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

i

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.

iii

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

vii

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

viii

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

1

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.

7

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

9

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.

10

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

11

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

14

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.

16

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.

17

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).

22

(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.

23

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.

24

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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