flow of steam to the
lumped capacitance model. In times of turbine operation, it went from being equal to the
ambient air temperature to being expressed by (18).
(18)
With these expressions for the time constant and the steady state temperature , the lumped
capacitance equation for the thermal mass was solved iteratively in TRNSYS. All parameters used
to model the turbine heating and cool-down in TRNSYS are presented in Table 4, where the
properties of the steam flow was assumed to be at turbine inlet conditions for the HPT and IPT,
respectively. The dimensions of the modeled turbines were attained through the turbine
schematics.
Figure 8. Simple drawing of lumped capacitance modeling situation for the Ivanpah turbines
25
Table 4. Parameters used for the lumped capacitance turbine model
Parameter and symbol Unit Value (HPT) Value (IPT)
Casing height 1 2,1
Casing width 1 2,1
Casing length 2,54 5,04
Casing thickness 0,2 0,25
Casing surface area (outer) 8,03 33,34
Casing surface area (inner) 6,43 29,38
Casing volume 2,28 10,53
Casing density
7850 7850
Casing specific heat (cast iron)
460 460
Steam specific heat
2720 2256
Overall heat transfer (casing – ambient)
45,8 190
Overall heat transfer (steam – casing)
5181 3461
Ambient temperature 25 25
The heat transfer coefficients between the incoming steam and the modeled turbines were
attained by the means of the Dittus & Boelter relation, which for a cooling case with respect to
the fluid flow is expressed as (19) according to [39].
(19)
For this relation, most of the parameters related to both the Reynolds and Prandtl numbers were
easily attained at steam inlet conditions and turbine schematics. The axial velocity determining the
Reynolds number was approximated using the steam mass flow at the inlet, the density of the
incoming steam, and the flow channel area attained from the turbine schematics. Using the
Dittus & Boelter relation also assumes that the steam flows through a smooth tube, which the
modeled situation described by Figure 8 could be said to resemble.
4.1.2 Difference from previous model
Modeling the turbine heating and cool down dynamically in TRNSYS instead of doing it as a post
processing measure in MATLAB has both its similarities and differences with respect to the end
result. For instance, the turbine cool down is modeled similarly by assuming the parameters of
the time constants τ as expressed in (16), but the assumption that the turbine temperatures are
equal to the inlet steam conditions during shut down is no longer made. Instead, the temperature
of the turbines are separated from the steam temperatures in TRNSYS, making the temperature
26
readings much more accurate in cases where the turbine is on-line for only a short amount of
time.
4.2 Increased accuracy during turbine ramp-up
Within DYESOPT’s dynamic simulation process, handled in TRNSYS, turbine start-up and
shutdown are handled in a strict binary manner. This means that the turbines are either treated as
“on” or “off” during every time step, which makes the power produced during the turbine start-
up phase to be rather overestimated in the raw TRNSYS output. To measure this in a more
accurate manner, two different methods were introduced into DYESOPT; the first being a
numerical integral corresponding to the Ivanpah turbines’ start-up curves that was implemented
in the post processing step. This method utilized pre-defined start curves for different starting
temperatures that were read into the MATLAB post-processing part of the model, which allowed
more accurate estimates of the power being produced during the turbine startup phases. The
implemented change in the case of a cold start of the Ivanpah HPT turbine is illustrated in Figure
9.
Figure 9. Power output during turbine start-up in TRNSYS and the real case based on Ivanpah turbine start curves for a typical cold start
In addition to this, a second method was introduced in the dynamic simulation part. This method
utilized a slightly modified version of the TRNSYS component “turbcontrol” (Type 292), which
can be read about in [9], in order to avoid the instantaneous ramp-up to nominal power. Using
the splitter bypass signal introduced by this component, it was possible to model a linear
relationship between the turbine power and time during times of ramping up within the dynamic
27
simulation, rather than in MATLAB. The difference in TRNSYS output from introducing this
method is shown in Figure 10.
Figure 10. TRNSYS power output during turbine ramping before and after inclusion of the splitter bypass signal from the turbcontrol component
4.2.1 Difference from previous model
In order to combat the instantaneous ramping that occurred in TRNSYS, the previous model
would simply remove all power produced during ramping, resulting in a lower power production
compared to the real case. With the implemented changes illustrated in Figure 9 and Figure 10
however, this partial power was accounted for in two different ways. Table 5 shows the
difference in partial production from implementing the numerical integral corresponding to the
supplied start curves of the Ivanpah turbine compared to the partial power production when
using turbcontrol for ramping.
28
Table 5. Differences in ramping time and partial power produced during start-up between the
two implemented
Start Curve
Numerical integral ramping time
[h]
Numerical integral partial power
[MWh]
Turbcontrol ramping time
[h]
Turbcontrol partial power
[MWh]
Difference in partial power
N1 0,13 11,45 0,25 15,75 -27,3% N2 0,5 42,73 0,25 15,75 +171,3% N3 1,38 118,28 1 63 +87,3% N4 1,05 75,18 1 63 +19,3% N5 1,13 85,42 1 63 +35,6% N6 1,5 114,03 1,5 94,5 +20,7% N7 2 135,71 1,5 94,5 +43,6%
Table 5 shows that even though the method of turbcontrol ramping gives a better picture of the
partial ramping power compared to the old model, the method of using the numerical integral
performs better overall. For instance, it is apparent that the turbcontrol currently only handles
three different curves, which correspond to average hot, warm or cold starts, whereas the
numerical integral method handles all seven available start curves. It is also apparent that the
turbcontrol ramping times, at first glance, may seem a bit off compared to the real start curves.
