Master thesis 30 hp

Master programme of Energy engineering 300 hp

Department of applied physics and electronics. Spring 2020

Transient Performance of Siemens SGT-750 and SGT-

800

Modeling and Simulations of Industrial Gas Turbines on Island

Grids

Alexander Raddum

.

Sammanfattning

Med en ökande mängd distribuerad energiproduktion, främst i form av förnyelsebaraenergikällor såsom vind och solkraft krävs ökat fokus på nätstabilitet. Ett atraktivtalternativ för att åstadkomma denna stabilitet är gasturbiner, mycket tack vare derasförmåga att arbeta snabbt och flexibelt. Detta examensarbete, utfört för Siemens In-dustrial Turbomachinery AB, syftar till att utvärdera transientkapaciteten hos derasgasturbinmodeller SGT-750 och SGT-800. I arbetet har modeller för ett önät utveck-lats i modellerings och simuleringsmiljön Dymola. Modellerna har verifierats mot dataför verkliga provkörningar och visat sig överensstämma väl med verkligheten. Model-lerna har sedan körts för att utvärdera kapaciteten hos gasturbinerna i specika scenarion.Utöver detta har simuleringar körts med alternativa bränslen innehållande varierandemängd vätgas: 25, 50, 75 och 100 volymprocent vätgas. Resultaten visar att SGT-750och SGT-800 klarar stora lastpåslag motsvarande 50% av märkeffekt i varierande omgivn-ingstemperaturer (-30, 15 och 30oC). Den dubbelaxliga SGT-750 påvisade något störrefrekvenstapp i dessa simuleringar. Vidare visade simuleringar med vätgasbränslen ingastörre avvikelser vid lastpåslag med undantaget 75% vilken innehöll en stor mängd in-ert gas. Utöver dessa resultat diskuteras även föreslagen κ-parameter för att kvantifierastabilitet och kapacitet vid större lastpåslag och effekterna av tröghetsmoment vid tran-sienter. De begränsande faktorerna utreds till att vara gasgeneratorns rotationshastighetoch turbinernas inloppstemperatur. Vätgassimuleringarna gav slutsaterna att körningarär fullt möjliga men modellen för brännkammare bör ses över för att få representivaresultat. Vid körningar med gaser som uppvisar låg volymetrisk energidensitet bör hän-syn tas till volymer och tryck i gassystem. Slutligen ges rekommendationer för framtidaarbeten på området.

Abstract

Distributed energy production in the form of renewable energy sources are expected toincrease in the coming years, a consequence of this is instability of the power grids due tothe stochastic nature and lack of inertia of renewable energy sources. In addition, smalland local, so called island grids, are on the rise and these system may present an evenhigher sensitivity to frequency fluctuations. In these applications gas turbines are anattractive option owing to the quick start capabilities, flexible fuel options and reliableoperation.The aim of this thesis is to evaluate the transient capabilities of the Siemens SGT-750double shaft and SGT-800 single shaft industrial gas turbines in island grid settings,through simulations of substantial load increases in varying ambient settings. Further-more the possibility of using hydrogen fuel as a renewable option to the standard naturalgas will be evaluated.

This thesis provides a model of a simple island grid for load sharing between twoor three turbines. The model was tuned to real life test data for the two gas turbinesconsidered. In order to evaluate the capabilities of the turbines simulations were run incold (-30oC), hot (30oC) and ISO (15oC) conditions, evaluating the maximum instantload increase capabilities. Case studies were also run on island grids containing two orthree turbines in order to determine the frequency response in case of an event. Case Aregarded a scenario in which two turbines ran on 50 % of rated power and one tripped,case B regarded three turbines working on 33 % of rated power and one tripped out.Lastly, the maximum load increase cases with hydrogen fuel mixes (25, 50, 75 and 100%hydrogen by volume) were considered.

The results suggest that the SGT-750 and SGT-800 gas turbines are capable of hand-ling scenarios on reasonably dimensioned power systems, with both machines capable ofrecovering instant load increases of over 50 % of the rated power. The findings shows thatshort periods (<10 s.) of allowed overfiring temperatures are necessary for the transientperformance for the most extreme scenarios of high ambient temperatures and large loadincreases (around 50% of rated power). Furthermore an empirical κ parameter, relatedto inertia and operational stability is discussed in order to compare GT load increasecapability. The relevance of inertia and dynamic response is discussed and conceptuallysimulated to highlight the their role in gas turbine transient response.

The hydrogen simulations, aside from the 75% case, showed little difference fromnatural gas in transient scenarios. The 75% hydrogen fuel consisting of high amounts ofinert gas however, rendered the turbine not able to withstand substantial load increases.The hydrogen simulation results are suggested to be accounted for by the rather simplecombustion system and the energy densities of the gases.

Acknowledgements

I would like to thank and acknowledge the support, patience and help provided by mysupervisors at Siemens industrial turbomachinery AB Anna Sjunnesson and John Svens-son. Without your tremendous expertise and input this would not have been possible,you have taught me a lot. Thank you for staying in contact every day throughout theCovid pandemic of 2020. Futhermore I would like to thank the performance departmentat Siemens Industrial Turbomachinery for their hospitality and helping efforts. Lastly Iwould like to thank my supervisor, Dr. Gireesh Nair at the department of applied physicsand electronics at Umea University. For his support, patience and meticulous review ofthe following work, and a lot of other work throughout my time as a student.

Nomenclature

List of AbbreviationsCCGT Combined cycle gas turbine RES Renewable energy sourceCT Compressor turbine ROCOF Rate of change of frequencyDG Distributed Generation TIT Turbine inlet temperatureGT Gas turbine TOT Turbine outlet temperatureHHV Higher heating value VGV Variable guide vaneIGV Inlet guide vaneISO Standard conditionsOCGT Open cycle gas turbinePID Proportional integral derivativeppmvd parts per million by volume

Symbols unit Greek symbols unitcp Heat Capacity at constant p J·(kg·K)−1 γ heat capacity ratioh Specific enthalpy kJ·kg−1 δ Anglef Frequency Hz ∆ changeJ Moment of inertia kg·m2 φ relative humiditym mass flow rate kg·s−1 ϕ power anglep pressure Pa η efficiencyP power W π pressure ratioq specific heat J·kg−1 ρ density kg·m−3

Q heat J τ torque NmR gas constant J·K−1 ·mol−1 ω angular velocity rad·s−1

T temperature oC / KU internal energy JV mean velocity m·s−1

w specific work J·kg−1

W work J

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Project description 22.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Siemens SGT-750 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.4 Siemens SGT-800 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Literature review 4

4 Theory 94.1 Gas turbine principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94.2 Combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.3 Other factors effecting performance . . . . . . . . . . . . . . . . . . . . . . 154.4 Mechanical and electrical torque balance . . . . . . . . . . . . . . . . . . . 164.5 Gas turbine control overview . . . . . . . . . . . . . . . . . . . . . . . . . 194.6 SGT-750 and SGT-800 control systems . . . . . . . . . . . . . . . . . . . . 20

4.6.1 SGT-750 control system . . . . . . . . . . . . . . . . . . . . . . . . 204.6.2 SGT-800 control system . . . . . . . . . . . . . . . . . . . . . . . . 23

5 Method 255.1 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.2 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.3 Dymola model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.4 Validation method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.5 Simulation case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6 Results 346.1 Results of validation cases . . . . . . . . . . . . . . . . . . . . . . . . . . . 346.2 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.2.1 Instant load increases under different ambient conditions. . . . . . 366.2.2 Island grid cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386.2.3 Hydrogen fuel cases . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.3 Remarks on the results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406.3.1 Limiting factors for the SGT-750 . . . . . . . . . . . . . . . . . . . 416.3.2 Limiting factors for the SGT-800 maximum instant load increase . 426.3.3 Hydrogen simulation results . . . . . . . . . . . . . . . . . . . . . . 42

6.4 On the differences of the machines . . . . . . . . . . . . . . . . . . . . . . 43

7 Discussion 457.1 The island grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457.2 Maximum capability simulations . . . . . . . . . . . . . . . . . . . . . . . 457.3 Hydrogen simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . 477.4 Reflections and areas of improvement . . . . . . . . . . . . . . . . . . . . . 47

8 Conclusions 49

9 Future work 50

1 Introduction

1.1 Background

The power grids of the future will possibly consist of a large number of producers util-izing renewable energy sources (RES). The International Energy Agency (IAE) predictsrenewable-based capacity will increase 50% globally between 2019 and 2024[1]. The in-crease in RESs seen today mainly consists of solar and wind which, due to their stochasticnature caused by their dependency of weather conditions, are unreliable and hard to pre-dict [2]. In addition to the increased share of RES microgrids are also on the rise, theseare localized electricity grids which can work in synchronous with larger (national) gridsbut also separately (island mode)[3]. No strict definition applies to microgrids, they arevaguely defined as smaller grids able to run independently of national grids. Some of theadvantages of microgrids are (but not limited to) reduced line losses, resilience to largescale disruptions, since they can work parallel to national grids in cases of emergency,and easier RES integration[3]. RESs present problems when implemented in larger scaleto existing grids, and microgrids can be designed in order to handle variable generationthrough storage and power balancing [3].

In order to secure a consistent power supply several technical solutions for energystorage exist depending on the time frame (e.g. flywheels, batteries and pumped hydropower)[4]. The most common methods of energy storage as of 2016 were pumped hydropower and battery storage[5], these methods, however, presents some issues respectively.Pumped hydro power requires the right geographical circumstances (i.e. considerablespace and elevated bodies of water) [4], while batteries, depending on chemical compos-ition, experience low energy density and economical and safety concerns[6].

Other solutions to the problem of fast transients, oscillations of the grid frequency,due to increased shares of RESs involves highly flexible conventional power plants suchas gas turbines (GT) or steam plants (SP) [7]. Gas turbines in particular are faster thanSP (with a load ramp capability of 20% of maximum load per minute as opposed toabout 6% of maximum load per minute for SP)[7] but slower than batteries, however,they are able supply more power to the grid and provide large quantities of power[8].Gas turbines could be a great option for microgrids due to their flexibility, in termsof swiftness, as mentioned earlier [7], fuel capabilities and lower emissions as comparedto SPs [9]. Furthermore GTs do not require require large boilers (as SPs do), specificweather conditions (as RESs do) or particular settings, as they can be delivered aspackages fitting within a single container. In order to further reduce the environmentalimpact hydrogen fueled GTs may also constitute an alternative. It is of importance toevaluate the capability of the GTs to quickly and forcefully respond to transients causedby load increases or frequency drops via increases in power output, but also fuel flexibility,before implementation.

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2 Project description

2.1 Objectives

This thesis will consider the performance of Siemens latest single and double shaft indus-trial GTs: SGT-750 and SGT-800 (57 MW) under different operational conditions. Thecore models of the engines and control systems that will be used are developed by Siemensindustrial turbomachinery AB in the modeling and simulation environment Dymola. Inaddition to the existing models models for simulations of island grids are to be developed.The developed models are to be tuned to real on site data and Simulink models in orderto produce representative results. The models will then be used to predict the behaviourof the SGT-750 and SGT-800 under different, predetermined, transient events and fuelcompositions.

The goals of this master thesis is to evaluate and quantify the transient capabilitiesof the SGT-750 and SGT-800 in terms of maximum transient load capabilities underdifferent ambient conditions and scenarios. It will also investigate the prospects andimplications of implementing hydrogen gas mixtures as fuel to reduce the environmentalimpact.

The purpose of this work is to investigate, in order to ensure, Siemens IndustrialTurbomachinery products are competitive in future energy systems consisting of largenumbers of distributed producers and possibly grids subjected to an increase of transients.The project will be done at the SIT AB Offices in Finspang, Sweden for the PerformanceR&D group.

2.2 Limitations

This thesis is limited to the evaluation of the Siemens SGT-750 and SGT-800 gas turbines.In addition, the following limitations applies: No major changes of the gas turbines willbe implemented (if not stated otherwise), no specific power system implication analysiswill be undertaken. The focus will be on the overall performance of the existing enginesand the operational aspects causing them. Thus excluding component design aspects,aerodynamics and exact heat transfer analysis.

2.3 Siemens SGT-750

The first GT considered in this thesis is the SGT-750, a twin shaft industrial gas turbinefor mechanical drive or power generation applications launched in November 2010 [10].General power generation performance data of the SGT-750 data is shown in tab. 1. TheSGT-750 is a flexible machine capable of of liquid and gaseous fuels, fast start capability(full load in <10 min) and best on the market NOx emissions. It offers high reliabilitywith only 17 scheduled maintenance days in 17 years. A cut through of the SGT-750showing its main parts can be seen in fig. 1. The SGT-750 has two VGVs and fourcompressor bleed valves.

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Table 1 – SGT-750 simple cycle power generation data [11]

Power 39.8 MWe

Efficiency 40,3 %Shaft speed 6100 rpmπ 24.3:1Exhaust gas flow 115.4 kg/sExhaust temperature 468 oCNOx emissions <15 ppmvd

Figure 1 – The Siemens SGT-750 cut through showing the compressor (13 stages) on theleft, the burner cans, CT (two stages) and power turbine (two stages).

2.4 Siemens SGT-800

The second GT considered is the single shaft SGT-800, originally launched in as GTX100capable of 45 MW output [12]. The later, updated versions of the SGT-800 are capableof up to 62 MWe. The data for the B5 version, considered in this thesis, is found intab. 2. As its twin shaft counterpart the SGT-800 is capable of low NOx emissions andhigh reliability, proven over more than 7 million operating hours, from the more than 370units sold [13]. The SGT-800 has three rows of VGVs and two compressor bleed valves.

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Table 2 – SGT-800 simple cycle power generation data [13]

Power: 57 MWe

Efficiency 40,1 %Shaft speed 6600 rpmπ 21.6:1Exhaust gas flow 136.6 kg/sExhaust temperature 565 CNOx emissions <15 ppmvd

Figure 2 – The Siemens SGT-800 cut through showing the compressor (15 stages) on theleft, the circumferential burners, and turbine (three stages).

3 Literature review

Transients and disturbances affecting the quality of the power delivered to the electricalgrid can be a result of external events (e.g. lightning) or internal issues, such as utilitiesconnected to the grid or producers delivering uneven frequencies or voltages. Issuesmay also arise when large inductive loads (e.g. inductive motors) are introduced due toreactive power imbalance. The increasing integration of distributed generation (DG) fromRESs contributes to these issues and can cause dips and steady state voltage rise, voltageflicker and harmonics(which in turn increases the current in the system by propagatingfluctuations)[14]. Fluctuations of frequency in power grids are countered by the inertia

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of the power grid caused by heavy rotational masses (turbines and generators) connectedsynchronously to the grid and capacitors, the increase of RESs may lead to a low-inertiapower grid since they do not posses these qualities in general [15]. Wind turbines, whilerotating, are in general decoupled from the grid through converters while photovoltaicsexperiences virtually zero inertia, these problems are amplified on island grids that areinherently sensitive due to the size and potential lack of backup power as required [15].The problems experienced by RESs may be mitigated in several ways, such as energystorage and inertia emulation discussed in [15, 16]. With inertial contributions and ondemand power supply the capabilities of GTs for the purpose of power balancing andgrid support will be evaluated.

In order to evaluate the capability of GTs as a tool for balancing power and transients,a simulation approach will be undertaken. Investigations of load sharing of one andseveral GTs and load distribution in the case of failure or trip (emergency shutdown) willalso be undertaken. For the applications regarded in this thesis, transient behaviour isto be simulated, hence a review of previous work on the subject will be undertaken inorder to provide insight and a foundation to work from. The review will, in addition,investigate factors affecting GT performance.

Firstly, a brief review of the main factors influencing thermodynamic performanceof GTs are regarded, these factors were investigated by Rahman et al. [17]. In [17]the thermodynamic relations are studied, and parametric simulations were undertaken.The findings suggests that compression ratios, ambient temperature, air to fuel ratio andisentropic efficiency strongly influences the thermal efficiency of a gas turbine power plant.The results shows that thermal efficiency is increased mainly by: an increase in turbineinlet temperature, a decrease in ambient temperature and an increase in compression ratio[17]. These finding were also substatiated by Saif and Tariq in [18]. From parametriccalculations, it was concluded that an increase in ambient temperature will yield lowerefficiency and specific work output while lower temperatures leads to the contrary.

These findings regard thermodynamics, the influence of control systems are discussedin later segments.