This is due to the fact that the ramping time needs to be specified as a divisor of the simulation
time step when using the turbcontrol component. Since the DYESOPT time step is set to 15
minutes, the ramping times can only be specified in factors of 0,25 hours unless the time step is
changed.
There is also another seemingly strange difference regarding the ramping time, which only applies
to the cold starts N6 and N7, is the fact that the N6 curves’ ramping time has been chosen for
the turbcontrol ramping without even considering the N7 curve. This is justified by the fact that
close to no N7 starts was observed to occur during a simulated standard year of the Ivanpah
plant. Finally, values for the old model of Ivanpah were chosen not to be included in this
comparison since those values for ramping time and partial production values would, as
mentioned earlier, simply amount to 0.
4.3 Start improvements
All considered start improvements were, within the context of this study, dealt with in the post
processing part of the DYESOPT calculation flow. This meant that the turbine cooling process,
which was modeled in TRNSYS, preceded the introduction of start improvements in the
calculations. Therefore, the modeling of start improvements in MATLAB may suggest that the
implemented start improvements heat the turbine after a period of cooling down; when in reality
they are simply hindering the cooling process.
Two different kinds of start improvements were considered; the installment of heat blankets
enveloping the turbines and an increase of the gland steam temperature within the turbines. Ways
to model these start improvements were already present in the inherited DYESOPT version of
Ivanpah. Hence, the contributions to this part of the model were mostly structural in nature,
although some improvements were made to increase the accuracy compared to the inherited
model. In the inherited version, the extents of the implemented start improvements were
29
specified by the user prior to running the dynamic simulation in TRNSYS. The impact of these
pre-defined start improvements could then be evaluated after the dynamic simulation during the
techno-economic post processing calculations. This way of pre-defining the magnitudes of heat
blanket power and gland steam temperature was changed so that a more goal-specific method
was used, which will be described below.
4.3.1 Start improvement strategies
After the changes made in conjunction with this study however, the user gets to specify a goal
that said start improvements should achieve. These goals/strategies are denoted as “modes”
within DYESOPT. Currently there are three modes implemented, each corresponding to a
different pre-defined goal:
Mode 1 – The mode inherited from the past model with pre-defined values for the extent
of chosen start improvements
Mode 2- All starts are improved “one step” with respect to the turbine start up curve
ranges specified in “default parameters”
Mode 3 – All starts are improved to the best (hottest) possible start up curve specified in
default parameters
Changing the structure so that the user specifies a desired result rather than the extent of the
implemented start improvements changed the post-processing method concerning these start
improvements significantly. If mode 2 or 3 is chosen, DYESOPT calculates the difference
between the turbine final temperatures after cool down during downtime and the desired start up
curve temperature limit (specified by the chosen mode), resulting in a desired temperature limit to
be reached. For example, the temperature difference that is desired for operating mode 2 or 3 is
described by (20), where denotes the turbine temperature after the cool down period
preceding the start and denotes the minimum temperature limit of the desired start up
curve.
(20)
Following the calculation of the desired temperature difference, the required inputs from heat
blankets and gland steam increase were determined using a set of contour plots
attained from previously performed numerical simulations. The contour plots used corresponded
to two different turbine cool down times of 10 and 20 hours, respectively. The required inputs
from the temperature maintaining modifications could then be approximated by inter- and
extrapolating between and from these values. This process was repeated for each turbine startup
during the simulated year, yielding a value for both the required heat blanket power and gland
steam temperature increase to fulfill the requirements of the specified operation mode.
The choice of two plots corresponding to 10 and 20 hours of cool down time was made due to
the fact that the majority of the cool down times during the simulated year were in this interval.
The contour plots used for determining the required inputs from the heat blankets and the gland
steam are shown in Figure 11.
30
Figure 11. Contour plots used for determining extent of used start improvements
As the contour plots in Figure 11 show, the relationship between the extent of the implemented
start improvements and the reduced turbine cooling is rather linear. This somewhat justifies the
inter- and extrapolations that were done for the cooldown times deviating from the 10 and 20
hours that the contour plots represented. Additionally, the plots show that heat blanket capacities
between 0 and 0,9 kW/m2 were considered, as well as gland steam temperature increases ranging
from 0 to 90 degrees Kelvin.
4.3.2 Difference from previous model
The structural change of implementing operation modes that aim to reach certain results
provides the opportunity to properly dimension the selected start improvements to reach a
specified goal. DYESOPT can easily be configured so that loading curves for the selected start
improvements are provided with the end result, visualizing the required extent of the start
improvements vs the amount of annual starts.