Asgari et al. released a comprehensive paper on the matter of transient simulationsof single shaft industrial GTs in 2014 [19]. In said paper two approaches to transientGT simulations are reviewed: a) Neural networks (black box approach) and b) A Modelbased on physical and thermodynamic properties (white box approach). In [19] fourdistinct data sets were considered for simulation and validation. The sets were definedby four binary parameters1: I. Starter is on or off: 1 or 0. II. The GT is connected tothe grid or not: 1 or 0. III. Customer trip happens or not: 0 or 1. IV. The flame ison or off: 1 or 0. The following data sets were considered: [0 0 0 1], [0 1 0 1], [0 1 0 1a] and [0 1 0 1 b], where the first two sets were used for tuning of models and the lasttwo were used for validation [19]. The simulations carried out all considered cold startof the GT in addition to the previously mentioned factors. The simulations carried outwere able to predict rotational speed (N), compressor pressure ratio (πc), compressor

1A data set [0 1 0 1] will, by this definition, mean that starter is off, the GT is connected to the grid,customer trip does not happen and the flame is on.

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outlet temperature (T02) and turbine outlet temperature (T04) for all data sets with bothmodels. The results presented RMSE of 0-4% for both models, they presented largererrors at the beginning of the simulations until they stabilized to the response [19].

Furthermore, a component based Simulink library GasTurboLib has been developedby Panov [20]. In the publication a meticulous description of the component modelingis undertaken, discussing the physical and thermodynamic assumptions as well as thecontrol system layout. The paper proceeds by verifying the model for a single shaftturbine (Siemens SGT-100-1S) and a twin shaft model (Siemens SGT-400), the resultspresented are in agreement with engine test data for load acceptance and startup [20].

In a paper by Bahrami et al. the transient performance (i.e. short term response),in particular during frequency drops, of GTs are discussed [21]. In [21] the differencesof single- and double-shaft GTs are discussed, it is pointed out that while the singleshaft turbine inherently has greater inertia and less lag, the mass flow rate of air willdecrease during frequency drops, due to the fact that the turbine provides torque directlyto the compressor, thus leading to potential unit instability. The control system of GTsis evaluated and it is stated that the temperature controller (an overview of a generic GTcontrol system presented in [22]) , struggles in the case of fast load changes. The governoradjusts fuel flow in to the combustion chamber during normal operation in order to matchthe required power output, the temperature controller maintains the temperature of thecomponents by controlling fuel flow and IGVs. It is worth noting that the control systemselects the lowest value of the temperature control signal and the speed governor signal inorder to get smoother transitions [21] (also discussed in [23]). The control system couldbe limited by the speed of the IGV actuators, thus rendering air flow the limiting factorfor fast response to load changes [21].

In order to improve the transient performance of GTs a separate steam injectionsystem which works on a separate controller (event-based controller) was suggested in[21]. Steam injection will allow for higher power outputs due to an increase in massflow and lowered risk of overheating and may as such improve performance. For smallamounts of steam injections there is no need for modifications of the gas turbine or thecombustion chamber [24]. The system was simulated for a 160 MW heavy duty GT. Theresults of the simulations indicated that steam injection improved the response to a stepchange in GT load while slightly reducing the compressor surge margin. The results alsoshowed a higher allowed change load, in particular at full load conditions, this is due tothe reduced temperature which allows the GT to continue running without triggeringthe temperature controller [21].

In a publication by Meegahapola the dynamics of gas turbines during frequencyvariations in power networks were characterized [25]. The model used for simulationis based on the model by Rowen [26]. The scenarios from [25] that were consideredwere operation below rated power output, load reduction and generator outage/loadincrease. Simulations were conducted on combined cycle GTs (CCGTs) and open cycleGTs (OCGTs), the rated power output was 400 MW, in the case of OCGTs two 200 MWunits in parallel were considered. Furthermore, the system considered operated with an1800 MW load, the CCGT had an inertial constant of 8 s and the OCGT had an inertial

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constant of 4 s, both setups were rated at 500 MVA. The test network had an inertiaconstant of 6 s and short-circut capacity of 5000 MVA and a frequency droop of 4 %was set. Simulations of load increased considered an additional 200 MW of load to thesystem. The CCGT presented a frequency drop to 49.18 Hz after a few seconds (from50 Hz), the OCGT dropped to 49.22 Hz minimum [25]. Furthermore the power outputof the CCGT reduced beyond the OCGT after 6.8 seconds of simulation worsening thefrequency stability of the system [25]. The causes of the phenomena found was explainedby lowering of compressor speed (single shaft OCGT), and for the CCGT an increasein the exhaust temperature finally leading to an override by temperature controller (asdiscussed in[21, 25]) causing altering of the IGVs2 resulting in a power reduction. Thetemperature spread in the load increase scenario was however low, likely not leading tolean blow out (LBO). The GTs were also simulated operating below rated power (380MW). When the load was reduced, the exhaust temperature did not rise as rapidly aswhen it was increased, this in turn led to to power increase. Both GTs frequenciesdecreased to about 49.3 Hz (from 50) and none of them experienced temperature spreadscausing concerns for LBO [25]. The simulation regarding load reduction assumed aninitial load of 2000 MW reduced to 1800 MW and both GTs operating at rated poweroutput. Both systems showed an increase in frequency to about 50.8 Hz, the temperaturecontroller never over rode the governor leading to both GTs reducing power outputby about 24% of initial output [25]. The CCGT presented a high spreads of exhausttemperatures due mainly due to increased air to fuel ratio due to inertia causing continuedair pumping when the fuel injection is reduced [25]. Finally, [25], concludes that thetemperature control system (also discussed in [21]), in this paper considered for theCCGT, may lead to increased turbine power output in the case of frequency changes.

When considering frequency response of systems the rate of change of frequency(ROCOF) is a recurring measure, especially in cases considering RES [25, 27, 28]. Aspreviously mentioned it is of importance to avoid frequency fluctuations in order to avoidinterruption, additional generator trip or even blackouts, for these purposes ROCOF canserve as an indicator [30]. In large grids ROCOF is also an important measure to avoidunwanted islanding, which also may pose risks [27]. In the case of GTs specifically,ROCOF can lead to LBO [25].

In a paper by Kakimoto [29] a parametric study of GT power plants (CCGT) duringfrequency drops is conducted. The authors uses a variation of the Rowen ([26]) modelto investigate factors influencing the behaviour of the GT. The findings suggest that thetemperature control overrode speed control in the case of frequency drops and restrictsthe fuel flow about the initial value [29]. It is also found that if the IGVs open fully,the temperature control and the frequency determines the fuel flow and thus the poweroutput [29].

In order to reduce greenhouse gas emissions several parameters may be considered,including but not limited to overall efficiency and fuel choice. Hydrogen may be a fuel

2In the simulations carried out the OCGT considered was not equipped with controlled IGVs [25].

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of the future since the combustion of hydrogen

H2 +1

2O2 = H2O −∆H (1)

releases no greenhouse gases. In addition to the environmental benefits hydrogen hasmore than double the energy density of conventional fuels at around 120 MJ/kg, how-ever the volumetric energy density is less than half that of conventional fuels [31]. Forthe time being, the volumetric energy density poses practical problems with regards totransporting and storing hydrogen [32]. Some of the solutions proposed for the problemsof storage and transportation of hydrogen includes compression, liquid storage and solidstorage all with their potential difficulties and issues [32]. Several ways of producing hy-drogen have been proposed including reforming of fossil fuels, production through RESs(biofuels or via electrolysis), gasification and thermocemical pyrolysis (water splittingthrough heat) [33]. In [33] it is concluded that thermochemical pyrolysis and gasificationare economically viable options, furthermore it is pointed out that the issues of storageand safety concerns need to be evaluated further.

In [34] the prospects of hydrogen as a fuel in heavy-duty GTs designed for natural gasis discussed. The authors runs simulations considering a CCGT consisting of a single shaftGT capable of 300 MW power output and steam turbine capable of of over 130 MW poweroutput. The effects of hydrogen combustion on turbomachinery is summarized by threeeffects: variation of enthalpy drop in the expander, variation in flow rate affecting theturbine-compressor matching and variations in heat transfer in turbine blades affectingthe cooling system performance [34]. Since the volume flow rate will differ for differentfuel compositions the compressor and turbine will land on different operating pointsin their characteristics (further described in later sections). The authors carries outcalculations through their developed code (from [35]) for three strategies of operation:increased π pressure ratio) and reduced TIT, re-engineering of the machine (increasingflow area and changing blade cooling) and VGV operation with lowered TIT. The variableVGV case shows slight differense for no-diluted hydrogen fuel with slightly lower TIT andTOT, and marginally increased efficiency. Similar results go for the diluted cases withsteam (6.78 dil./fuel mass ratio) and nitrogen (14.44 dil./fuel mass ratio) [34]. Whilehydrogen reduces CO, CO2 and HC emissions a potential drawback of hydrogen fuelsmay be an increase in NOx with increased hydrogen content [36].