As one might imagine, fulfilling a certain start improvement strategy for every single start during
the course of a year may be unrealistic, since the case might be that approximately 90% of all
starts can fulfill the chosen strategy even when the extent of the start improvements are severely
cut. For obvious economic reasons, vastly over dimensioning the start improvements to meet the
demand for a few, extreme cases is no desired scenario and hence the loading curve serves a
valuable purpose from an economic point of view. One way of compensating for this over
dimensioning is the introduction of a coverage factor which states to what extent the chosen
operating strategy should be fulfilled. This coverage factor, specified as a percentage, used loading
curves for the chosen improvements in order to filter out any eventual “peaks” of required start
improvement capacity. This filtering method, and how it relates to the investment calculations is
further described in Section 4.4.1.2.
Since the extent of the start improvements are determined for each start separately rather than as
in the inherited model being constant, the yearly required power input for the heat blankets and
the desired gland steam temperature increase was also able to be calculated. This is highly suitable
for economic calculations on the start improvements in order to determine their economic
31
viability. This is due to the fact that the energy input requirements for both improvements can be
determined in a more realistic way compared to the inherited model’s constant energy input
values.
4.4 Costs and benefits of start improvements
Before this study, DYESOPT was only able to assess the impact on electricity production and
equivalent operating hours from implementing heat blankets and a gland steam temperature
increase of pre-defined magnitudes. However, there were no functions available to perform
realistic calculations related to the costs of these measures. Therefore, the economic advantages
and penalties of the modeled improvements had to be added within the “Cost Functions” part of
DYESOPT, visible in Figure 7.
4.4.1 Gland steam temperature increase
The temperature maintaining modification based on increasing the gland steam temperature
required steam at a rather high temperature to be produced during the plant’s off-line hours.
Within the context of a CSP-plant, that meant that an auxiliary boiler would have to be used,
since no steam could be generated in the receiver during plant downtime. Hence, the costs
related to this start improvement consisted of the capital investment as well as the operation and
maintenance costs of this auxiliary boiler.
4.4.1.1 Operation and maintenance cost calculations
The operation and maintenance costs related to the auxiliary boiler for the gland steam was
calculated based on the annual fuel consumption throughout the modeled year. According to
[40], the total operation and maintenance costs for a steam generator can be approximated
through (21).
(21)
From this equation, all that remained was to calculate the annual fuel costs for the auxiliary boiler
which, in turn, meant that the yearly fuel consumption would have to be computed. In order to
do this, the amount of fuel needed for each start in order to fulfill the chosen operating strategy
as described in Section 4.3.1 was computed as (22).
(22)
After attaining the required heat input for each start, the annual required heat output from the
boiler was calculated. The required fuel costs could then be attained by assuming a boiler
efficiency of 80% and a choice of fuel to be natural gas to attain a lower heating value and a cost
figure. The final calculation of the fuel cost is shown in (23).
(23)
4.4.1.2 Capital cost calculations
In order to calculate the investment cost, the required capacity of the auxiliary boiler had to be
dimensioned. From the previously mentioned calculations for each plant start that led up to
32
(22), a similar computation could be made for the boiler capacity. This computation was done
for each start throughout the year and is shown in (24).
(24)
The maximum value of this equation for all the starts occurring annually could then be used to
dimension the boiler in order to fulfill the chosen strategy of start improvement operation. The
values attained from (24) were sorted, providing an annual loading curve for the auxiliary boiler,
which for the cases of upgrading all start curves one and two steps are illustrated in Figure 12.
Figure 12. Gland steam loading curves for starts being upgraded one and two steps
Looking at Figure 12, a significant peak of gland steam power can be distinguished in the case of
all starts being upgraded one step, whereas no real peaks exist in the case of all starts being
upgraded two (or more) steps. The fact that no peaks exist in the case of two upgrade steps is
probably due to the moderate extents of start improvements included in the supplied contour
plots (Figure 11). As seen in Figure 12, the “maximum” capacity of the start improvements was
reached even with just one desired upgrade step. The implications of Figure 12 meant the
dimensioning of the auxiliary boiler called for an implementation of a coverage factor, which was
mentioned in Section 4.3.2.
Within the context of this study, the coverage factor, stating to what extent the chosen operating
strategy of the start improvements should be fulfilled, was set to 90%. This resulted in a new
gland steam loading curve for the case of one upgrade step, which is presented in Figure 13.
33
Figure 13. Gland steam loading curve for one desired upgrade step after implementing a coverage factor of 90%, to be compared with Figure 12
After the dimensioning of the auxiliary boiler was done, its’ capital cost was approximated by
using data from [41]. From the price data provided for different boiler capacities there, a third
grade polynomial fit was applied in order to achieve a function relating the required boiler
capacity to the investment cost. How the dimensioned boiler power then related to the
investment cost is described in equation (25).
(25)
Where represent the polynomial coefficients obtained from the third grade curve fit in
MATLAB. In addition to the investment costs related to the boiler itself, the required increases in
balance of plant costs and costs for installation were considered through the percentual
coefficients and . The boiler lifetime was also assumed to be 15 years, yielding the
final investment costs of the boiler as described by (27).