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4 Theory

4.1 Gas turbine principles

A GT consists of three main components: compressor, combustion chamber and turbine.The two types of GTs considered in this paper, single shaft and double shaft, can beseen in Fig. 3. The single shaft GT, as the name suggests, dispenses all work throughone shaft, driving both the compressor and the load (e.g. generator). The double shaftGT utilizes a second turbine for the load thus allowing the gas generator (GG) to workon separate speeds independent of the power turbine (PT). The double shaft GT is ableto produce higher torques at lower rpm, but may also "rush" (overspeed) when load isdecreased, which is not the case for single shaft GTs due to higher inertia [37]. Moreover,single shaft GTs may experience an increase in TOT (thus decreasing η3) and at lowerfrequencies close in on the surge margin while the double shaft GT is free to work at itsdesign point regardless of PT operation [37].

Figure 3 – Schematic of single shaft (upper) and double shaft (lower) gas turbines, wherethe arrows represents flow of air, gas and exhaust.

In order to describe the GT one may start with the well established theory of conservationof energy [38]

δU = δQ+ δW, (2)

stating the internal energy of a system δU is the sum of work done δW (by or on thesystem) and the heat, δQ, entering or leaving the system. The GT works on an openpower cycle, the Brayton cycle proposed by George Brayton in 1870 [39], and consists of 4main stages shown in fig. 4. The cycle displayed however is ideal (∆scomp = ∆sexp = 0),since in a real cycle entropy will increase and the vertical lines will be slightly tilted. Theoffset from vertical in the T-s diagram is due to the isentropic efficiency ηis.

3This may not be true for CCGT operation where an increase in TOT increases heat transfer in theboiler.

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Figure 4 – Entropy (s) and temperature (T) diagram of the ideal Brayton cycle including:(1-2) compression, (2-3) combustion and (3-4) expansion.

Using the relation for conservation of energy and neglecting changes in kinetic and po-tential energy, the Brayton cycle in steady state operation is described (on a unit massbasis) by

(qin − qout) + (win − wout) = hin − hout, (3)

where q is heat, w is work and h is enthalpy. From eq.3 the a relation for each maincomponent can now be written

w12 = −(h2 − h1) = −cp(T2 − T1)q23 = h3 − h2 = cp(T3 − T2)w34 = (h4 − h3) = cp(T4 − T3)

(4)

where cp is heat capacity (cp = ∂h∂T |p). Still considering the assumption of neglecting

kinetic and potential energy the cycle efficiency is described as the work output dividedby the heat supplied, i.e.

ηBrayton =cp(T4 − T3)− cp(T2 − T1)

cp(T3 − T2). (5)

Considering the isentropic p − T relation (no heat transer to the surroundings and noirreversibilities [39])

T2T1

= πγ−1γ =

T3T4, (6)

where π is the pressure ratio (π = p2/p1 = p3/p4) and γ is the pressure ratio of the gas,the efficiency can now be expressed as

ηBrayton = 1−( 1

π

) γ−1γ , (7)

giving a description of the efficiency only depending on the pressure ratio and nature ofthe gas [40]. Since, however, the specific work output can be expressed as

w = cp(T3 − T4)− cp(T2 − T1), (8)

10

the expressionw

cpT1= t

(1− 1

πγ−1γ

)−(πγ−1γ − 1

), (9)

where t = T3/T1, shows the dependency of temperatures and the pressure ratio [40].The presented equations (2-7), as stated, are not representative of real life GTs, as suchfurther theory on methods to account for losses follows. To include the neglected termsof velocity, stagnation quantities (subscript 0) may be considered as

h0 = h+V 2

2

T0 = T +V 2

2cp

p0 = p

(T0T

) γ−1γ

(10)

based on the assumption of a perfect gas (h = cpT ), where V is velocity [40]. Furthermoreconsiderations of irreversibilities should be made to calculate the actual work neededand delivered by the components. for this reason the isentropic efficiency is introduced,expressed for the compressor and the turbine as [39]

ηc =wsw

=h02s − h1h02 − h1

=T02s − T01T02 − T01

.

ηt =w

ws=h03 − h04sh03 − h04

=T03 − T04T03 − T04s

.

(11)

where subscript s represents the isentropic state (states shown in fig. 4). Eq.11 representsthe ratio of the real and ideal performance. Temperature correlations can be written as

T02 = T01 +T01ηc

[1−

(p02p01

) γ−1γ], (12)

and

T04 = T03 − ηtT03[(

1

p03/p04

) γ−1γ

− 1

]. (13)

For calculations the isentropic efficiencies may be assumed, however, when designingGTs, or calculating performance at different pressure ratios, these are not sufficient[40].For the aforementioned purposes polytropic efficiency is introduced. The case for theintroduction of polytropic efficiency can be made based on eq. 6. Considering theisentropic efficiency of one stage of the compressor ησ to be constant it follows that

T2 − T1 =1

ησ

N∑0

(Ti − Ti−1s), (14)

11

where N is number of stages. As seen from eq. 11 (T2s − T1) = (T2 − T1)/ηc thus

ησηc

=

∑N0 (Ti − Ti−1s)

(T2 − T1s), (15)

here it is clear that the numerator will be of greater magnitude since it the distancebetween pressure lines will increase as entropy increases, and it will increase the morestages that are present [40]. In practice this means that the isentropic efficiency of thecompressor will decrease as pressure ratio increases [39], since some of the work inputwill result in heating of the gas. A similar reasoning goes for the turbine resulting in anincreased efficiency as the pressure ratio, and thus the temperature difference, increases.Temperature correlations for using polytropic efficiencies are [40], similar to eqs. 16-17,

T02 = T01 + T01

[1−

(p02p01

)X], (16)

and

T04 = T03 − T03[(

1

p03/p04

)Y− 1

]. (17)

where

X = (γ − 1)

/γ

ln(p2/p1)(γ−1)/γ

ln(T2/T2)(18)

and

Y =ln(T3/T4)

γ/(γ−1)

ln(p3/p4)((γ − 1)/γ). (19)

Continuing with the assumption of an ideal gas, gives ways to express the power output

Pt = m∆ho =

= mcp∆T0 =

= mcpT0,inηt

(1− π

1−γγ

t

) (20)

similarly for the compressor

Pc =mcpT0,in

ηc

(πγ−1γ

c − 1

). (21)

The overall characteristics of a given compressor can be represented in a compressormap also known as a characteristics diagram. An arbitrary compressor diagram canbe seen in fig.5, where the quantities displayed are normalized as to be representative atarbitrary ambient conditions. It is of essence that the compressor works to the right of the"surge line" in order to avoid damage to the compressor and performance deterioration.Surge is a complex phenomenon, which, in short, causes separation from the compressor

12

blades causing reversed discharge through the compressor potentially causing damage[45].

Figure 5 – Typical compressor characteristic.

As with the compressor, the turbine characteristics can be mapped, this can be usefulfor off-design performance calculations [45]. An arbitrary turbine efficiency map is shownin fig 6 in which the axis values are normalized and the different lines represents differentnormalized speeds. The non dimensional quantities are implemented for generalizationpurposes and can be found by the Buckingham Pi theorem

Π1 = ψ(Π2,Π3, ...,Πk−r), (22)

which states that a function of k variables in r dimensions can be expressed in k − rgroups (Π-terms) [43]. For a compressor these are

p02p01

,T02T01

,m√RT01

D2p01,ND√RT01

, (23)

these terms may be implemented to represent performance as in fig.5 and for a givenmachine at constant size working on the same fluid R, A and D can be omitted [40].

13

Figure 6 – Typical turbine efficiency characteristic.

Similar maps as described above of the SGT-750 and SGT-800 are implemented in thesimulation program used in this thesis.

4.2 Combustion

The addition of energy in the GT takes place in the combustion chamber where fuel,typically natural gas or liquid hydrocarbons, are burned. The combustion process isone of high complexity involving fluid dynamics and chemistry outside the scope of thisthesis. However, the basics will be touched upon in order to get a holistic view of the GT.In the combustion chamber fuel is sprayed and mixed with air with the goal of highlyefficient combustion and even temperature distribution. To achieve this the aerodynamicsof the combustion chamber must be consider so as to ensure complete combustion andsufficient mixing of fuel and air. In order to increase the overall efficiency of the GT it isdesirable to reduce the pressure drops occurring in the CC as well. The pressure lossesarise as a result of two causes: turbulence and skin friction and temperature increases[40].The stagnation pressure loss due to temperature increase can be described (assumingincompressible) as [40]

p01 − p02ρ1V 2

1 /2=(T2T1− 1), (24)

from the momentum equation. The losses form friction can be adequately be describedby the pressure loss factor [40]

PLF =∆po

m2/(2ρ1A2m), (25)

where Am is the maximum cross sectional area of the chamber. The combustion reactionof hydrocarbons in excess air is described as

CxHy +m(O2 + 3.76N2) =

= xCO2 +y

2H2O + (m− x− y/4)O2 + 3.76N2,

(26)

14

here m denotes number of moles of air and x and y the number of carbon and hydrogenatoms in the fuel(e.g. methane CH4). The energy released is the difference of the energyin the reactants and the products. Eq. 26 does not take into consideration hydrocarbons(e.g. CO) formed from incomplete combustion and nitrous oxides (NOx). NOx pollutioncause the formation of ground Ozone (O3) and leads to acid rain thus explaining theneed to reduce formation and release [46]. CO is an odorless gas which may cause severalhealth problems (comprehensive summary in [47]). The formation of CO can be reducedby increasing the available air in the combustion process allowing complete combustion[45]. Nitrogen does not take part in the combustion process, however nitrogen from thefuel and air may react to form NOx due to the conditions in the combustion chamber.NOx formation increases with an increase in pressure and temperature, while the inverseis true for CO [45]. In order to compare fuels in a useful way the Wobbe Index isintroduced

Iw =HHV√ρgas/ρair

, (27)

which is a measurement of the volume of fuel required for a certain amount of energy,in units MJ/Nm3. For a fuel mixture containing inert gases (e.g. CO2) some energy isrequired to heat these, which in turn means the required volume is not only dependingon the HHV [37].

4.3 Other factors effecting performance

Apart from thermodynamics the performance of a GT is, of course, depending on thedesign of the machine. In some ways the physical properties of the machine indirectlyeffects the thermodynamics discussed previously. Ambient conditions also influences theperformance which can be seen from eq.4 which can be written as

W12 = m∆h, (28)

for the compressor. Since the density of air is [39]

ρ =p

RaT+

φp

RsT, (29)

where R are specific gas constants and φ relative humidity, it is clear from eq.28 thatambient conditions directly effects the compressor power required, a similar reasoninggoes for the turbine. The work output of a GT will, as seen in eq. 20, depend on theTIT and is thus limited by the materials. The performance of the compressor can bechanged using VGVs, which in practice changes the geometry of the compressor as wellas the angles and swirl of the airflow. VGVs help in ensuring performance at sub-ratedoperation by controlling airflow. At the same time VGVs impact the TOT temperature.Opening of VGVs will serve to change the compressor characteristics in fig. 5 and allowfor a greater surge margin [45]

SM =πsπw− 1, (30)

15

where s denotes πc at surge and w the working point, describing the proximity to thesurge line. The so called velocity triangles for a stage of rotors and stators, often used([40],[45], [37]) is shown in fig.7. In fig.7 subscripts a and w represent axial (vertical) andtangential velocities respectively, U is the velocity of the rotors α represents the angleatt which the flow enters the rotors and stators and β are the rotor blade air angles.

Figure 7 – Velocity triangles of one stage of of rotors and stators.

Once again, utilizing conservation of momentum (and simple trigonometry4) the powerinput can be expressed as

W = mUCa(tanβ1 − tanβ2), (31)

so closing the VGVs reduces stage loading on the effected stage [45]. In turn the VGVangle will affect flow and thus temperatures by, in effect, changing the geometry of thecompressor.

4.4 Mechanical and electrical torque balance

GTs are interconnected with the grid via synchronous generators, meaning their frequencyis equal to that of the grid. The electrical frequency is described as a function rotor speedand number of poles of the generator by the relationship

fe =p

120nm, (32)

where p is number of poles, and nm is the rotational velocity of the machine in rpm. Theequation of motion for a synchronous machine, based on Newtons II:nd law is written as

Jdωmdt

= τm − τe, (33)

4Works under the assumption Ca1 = Ca2.

16

where J is the total moment of inertia (i.e. from turbine and generator rotor), ω is theangular velocity of the rotor axis relative to a reference, τe and τm are electrical andmechanical torque respectively[41]. Eq. 33 can be normalized by introducing the inertiaconstant defined as

H =Jω2

0m

2V A0, (34)

where ω0m is the nominal rotational angular velocity, and V A0 is the rated power outputVA (Volt Ampere) of the machine. H can simply be seen as normalized kinetic energysince Ek = Jω2

2 for rotation round a fixed axis [42]. Solving for J from eq. 34 andsubstituting into eq. 33 yields

2HV A0

ω20m

dωmdt

= τm − τe. (35)

By rearranging and utilizing the fact that V A0/ω0m (since τe = P/ω) gives eq.33 in perunit form as

2Hdωrdt

= τm − τe, (36)

where ωr = ωm/ω0m = ωr/ω0 (where ωr is the instant rotor electrical angular velocityand ω0 is rated electrical angular velocity, based on eq. 32). By introducing the terms

δ = ωrt− ω0t+ δo

dδ

dt= ωr − ω0 = ∆ωr

d2δ

dt2=d(∆ωr)

dt= ω0

d(∆ωr)

dt

(37)

representing angular position of the rotor with respect to a synchronous rotating referenceand where δ0 is δ at t=0 we arrive at

2H

ω0

d2δ

dt2= τm − τe, (38)

describing the rate of change of deviations of the rotor rotation from the synchronouselectrical rotational reference, the quantities are visualized in fig. 8.

17

Figure 8 – Simplified illustration of the quantities describing the operation of a synchron-ous generator, including a rotor and an electrical rotation frame.

Furthermore, eq. 38 can, when considering small deviations (∆) from nominal values(subscript 0)

P = P0 + ∆P,

τ = τ0 + ∆τ,

ωr = ω0 + ∆ωr,

(39)

be written asd∆ωrdt

=∆Pm −∆Pe

2H, (40)

giving a relationship between power deviations and deviations (from synchronous rota-tional speed) in rotor rotational speed [41]. Eq. 40 is represented visually in fig 9 toshow the impact of inertia on frequency response.

18

Figure 9 – Visualisation of the frequency response as a function of inertia and powerdeviations.

4.5 Gas turbine control overview

The GT control system is responsible for maintaining desired operation of the machinethrough fuel and air flow. The control system needs to monitor and adjust temperaturesand speed in order to deliver the required power but also reduce the wear of parts.The control system of a GT consists of several sensors, controllers and actuators inorder to achieve optimal operation. The main sensor inputs are temperatures, pressuresthroughout different locations of the GT and turbine shaft rotatinal speed [45]. Thesignals are compared to reference signals (set points) and the result is fed through a lowsignal selector and the actuators controlling fuel valves and VGVs.

The output of the closed loop control system is achieved by proportional (P), integral(I) or derivative (D) or a combination of the aforementioned. The controller comparesthe sensor value to a reference (e.g. maximum temperature) and the outputs (u) aredescribed by the following [48]

up = Kpε+ u0,

ui =1

Ti

∫ t

0εdt,

ud = Tddε

dt

(41)

where subscripts p, i and d represents the type of controller. In eqs. 41, ε representsthe error, uo the default output signal and Kp, Ti and Td constants. It can be notedfrom the equation for the P controller that using this type only will result in a residualerror since the output signal for a given error will be constant. In order to eliminate theerror the I part is included, while the P part is proportional only to the rate of changeof error, thus increasing output if the error is increasing rapidly. In order to achieve thecorrect governor output signal all measurements are compared to their set point and sent

19

through a minimum value selector. The principle is shown as a block diagram in fig. 10,where three controllers each responsible for different quantities (e.g temperature, speedand power) are compared and sent to the GT depending on the magnitude of deviationfrom the set point.

Figure 10 – Block diagram of the min value selection principle, here SP refers to setpoints of a given quantity and the GT output represents the corresponding quantity foreach controller.