(26)
4.4.2 Electrical heat blankets
The economic modeling of the electrical heat blankets took inspiration from [3], with a few
additions to fully assess their economic impact. As with the gland steam temperature increase,
implementing electrical heat blankets came with both capital and operational costs, which will be
presented below.
34
4.4.2.1 Operational cost
The operational cost of the heat blankets was assumed to be in terms of lost electricity
production, as modeled in [3]. As with the gland steam temperature increase, the required heat
blanket input for each start was summarized throughout the simulated year in order to attain the
yearly electricity consumption of the blankets, calculated as (27).
(27)
This annual electricity input was then added to the parasitic consumption of the plant, thereby
reducing the annual net electricity production described in (3) in Section 3.1.3.1. Thus, the
operational cost of the heat blankets was modeled as a loss in plant electricity production rather
than a monetary parameter, similarly to the assumptions made in [3]. Naturally, this was done
under the assumption that purchased electricity for the heat blankets during off-line hours was at
the same price level as the electricity sold by the CSP-plant.
4.4.2.2 Capital cost
The capital cost of the heat blankets was approximated using figures from [42] and [43]. The cost
figure included both a fix installation cost ( ) as well as a variable heat blanket cost ( )
which was given as USD per m2. The heat blankets were also scaled based on their rated power
input compared to a reference case ( ). This reference case was obtained through a
comparison of annual turbine heat loss with and without blankets, listed in [43], which was used
to scale the blankets against their rated power obtained from the contour plots ( ).
Furthermore, it was also found that the heat blankets would need to be purchased for four times
the turbine total area according to [35], yielding the total capital cost as (28) assuming a blanket
lifetime of 10 years.
(28)
Where represented the total outer casing area of both the HPT and IPT turbines, listed in
Table 4.
4.5 Maintenance and EOH
In Section 2.8, a number of common maintenance strategies used in power plants around the
world was presented. From the description of these operating strategies, one could deem it
optimal to model a case of predictive or reliability centered maintenance. Although, this would be
very difficult to achieve in practice, since unforeseen changes in turbine conditions are very hard
to integrate into a pre-defined power plant model. Thus, this study modeled a case of predictive
maintenance, where EOH was the sole indicator for maintenance requirements and schedules.
The modeling of lost electricity production due to maintenance outages was already implemented
within the DYESOPT model for direct steam generation power plants. This was done through
the use of the availability factor earlier introduced in (8) based on the annual accumulated EOH.
35
Within the context of this study however, additional benefits of lowering the annual EOH of the
plant were implemented within DYESOPT. Besides the benefit of less downtime due to turbine
overhauls, additional benefits were added in the form of lower OPEX costs of the power plant.
Specifically, the amount of EOH were modeled to affect the labor costs related to the power
block maintenance, as well as the miscellaneous costs related to purchases of new components
for replacing deteriorated ones. This was done similarly to the way the capital costs of the
auxiliary boiler for the gland steam and the electrical heat blankets were calculated in the sense
that a reference case was used. A reference amount of EOH was considered to be the amount of
EOH accumulated in an annual simulation of Ivanpah without any start improvements
considered. From this, a coefficient could be computed to extrapolate the labor and
miscellaneous costs for the power block from their default values in accordance with (29).
(29)
36
5 Results & analysis In this chapter, the results from the power plant simulations will be presented and analyzed. This
chapter is split into three main parts, one containing an annual start-up analysis for the Ivanpah
plant, one relating the extent of installed start improvement to plant performance indicators and
one being a sensitivity analysis concerning some of the assumptions made.
5.1 Start curve analysis
When analyzing the annual plant start-ups, the annual start curve distribution, assuming no start
improvements are implemented, is solely decided by the cool down times between plant
shutdown and start-up. Therefore, it is of great interest to examine these cool down times and
relate them to the start curve distribution. This distribution of the annual cool down times of the
Ivanpah plant is illustrated in Figure 14.
Figure 14. The yearly distribution of cool down times sorted by magnitude.
Examining Figure 14, it is clear that most of the annual plant start-ups occur after a cool down
time between 20 and 10 hours, with a few anomalies occurring throughout the year.
For a standard year, the amount of start-ups for the Ivanpah plant amounted to a total of 387
starts, which corresponds to the 387 cool down times illustrated in Figure 14. The way these start
curves were distributed between cold, warm and hot starts was examined based on different
specified strategies for the implemented start improvements. Data regarding the distribution of
37
these start characteristics, as well as the annual time spent in the turbine start-up phase is
presented in Table 6.
Table 6. Distribution of annual start characteristics and time spent in turbine start-up phase for different extents of implemented start improvements
Desired upgrade
steps Hot starts [%] Warm starts [%] Cold starts [%] Time in start-up [h]
0 5% 93% 2% 637
1 20% 78% 2% 566
2 56% 43% 1% 398
3 75% 24% 1% 321
4 78% 21% 1% 309
To further illustrate how the start characteristics change with a higher extent of installed start
improvements, Figure 15 shows the same distribution of all annual starts and how this
distribution changes with a higher extent of implemented start improvements as a bar graph. As
for Table 6 however, there is an unsurprising clear trend of reduced time spent in the turbine
start-up phase as the extent of implemented start improvements increases. For instance, it is
apparent that the time spent in start-up is more than halved when going from the base case to
four desired upgrade steps.