4.6 SGT-750 and SGT-800 control systems

The layouts of the SGT-750 and SGT-800 governors differ slightly, the following sectionswill serve to describe the basics of the implemented control systems, since these, besidesthe physical properties of the GT have a great impact on the GT dynamic response. Sincethe control systems are proprietary, only the overarching principles will be discussed andas such some details are left out.

4.6.1 SGT-750 control system

The SGT-750 governor is comprised of several controllers, a brief description of theirfunction follows.

Starting with the FLC (frequency load controller) controller block two main con-trol methods are used for active power and frequency control, isochronous (constantfrequency) and droop control, the principle is shown in fig. 11. The frequency controlmode allows a turbine to adapt the power to the frequency present, thus the load on

20

each present generating unit is determined by the maximum power output and the droop[37]. As a consequence, assuming two or more machines have the same droop setting,the larger machine will deliver more power as is apparent in fig.11.

Isochronous control (shown in fig. 11), on the other hand, will deliver the set pointpower independent of the frequency. In turn, isochronous control may present problemswhen used on several units connected to the same system since they will compete to getsystem frequency to its own setting, requiring precisely the same measured frequencyfor stable operation [41]. In order to operate units in parallel governors are providedwith droop control. The droop characteristics are shown in fig. 11, as shown, thischaracteristic makes the speed droops as load is increased and vice versa. On smallerlocal grids (island grids) frequency needs to be sustained by the present machines, andthe power output is decided by the present loads.

Figure 11 – Characteristics of isochronous and droop control.

The regulator droop in percent is defined as

Droop ≡ δρ =∆f

∆P[%], (42)

here ∆f is interchangeable with ∆ω. In practice the droop value dictates the change inpower output (or frequency) at a given frequency (or load), this allows turbines workingin parallel to find a stable working frequency as they share a load. The droop settingon gas turbines working on island grids as sole power producers will, however, cause thefrequency to change depending on the present load. Since synchronous machines are

21

forced to work on the same frequency, machines of different sizes and/or with differentdroop values may share loads unevenly. It is clear that not all machines can work inisochrnous (frequency control) mode, due to the previously mentioned reasons, i.e. onemachine might end up producing all power, while the other starts slowing down. In orderto operate several machines on a smaller grid the speed setting of the controller can bechanged thus moving the power-frequency equilibrium [41]. The speed change settinginfluence is shown in fig. 12 representing an increase in power output and a change inspeed setting. In fig 12 it is shown how the frequency can be maintained at nominalvalue while using droop control.

Figure 12 – Speed change in droop control mode, line A represents the initial droop,line B represents the new speed setting resulting in the same frequency at higher poweroutput.

The load controller simply adjust the speed through feedback of the power set point. IfYt represents the output signal (in the time domain), or the deviation, then5

Yt = (fsp − f) + δρδf (Psp − P ), (43)

where subscript sp represents the set point value and δf is the droop factor representing

δf =f0Pmax

. (44)

From this it is clear that when the generator via the turbine delivers the required powerPsp the droop will not restrict the output signal, thus allowing the turbine to return tonominal frequency. Eq. 43 also shows the impact of droop when the power set point is

5This formulation does not express response time or any delays.

22

unchanged causing additional influence on the output signal. The signals entering theFLC are sensor values and restrictions calculated by other controller blocks. In the FLCthe fuel flow set point is determined by a PI-controller depending on the deviations off and/or P . The deviation itself, devi, and the hfsvi is fed to the selector unit whichselects the hfsvi with the lowest corresponding devi.

The start control, STC , is responsible for reliable starts and to avoid damage. TheSTC limits acceleration of the machine and hence limits the thermal stress. The inputs ofthe STC are monitored and kept within limits of the specific machine until the requiredspeed of the gas generator is achieved, once this criteria is achieved the NGGL takes overthe operation.

The gas generator speed limiter NGGL, as the name suggests, is responsible forlimiting the speed of the gas generator shaft. The STC unit sets the reference value ofngg and based on the deviation a PID controller outputs the fuel flow set point.

The temperature limiter T800L is fed values of the TIT and the exhaust temperature.The output signal (set point) is based on the deviation from the temperature set points.Inside of the temperature limiter are two PI controllers, for each of the temperatures,the deviations from both are compared and the smaller is passed out of the block to themain min value selector.

The pressure ratio controller, πC , takes in measurements of the pressure ratio π andthe normalized speed,

nggnorm = ngg

√Tref

(Tin,comp + Ck), (45)

where Tref is a reference temperature and Ck = 273.15 K. The normalized gas generatorspeed is transformed through interpolation to the corresponding pressure ratio value andcompared to measured pressure ratio value through a PI controller. In addition thenngnorm value affects the variable guide vanes in the VGVC by interpolation.

Furthermore the load loss detection block, LLD , measures rate of change of powerand signals the other controllers in order to bypass them and reduce the fuel flow to leanflame sustain level. The block measures the rate of change of fuel flow and at a criticalvalue activates the output signal. The signal will also manipulate the bleed valves andthe gas servos in order to avoid surge. The Selector unit , described previously, passesits output to the flame sustain control, FSC , which, in normal operation passes thissignal to the fuel valves. The FSC is also responsible for setting the lower limit on thefuel flow set point based on an interpolation of nngnorm. which block decides the fuelflow.

4.6.2 SGT-800 control system

The control system of the SGT-800 GT shares many similarities with that of the SGT-750, however, the differences warranties a separate section in order to differentiate. Thedifferences between the two arise, mainly from the fact that the SGT-800 consists ofa single shaft only. Some of the obvious consequences of the single shaft is that the

23

GT cannot deliver any torque at zero speed, neither can it change the speed of the gasgenerator, since it also is the power turbine and needs to be kept within a strict frequency.

First of is the T52 limiter , this unit serves to limit the TIT. The TIT is comparedto the measured value and the deviation and calculated required heat flow (from a PIDcontroller) is sent to the selector as in the SGT-750. The required heat flow is boundby a lower and upper limit calculated based on several of the conditions measured. Thecalculation of TIT reference is done through interpolation of a predetermined table. TheT52 limiter will serve to protect the turbine from over heating.

The tCON , temperature controller, block adjusts heat flow based on exhaust tem-perature and TIT. The block contains a minimum value selector in which the measuredvalue is compared to the set point for both. The tCON is responsible in adjusting VGVpositioning to affect the TIT. The set point value for exhaust temperature is based onseveral parameters. At idle and and part load the output will be based on the exhausttemperature deviation until the the value has reached the set point, when the tCON willadjust the turbine inlet temperature by adjusting the VGVs.

The BleedC controls the bleed valves to prevent choking of the later compressorstages. At lower rpm the bleed valves are open, and then closes as the rpm approachesnominal value.

The MLC , maximum load controller, limits the output power of the GT and works onthe same deviation to selector principle as many of the other controllers. The maximumallowed power output differs slightly between gas and liquid fuels.

Depending on the inlet temperature and pressure the normalized electrical power iscalculated in the Pel Norm. block.

The FLC , FSC and LLD works very similar to those of the SGT-750 with slightdifferences due to the difference in single and dual shaft design.

In the SGT-800 the upper and lower fuel flow limit is set by the tCON and the FSC.

24

5 Method

5.1 Procedure

The investigation of GT performance in island grid situations commenced with the designof a fitting model. The modeling was done in the modelling and simulation environmentDymola, where in house models for the SGT-750 and SGT-800 designed by Siemens wereavailable. Simulations of the system were compared to data from the factory test rig andon site data and the models were altered to reproduce the test results. The alterationswere simply adjustments of certain values recorded by the sensors at the time of themeasurement, apart from those corrections a heat flow limiter, discussed in detail inlater sections and a rate limiter of the power set point also discussed in coming sectionswere added.

5.2 Modelling

In order to simulate the turbine response an island system capable of connecting theauxiliary turbine models was designed. The system designed included generators, a loadtable and active load sharing logic. An overview schematic is shown in fig 13. The blocksshown in fig. 13 are mathematical operations of the main quantities entering and leaving.The island setup, includes blocks 1 through 4 in fig. 13, blocks 1-3 were constructed asgeneric components, while block 4 needs to be adjusted depending on the gearbox andgenerator present. The quantities that was adjusted in order to match measurement datawere: losses in the generator and gears, fuel system pressure (in accordance with thosemeasured in the test rig data), inertia of the generator and gears and ambient conditions.The adjusted values were all obtained from measurement data and data sheets, the gearlosses were accounted for as

Wgearloss = α+ βNγ + δWin, (46)

where α, β, γ, δ are constants for polynomial fitting, N is rpm and Win is the shaftpower. The model can be explained as a mechanical torque calculated based on a userdefined active load which is shared between two or more turbines equally. The torqueproduced, which is divided provides the electrical torque τe from the torque balanceequation discussed earlier.