Figure 15. Annual start curve distribution between cold, warm and hot starts for different extents of installed start improvements
38
As Figure 15 shows, most of the annual starts are found to be warm starts for cases of no or a
low extent of implemented turbine start improvements. As a larger extent of start improvements
are installed however, the annual starts shift more towards the hot spectrum. Further examining
this, the distribution for all start curves N1-N7 was also examined in the same way. The results
from this analysis are illustrated in Figure 16.
Figure 16. Annual start curve distribution expressed as N1-N7 starts for different extents if implemented start improvements
Comparing these two figures, Figure 16 gives a more accurate illustration of what happens when
the start improvements are installed. For instance, we see that there are significant shifts in the
warm start spectrum (N3-N5) as the extent of the start improvements increase, which is
otherwise not visible by simply examining Figure 15. Unsurprisingly, we see a shift in start curve
distribution towards the hot spectrum as the extent of the start improvements increase. The low
amount of cold starts is attributed to the fact that there are very few long cooldown times
observed during the simulated year. Furthermore, the unchanged amount of N7 starts is due to
the first start of the year always being assumed to be at a temperature level equal to the ambient
air temperature.
39
Something that might appear strange at first glance is the fact that the amount of N1 starts
doesn’t change when increasing the start improvement strategy to be larger than 2 desired
upgrade steps. The explanation for this lies in the maximum capacities of the start improvements
to prevent the turbine cooling, as defined by the contour plots shown in Figure 11. The same
phenomena is the reason for the very small difference in start curve distribution between the
strategy of 3 desired upgrade steps to the one with 4 desired upgrade steps. The start
improvement capacities for different cooldown times, as defined by the contour plots, are simply
run at close to maximum capacity when the strategy is defined as 3 desired upgrade steps.
5.2 Performance indicators
With the start-up analysis presented in Section 5.1, this section presents how these changes in
start-up behavior translates into the performance indicators of the Ivanpah plant. First off, Table
7 shows how the annual net electricity production, the capacity factor, the EOH as well as the
amount of NOH per EOH vary as the extent of installed start improvements are increased.
Table 7. Thermodynamic and maintenance-related performance indicators varying with increased extent of implemented start improvements. The "steps" field denotes the specified strategy for the start improvements, where 0 steps acts as a base case in which no improvements are implemented.
Steps Enet
[MWh/yr] fcap [%]
EOH [h]
favail [%]
NOH/EOH [%]
0 2,84*105 25,8% 10628 95,71% 28,3%
1 +2,5% 26,4% -5,2% 95,93% 30,5%
2 +7,8% 27,7% -16,5% 96,44% 36,8%
3 +9,9% 28,3% -23,1% 96,70% 40,7%
4 +10,2% 28,3% -24,1% 96,74% 41,4%
Clear trends relating to increased extent of start improvements can be distinguished for all the
included performance indicators by simply looking at Table 7. For instance, the annual electricity
production, and thereby also the capacity factor, is increased as the extent of start improvements
increase due to more time spent in nominal operation mode, rather than the turbine start-up
phase. Additionally, there is a clear trend in reduced EOH, which in turn contributes to a higher
amount of NOH per EOH as well as a higher availability factor due to less down time due to
maintenance operations.
To put it simply, the start improvements are shown to act positively towards most of the
thermodynamic and maintenance related indicators. However, these indicators alone cannot
determine the full performance implications of the examined start improvements without
considering the economic benefits and penalties. In
40
Table 8, the economic benefits and penalties of the start improvements are taken into account,
which combined with the thermodynamic indicators yields the overall LCOE.
41
Table 8. The effect of implementing start improvements on Ivanpah’s economic performance indicators. The "steps" field denotes the specified strategy for the start improvements, where 0 steps acts as a base case in which no improvements are implemented. The OPEX for the startimprovements do not include the costs connected to the parasitic consumption of the heat blankets, however, the penalty is included when calculating the LCOE.
Steps CAPEXtot
[USD] CAPEXStartimprovements
[USD] OPEXtot
[USD/yr] OPEXBoiler [USD/yr]
LCOE [USD/MWh]
0 5,60*108 0 9,50*106 0 211,94
1 +0,72% 2,855*106 -1,58% 2621 -2,34%
2 +1,03% 4,232*106 -5,37% 7412 -6,61%
3 +1,03% 4,232*106 -7,26% 9671 -9,09%
4 +1,03% 4,232*106 -7,58% 9880 -9,46%
The increase in CAPEX from implementing start improvements is shown to reach a cap when
the specified strategy is set to improve each start curve by 2 steps. This is due to the fact that the
maximum capacity required as defined by the contour plots in Figure 11 for both the gland steam
boiler and the blankets is reached at this point already. Even though this maximum for the
CAPEX is reached at 2 steps, the costs penalties for the start improvements still increase with
OPEX costs for the gland steam boiler. These OPEX costs increase with the annual fuel
consumption as more extensive strategies for the start improvements are applied. The total
OPEX, on the other hand, decreases with an increased extent of start improvements, which is a
result of reduced cost of labor and purchases of parts related to turbine maintenance. All in all,
the start improvements result in an overall reduction of the LCOE.
Reverting back to the LCOE equation (13) with the knowledge that the CAPEX increases, it is
clear that the reduction of the LCOE is a combination of the OPEX decreasing and Enet
increasing. In order to examine which one of these positive effects has the largest impact. The
model was set to run without the positive OPEX benefits of the start improvements. This case
was then compared to the previously acquired LCOE data to determine whether the assumed
OPEX reductions or the increased power production had the most impact on the overall LCOE.
The comparison is shown in Table 9.
42
Table 9. Comparison of LCOE reduction from start improvements with and without assumptions regarding OPEX cost reductions
Steps LCOE
[USD/MWh] LCOE (OPEX unchanged)
[USD/MWh]
0 211,94 211,94
1 -2,34% -1,77%
2 -6,61% -6,19%
3 -9,09% -8,08%
4 -9,46% -8,36%
Table 9 shows that it is the increase in annual power production, stemming from an increase of
nominal operating hours due to faster starts and increased availability due to less maintenance
requirements, that contributes the most to the LCOE reduction when implementing start
improvements. This relationship is further illustrated in Figure 17.
Figure 17. Annual electricity production and levelized cost of electricity as a function of start improvement steps
It is apparent that the curve representing the annual power production and the curve for the
LCOE mirror each other. The fact that the increase in power production and the reduction of
43
LCOE relate to each other in this manner suggest that the start improvements result in
significant thermodynamic benefits at a very low cost.
5.3 Sensitivity analysis
As stated above, the examined turbine start improvements were shown to yield significant
thermodynamic benefits at relatively low economic expense. Therefore, it is of interest to
conduct a sensitivity analysis showing how sensitive the performance indicators are to the
assumptions made regarding the start improvements. This sensitivity analysis was done by
introducing sensitivity factors for the contour plots, as well as a factor for both the OPEX and
CAPEX penalties of the start improvements. Starting with the contour sensitivity factor, this
factor scaled down the thermodynamic benefits in terms of reduced turbine cool down. This
contour factor was set to be <1, and was multiplied with the contour plot matrix. Table 10 shows
how varying this contour sensitivity factor affected some of the performance indicators, as well as
the end impact on the LCOE for a case of 4 start improvement steps, since this strategy was
shown to yield the biggest differences with respect to all performance indicators.
Table 10. Sensitivity analysis on the thermodynamic benefits of the start improvements as given by the contour plots for a case of running a strategy of 4 desired upgrade steps.
Contour sensitivity
factor
Enet
[MWh/yr] EOH [h]
favail [%]
LCOE [USD/MWh]
LCOE difference [%]
1 3,13*105 8066 96,74% 191,69 ±0%
0,8 -1,12% +5,9% 96,55% 194,90 +1,67%
0,6 -3,11% +14,4% 96,27% 199,61 +4,13%
0,4 -4,94% +20,7% 96,07% 203,92 +6,38%
0,2 -7,44% +28,9% 95,80% 210,12 +9,61%
Changing the sensitivity factor for the contour plots makes the start improvement maximal
capacity much lower, which for an aggressive strategy such as the one that seeks to upgrade each
start by 4 steps means that the start improvements means that the start improvements will be run
at maximum capacity but with significantly lower benefits. As Table 10 also shows, a decrease in
annual production in combination with an increase of maintenance requirements following the
reduction of start improvement efficiency results in an increase of the LCOE. Comparing Table
10 and
44
Table 8 however, it is apparent that even if the start improvements are shown to work at 20% of
the efficiency assumed in this study, they would still benefit the power plant from a pure LCOE
perspective when compared to a case where no improvements are installed. This is, of course,
only valid provided that the cost assumptions are accurate.
In addition to examining the sensitivity of the performance indicators based on the start
improvements’ efficiency. Sensitivity studies were also made with respect to the costs associated
with start improvement installation and operation. Opposite to the factor used for the contour
plots, two factors >1 was introduced and multiplied to all costs associated with start
improvement installation and operation respectively. Table 11 and Table 12 show the results of
this sensitivity analysis.
Table 11. Sensitivity analysis on the CAPEX cost assumptions related to the implemented start improvements
CAPEX sensitivity factor CAPEX [USD]
LCOE [USD/MWh]
LCOE difference [%]
1 5,66*108 191,69 ±0%
1,2 5,67*108 192,02 +0,17%
1,4 5,68*108 192,33 +0,33%
1,6 5,69*108 192,66 +0,51%
1,8 5,70*108 192,97 +0,67%
Table 12. Sensitivity analysis on the OPEX cost assumptions related to the implemented start improvements
OPEX sensitivity factor OPEX
[USD/yr] LCOE
[USD/MWh]
LCOE difference [%]
1 8,78*106 191,69 ±0%
1,2 8,78*106 191,70 ~0%
1,4 8,78*106 191,71 0,01%
1,6 8,77*106 191,72 0,02%
1,8 8,77*106 191,72 0,02%
Comparing the two tables above, it is clear that most of the cost related penalties are in terms of
CAPEX costs. This is partially due to the fact that the electrical heat blankets aren’t associated
with any O&M costs directly, but rather with a parasitic electricity consumption that is not
changed as the OPEX sensitivity factor changes. This is further illustrated by the small changes in
OPEX when applying this sensitivity factor. Another fact that becomes apparent when looking at
the two tables is that the benefits of the start improvements are not very sensitive to the
assumptions made regarding their costs, as cost increases by up to 80% doesn’t change the
impact on the LCOE in a very significant manner.