25

Figure 13 – Simplified schematic of the turbine island setup and the main quantitiesentering and leaving each block, not including logical signals.

Since the focus of the simulations were turbine performance a simple generator modelwas implemented, disregarding voltage control. The control system of the turbines weremodeled to receive the load power value as the power set point in order to be able tooperate with droop, without a residual frequency drop. The generator model was set toa power factor of cosϕ = 0.9 and the losses was extrapolated from a map of values forthe generator. The inertia of the machine, the gear losses and the generator loss mapwas based on the generator used in each respective case.

5.3 Dymola model

The Dymola model used consisted of four main blocks as touched upon previously. Theactive load on the can easily be set via a time table or ramp function. The torque thatarises from the given load is calculated by the island torque block seen in fig. 14.

26

Figure 14 – The island torque block calculating the resulting torque from a given load.

The total torque given the load is then shared equally between the turbines connected viathe load share block depicted in fig. 15. Here we note that pink lines represent booleansignals (1 or 0). In the load sharing block switches, 1 through 3, are activated as soonas the turbines are ready to sync and then delivers three equal torque values. Withinthe load sharing block there are also three switches for tripping the turbines. The tripswitches are governed by timers and once one of the turbines trip the integer value ofthe denominator governing the fraction of torque to each turbine is increased evenly bythe amount of power the tripping unit was delivering.

27

Figure 15 – The load sharing block for three turbines containing trip switches, fractionsof torque to each turbine and a load set point output fet to the control systems of theturbines.

The fraction of torque to acting on each turbine is then routed to the generator blockshown in fig.16 in which the numerical values for torque are connected to a flange ad-apter which in turn is connected to each of the generators. The generator flanges arethen externally connected to the turbine shafts. The generators, as described earlier,are simple models taking into account losses regarding cosϕ, gears and mechanical, e.gfriction. The power delivered and the frequency of the generators are fed back to the GTcontrol systems as measurement values.

28

Figure 16 – The generator block for a triple GT island grid where the torque values fromthe load share block is converter through flanges to produce a mechanical torque on thegenerators.

All blocks described are contained within the island grid block seen in fig.17. Thisblock is connected to the GTs via shaft flanges and it feeds the turbine governors thepower output, the frequency and the power set point.

29

Figure 17 – Island Grid Dymola diagram, consisting all components but the GTs whichare connected from without.

5.4 Validation method

The constructed model was, in order to verify its reliability, compared to aforementioneddata. Ambient conditions, relative humidity, pressure ,temperature, and fuel pressurewas matched. Data for the present generator and gearbox was, from measurements anddata sheets, fed to the model as was the gas system pressure and volumes of the gassystem. The available measurement data was limited and scenarios had to be matchedclosely in order to be comparable. The available data contained heat flow (hf) in MJ/s,valve opening in % of max, velocity in RPM and controller setting amongst more. The

30

controller data settings from the test runs indicated that, in one of the validation cases,the SGT-750 load increase run, the maximum load controller limited the heat flow, thusaltering the transient response. This sudden controller discrepancy, was accounted forsimulated by adding a heat flow limiter block as seen in fig. 18.

Figure 18 – Dymola diagram of the heat flow limiter chocking the heat flow set point ina given time interval to duplicate the available data.

The heat flow limiter was tuned based on the heat flow in the test rig data. The slewrate limit (SRL) was calculated as

SRL =

∣∣∣∣∆hf∆t

∣∣∣∣, (47)

in the time interval ∆t where the hf increases by ∆y shown in fig.19Since grid frequency stability is of essence in island grid configurations the normalized

31

Figure 19 – Heatflow of the SGT-750 when load is increased from 11-18 MW.

rpm (npu) of the simulationsnpu =

n

n0, (48)

were bench marked to the test rig data. The Dymola models were also compared toMATLAB Simulink models, which contained more sophisticated generator models (butless detailed GT models) in order to evaluate potential differences. Measurement datafor heat flow, initial rotational speed and loads were closely matched in order to ensurecorrect response to input of the model.

5.5 Simulation case studies

Several cases were simulated to determine the SGT-750 and SGT-800s potential for islandoperation. The cases considered consisted of no additional producers, hence no additionalinertia. This in term is an extreme case where the GTs present are the only means offrequency and load control. The main Island grid simulations cases are presented in tab.3. In addition to those presented in tab. 3 simulations were carried out to determine themaximum instant load increase the GTs were capable of. The transient occurrence in eachsimulated case was set to take place once steady state operation was reached for a giveninitial load. All case study simulations were tested at three different ambient setting:hot, where Tambient=30oC, ISO, where Tambient=15oC and cold, where Tambient=-30oC.In all ambiant cases the relative humidity was set to 60 % and ambient pressure to 101.3kPa.

The implementation of the case studies, including the maximum load capability tests,consisted of numerous simulations and monitoring of engine parameters. Once the sim-ulations were completed the limitations and capabilities were determined.

Apart from the main cases, simulations of different fuel compositions were to be eval-uated through simulations using the same models. No modifications were made to thegas turbine or auxiliary systems aside from the addition of a fuel and chemical reactionfunction taking place in the combustion chamber. A second chemical reactions function

32

Table 3 – Main island grid cases simulated, the final power output was Pmax at ISO, ifnot otherwise stated.

Case Pinit [MW] DescpriptionA-750 0.5Pmax Two SGT-750 one tripsB-750 0.33Pmax Three SGT-750 one tripsA-800 0.5Pmax Two SGT-800 one tripsB-800 0.33Pmax Three SGT-800 one trips

was added for the reactants and the products of hydrogen fuels working on the samestochiometric matrix principle as the the regular NG-function, the function is simply cal-culating chemical balance of reactants and products. The combustion, in short, calculatesthe energy of the reactants and products to deliver power to the turbine. Simulationswere conducted with fuels consisting of 25, 50, 75 and 100% hydrogen by volume, the ex-act fuel compositions are presented in tab. AI.1 in appendix. The simulations hydrogenfuel regarded maximum instant load capability.

33

6 Results

6.1 Results of validation cases

The described procedures were conducted in order to validate the models, the cases arepresented in tab. 4.

Table 4 – List of validation cases carried out.

GT model ValidationModel ∆ P MW Comment FigureSGT-750 11-18 n0 = 5900, /w hf limiter 20

39-18 n0 = 6100 21SGT-800 15-24-38 n0 = 6610 22

24-15 n0 = 6610 23

Since the frequency of the turbine will reflect the overall dynamic response of theGT, and is of outmost important for stable grid operation, this quantity serves as thebenchmark for correlation. As is apparent in figs. 20-23, the simulations, while corres-ponding in time, slightly under predicts the magnitudes of the responses. In fig. 20 e.g.the maximum frequency drop of the simulation is about 0.975n0 while the test rig datashows a minimum frequency of about 0.965n0. The maximum frequency drops, however,occur after the same amount of time has passed since the load was increased, this goesfor all validation cases.

Figure 20 – Dynamic response to load increase 11-19 MW of the SGT-750 with heat flowlimiting using the test rig gearbox and generator.

The validation cases of the SGT-750 were compared to data from the test rig, in whichthe gearbox and generators are slightly heavier than the standard package. The casedepicted in fig. 20 utilizes the heat flow limited discussed in previous section to replicatethe conditions of the test. The results for the SGT-750 simulations shows an underprediction of magnitude < 1% of f0 at the point of greatest deviation.

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Figure 21 – Dynamic response to load decrease 39-18 MW of the SGT-750, highlightingthe point of shutdown of the test rig GT, explaining the large deviation after the fact.

The validations of the SGT-800 (figs.22 and 23) were carried out with data where stand-ard gears and generators were used. Once more there were slight under predictions bythe Dymola model. Here the greatest discrepancy between the model and simulationswere of the magnitude < 1 % of f0.

Figure 22 – Dynamic response to load increase 15-24-38 MW of the SGT-800.

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Figure 23 – Dynamic response to load decrease 24-15 MW of the SGT-800.