45
5.4 Break-even points
With the sensitivity analysis made, it is clear that neither the cost assumptions nor the
assumptions regarding the thermodynamic efficiency of the start improvements are very sensitive
with respect to the LCOE. However, it is of interest to know at which point the turbine start
improvements becomes a loss economically. Therefore, the sensitivity factor for the cost
functions were increased, and the contour plot sensitivity factor was reduced, until a “break-
even” point was found. This point was defined as the point at which the LCOE would increase
from implementing start improvements to the DSG-STPP. Within this analysis, the OPEX and
CAPEX sensitivity factor we’re treated as one and the same, as the OPEX factor alone was
shown to have very little impact in Table 12. The results of this analysis are shown in Table 13.
Table 13. "Break-even" points for the costs and thermodynamic effectiveness' of the start improvements with respect to the LCOE where no start improvements are implemented.
Case LCOE0steps
[USD/MWh] LCOE4 steps [USD/MWh]
Cost sensitivity factor = 13,65 211,94 211,93
Contour sensitivity factor =0,11 211,94 212,01
As Table 13 shows, approximative “break-even” points for the start improvements are reached
when the cost assumptions made are increased by a factor of 13,65 (1265%) or when the
assumptions regarding the thermodynamic efficiency of said improvements is decreased by 89%.
46
6 Discussion This chapter critically analyzes the assumptions made and results obtained. It is split into four
main parts, the first commenting on the assumptions made, the second analyzing the results from
the sensitivity analysis, the third comparing the results to similar studies as well as analyzing
eventual differences and the last suggesting future related areas for improvement.
6.1 Contributions to model
All additions to the DYESOPT software made in conjunction with this study have all come with
a number of assumptions. Below, the validity of these assumptions will be discussed, as well as
their effect on the results as a whole.
6.1.1 Turbine heating and cooling
The newly implemented method of dynamically simulating both heating and cooling, while
eliminating some rough assumptions, is based on a couple of estimates. For instance, the
temperature of the turbine is still assumed to be uniform when using the lumped capacitance
method. The heat transfer coefficients are based on rather rough estimates, such as the turbine
being modeled as a cylindric tube, using steam inlet conditions to calculate the Reynolds and
Prandtl numbers. In reality however, the turbine geometry is somewhat different, and the
Reynolds and Prandtl numbers change as the steam passes through the turbine blade rows,
causing the heat transfer coefficient to change as well.
Although, this must be put in contrast with the previous way of calculating the turbine
temperature, where the turbine was assumed to have the same temperature as the nominal steam
conditions at shutdown and the same lumped capacitance method was used for the cooling
procedure. Compared to this previous case, the dynamic way of handling the turbine heating
process increases the accuracy in cases were the turbine is run for a short amount of time.
6.1.2 Turbine start-up modeling
As described in Section 4.2, two different ways of accounting for turbine ramping was
implemented; one where the actual start-up curves from the Ivanpah plant was used and one
linear ramping method within TRNSYS using the turbcontrol component. From a theoretical
standpoint, the first method of implementing start curves in the post processing step is much
more justifiable than the second one. This is due to the fact that actual start curves are used,
which significantly adds to the method’s accuracy. What the second method lacks in accuracy
however, it gains in robustness. While the assumption of a linear relationship between the turbine
power output and time by no means is optimal, it still serves as a more reasonable alternative
than that of an instantaneous ramp to nominal power. Therefore, this method serves a purpose
as a “backup” when no start curves for the plant that is modeled are available.
Another problem with using the linear ramp in turbcontrol however is the fact that it works
poorly with start improvements in its current stage. This is due to the fact that the partial power
production during ramping, when done in the dynamic simulation, stays constant and unaffected
by the extent of the implemented improvements.
47
6.1.3 Start improvement costs and benefits
Commenting on the modeled benefits and penalties/costs for the implemented start
improvements, it can be said that the benefits of said improvements are, in general, reinforced by
better assumptions and approximations. For instance, the thermodynamic benefits of the added
start improvements resulting from faster ramping due to higher turbine temperatures are based
on numerical simulation data as well as turbine start curves from the actual Ivanpah turbines. The
assumptions regarding costs however, are highly empirical and should be taken with a grain of
salt. Other things that should be scrutinized are the approximations regarding the reduced
maintenance costs resulting from less accumulated EOH. Even though the cost posts affected
surely will decrease as a result from lower annual EOH, the extents at which they do are highly
uncertain.
6.2 Results & Sensitivity analysis
The figures obtained from the analysis in Section 5.3 show a number of interesting findings.
Firstly, they show that the investment costs related to the start improvements have a very small
effect on the total CAPEX. This is because the investment costs related to the auxiliary boiler, as
well as the electric heat blankets, are in the magnitude of 106 USD, whereas the total CAPEX, as
shown in
48
Table 8, is in the magnitude of 108 USD. This rather large difference in investment costs stems
from the low heat blanket and auxiliary boiler capacities required to upgrade the turbine start
curves. Because of these low investment costs that are paired with lower OPEX figures and
higher annual electricity production, it is found that the overall levelized cost of electricity can be
significantly reduced for the Ivanpah plant by implementing these changes.
Comparing the sensitivity analyses in Table 10, Table 11 and Table 12 further strengthens the
previous analysis regarding the root cause of the start improvements performance within the
DYESOPT DSG model. It is apparent that reducing the thermodynamic benefits of the start
improvements have a greater relative effect on the overall LCOE than increasing the cost of said
improvements. This is due to the previously mentioned fact that the costs of the start
improvements are very low compared to their thermodynamic benefit. Hence, reducing the
thermodynamic benefit has a greater result on the LCOE than simply increasing the costs by a
certain factor.
The “break-even” points found for the contour plots and the costs associated with the start
improvements, summarized in Table 13, illustrate the magnitude at which these assumptions
must be off in order to make the start improvements act negatively from an economic point of
view. The numbers presented for both cases hint towards the fact that the assumptions must be
far off indeed if the implemented start improvements are to act negatively in this way. This may
suggest that even if the figures for reduced LCOE when implementing start improvements aren’t
accurate, the notion that start improvements have the potential of reducing the LCOE is indeed a
possibility.
6.3 Comparisons with previous work
Comparing the findings in this thesis with previous studies within the same field further validates
the results. For instance, [2] found that the introduction of temperature maintaining
modifications, in the form of heat blankets and increased barring speed, that yielded 80% annual
hot starts could increase the annual power output of a DSG-STPP plant of similar size by 8 %.
This figure differs somewhat from the results obtained in this paper where the modifications,
which for 3 and 4 steps in mode 2 had a similar amount of hot starts, yielded an annual increase
of almost 10%. However, the other studies used an older version of DYESOPT, where the
partial power production during turbine ramping wasn’t accounted for. Since the turbine ramping
curves get much steeper as the starts get hotter, the difference in findings regarding the annual
power output can most probably partly be attributed to this. Another significant factor that has
changed from the older version is the turbine heating and cooling described in section 4.1, to
which the difference in results also may be partly attributed.
6.4 Future Work
A significant part of the work process for this thesis was spent on further developing the
DYESOPT DSG-STPP model. However, further additions and refinement have the potential to
further improve the model. For instance, a more accurate way of approximating the reduced
maintenance costs due to lower EOH is warranted. Furthermore, there is a need of implementing
a way to change the turbine heating and cooling model, as well as the start-curves, depending on
the plant size, since the Ivanpah parameters is used for all plant sizes within the current model.
One way of doing this could be to make the start improvements work with turbcontrol ramping
49
in TRNSYS. In addition to this, the contour plots defining the effects of the start improvements
will have to be adapted to suit other plant configurations than the one of Ivanpah.
Adding more combinations of start improvements would further add to the DSG model. For
instance, previous work from the solar group have investigated the effects of increasing the
barring speed during offline hours as a temperature maintaining modification. Adding some
economic penalties to these previous studies would allow an easy integration with the new model.
This could further add to the optimization process, where the effect of different start
improvement combinations may be examined when adding new start improvements into the
model.
In order to fully assess the techno-economic effects of the examined start improvements,
attention should be focused on making more accurate assumptions regarding costs as well as the
thermodynamic benefits of the heat blankets and increase of gland steam temperature. The
process in DYESOPT leading up to this point however, is based on well reinforced assumptions.
50
7 Conclusions The changes made to the DYESOPT modeling tool within the context of this project has
enabled a new platform for examining the economic benefits and penalties of implementing
turbine start improvements in a DSG-STPP. The model has also been improved with respect to
accuracy in the strict thermodynamic processes associated with turbine startup.
The implementation of start improvements consisting of electrical heat blankets and an increase
of the gland steam temperature were shown to have the potential of reducing maintenance
requirements, increasing the annual power output and reducing the overall LCOE for the
modeled DSG plant. It was observed that a LCOE reduction of 9,46% could be achieved for the
Ivanpah plant when implementing these measures, provided that the assumptions regarding their
costs and thermodynamic properties were correct.
The reduction of the LCOE due to the implemented start improvements mainly stemmed from
the increase in power production due to faster start-up times and higher turbine availability.
However, lower maintenance requirements also stood for a significant part of the cost
reductions. Additionally, the cost penalties for the start improvements in the form of investment
costs and parasitic consumption increases were found to be low compared to the techno-
economic benefits of said improvements.
The sensitivity analysis showed that either an increase of the assumed cost by approximately
13,65 times or a reduction of the start improvements thermodynamic effectiveness by 89%
would result in the start improvements yielding a negative effect on the overall LCOE. This
suggests that even if the assumptions regarding costs may be inaccurate, start improvements still
seem like a promising option to reduce the LCOE for a DSG-STPP.
51
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