Validations carried out comparing the Dymola model to the MATLAB Simulinkmodel (see appendix II fig. AI.1) showed that the Simulink model under predictedinstant load increase for the SGT-750. However, it marginally over predicted frequencydrop from a load increase of the SGT-800.

6.2 Case studies

6.2.1 Instant load increases under different ambient conditions.

In order to evaluate the maximum cable load increase on the GTs, simulation were rununder three different ambient settings, all at an initial power output of 10 MW, theresults are displayed in fig. 24.

For the SGT-750 the maximum capability is at -30 oC with a capability of a 30MW instant load increase resulting in a minimum frequency of 0.87f0, at higher ambientsetting the capability decreases somewhat, resulting in a minimum frequency of 0.89 f0for a 25 MW increase at -15 oC and a minimum frequency of 0.91 f0 for a 20 MW increaseat 30 oC.

The identical, ambient condition procedure, was carried out for the SGT-800. Theresults shown in fig. 24 shows very slight differences between the different ambientconditions, although we see that the capability is highest at -30 oC where the SGT-800 iscapable of a 50 MW increase resulting in a minimum frequency of 0.96 f0. The other twocases results in a minimum frequency of 0.95 f0 at the maximum instant load increaseof 40 MW.

For capability comparisons the maximum frequency drop at a given fraction of ratedpower of both GTs is shown in fig. 25. Within the displayed intervals ambient temper-ature has little impact on the maximum frequency drop, note however that the SGT-750data at ISO and 30oC conditions only contain two and three data points respectively.

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Figure 24 – Maximum instantaneous load increases for both GTs at different ambientsettings.

Figure 25 – Maximum frequency drop at instantaneous load increases at different ambientconditions with Pi=10 MW corresponding to 0.25Prated for the SGT-750 and 0.17Prated

for the SGT-800.

From fig. 25 a linear fit can be formed as

∆nmax = κ∆P (pu) +m, (49)

where κ = ∆n/∆P (pu), empirically, can describe the sensitivity to load increases so that∆n(pu)max = fκ(P (pu)). Based on the linear fit the SGT-750 shows κ750=0.19, m750 =-0.0084 (r2 > 0.99) and the SGT-800 κ800=0.052, m800=-0.0017 (r2 > 0.99).

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Figure 26 – Frequency response of all the grid-trip cases, here c* denotes the maximumpower for SGT-750 at -30oC, i.e. not at ISO conditions.

6.2.2 Island grid cases

Island grids of two and three turbines as specified in 3 were simulated, the results in theform of frequency responses are presented in fig. 26. For the SGT-750 we see that atISO condition the minimum frequency is 0.919f0 and at -30oC the minimum frequency0.91f0. In fig.26 the line denoted with c* represents operation at higher output, sincethe power rating is inversely proportional to the ambient temperature. This phenomenagoes for all GTs but the special notation was only used since the SGT-750 at -30oCwas also ran at power rating at ISO conditions power rating, i.e slightly lower. Thefκ,750-linear fit for the SGT-750 -30oC case predicts fmin = 0.91. At 30 oC we see thatthe SGT-750 is able to recover a load increase of 0.5Prated in the process dropping to0.85f0. In addition the recovery requires a longer time span (20 s.), relative to the otherconditions. The B-750 case is managed at all ambient conditions with fmin ≈ 0.97f0, thefκ,750 predicts fmin ≈ 0.98f0. The island grid cases for the SGT-800 (26 A-800 and B-800), shows that it is capable of prevailing through both cases at all the ambient settingconsidered. The A-800 shows that the a load increase of 0.5Prated results in a similarresponse at all ambient temperatures where the frequency drops to 0.975f0, predicted byfκ,800 to be fmin ≈ 0.97f0. the B-800 case shows fmin ≈ 0.992f0 predicted by fκ,800 tobe fmin ≈ 0.99f0.

6.2.3 Hydrogen fuel cases

Simulations regarding maximum load increase capability were repeated using differentfuel mixes containing hydrogen. A total number of 48 simulations were carried out foreach GT, the complete set of results are displayed in figs.27-28. The cases using 25, 50

38

and 100% H2 by volume showed similar results, in terms of frequency response, as theNG cases. However, the 75% by volume (BV) case was only able to handle a 10 MWincrease in the SGT-750 simulations

Figure 27 – SGT-750 hydrogen mix fuels at different ambient conditions at instant loadincreases.

Figure 28 – SGT-800 hydrogen mix fuels at different ambient conditions at instant loadincreases.

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6.3 Remarks on the results

We start the analysis of the results by looking at the maximum load cases, where theSGT-750 showed a maximum instant load increase capability of 20 MW (>0.5Prated) inall conditions considered, and higher capabilities in lower ambient temperatures, see fig.25. The SGT-800 was capable of recovering from a 40 MW (>0.7Prated) instant loadincrease in all ambient settings as seen in fig. 25. In the simulations, as in practical islandoperations settings, violations of the maximum temperatures were allowed over shortperiods of time. The most extreme cases are shown in fig29 (the A-750 and A-800 at30oC), where we see short periods in which temperatures over the maximum operationalvalue (in the figure 1 pu) occur. In fig. 29 it is apparent that the gas generator speed iskept below or at max at all time, while the TIT slightly overshoots the operation limitfor the SGT-750. In the SGT-800 the TIT and TOT overshoots for a time period of littleless than 10 seconds before settling the allowed operational temperature as the frequencyrecovers.

40

Figure 29 – GT quantities at island case where one unit trips at half power.

6.3.1 Limiting factors for the SGT-750

In the the ISO case of the SGT-750 (fig. 26), when instantaneously loading 25 MW(or >0.65%Prated) the GG shaft reaches its normalized speed limit while at the sametime the TIT (which can be overlooked in these timescales) reaches its maximum allowedvalue this in turn causes the longer and deeper frequency dip. At -30oC when adding30 MW of load the speed limiter is engaged for a brief period of time, preventing overspeeding and then recovering without remarks. In the +30oC case however the ngglsystem limits power output for a longer period of time which is apparent from the longand deep frequency dip in fig. 24.

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6.3.2 Limiting factors for the SGT-800 maximum instant load increase

The results of the SGT-800 simulations show lesser frequency dips but similar recoverytimes, the recovery from the local minimum is slightly more level. The ambient temper-ature does not impact the magnitude nor the recovery time substantially, this is in partdue to the tolerance imposed on short bursts of over temperatures. Slight oscillationsoccur at -30oC, possibly due to the slight oscillations of the VGVs shown in fig.30.

Figure 30 – Per unit VGV opening at different ambient conditions for the SGT-800 inscenario A-800.

6.3.3 Hydrogen simulation results

All but the 75%-H2 mix showed similar results as the NG simulations. The short answerto why this is can probably be found in the similarities of Iw allowing the same amountof energy flow through the constant dimension fuel system, valves and CC. This is em-phasized by the fact that the 75%-H2 is not capable of larger load increases. The cause isthe high wt% of inert gas (N2) through an undersized and under pressurized fuel system.The temperatures were slightly lower when the amount of inert gases were high. Theresults of the hydrogen simulations can be summarized as follows: the 25, 50 and 100%hydrogen fuels presented similar results in regards to frequency response, especially forthe SGT-800. We can see from fig. 31 that the main fuel valve opening is capable ofcounteracting the lower energy density, by allowing a greater volume flow of fuel gas andhigher rate of valve opening. The simulations suggest that the fuel flow is the limitingfactor for the SGT-800.

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Figure 31 – Fuel valve opening comparison for NG and 75% H2 (by volume) for theSGT-800 at instant load increase (10-40MW).

This was further investigated by increasing the fuel pressure by 15 %. The resultsin fig. 32 showed that the pressure increase to some extent could mitigate the problemswith a low energy density fuel.

Figure 32 – Fuel valve opening and frequency response (10.50 MW load increase) fornominal and 15 % increased fuel pressure.

The SGT-750 had a more violent frequency response for the 100% H2 fuel at 30oCambient temperature at 10-30 and 10-35 MW increases, in these cases the speed limiteris in operation which decreases the output causing a larger frequency dip and a longerrecovery time. No significant changes in temperatures were noted when using hydrogenfuels.

6.4 On the differences of the machines

From the simulations we have seen that the SGT-750, in terms of magnitude of frequencydrop, is more sensitive to load increases, even when the rated power is taken into consid-eration. From sec. 4.4, we have seen that the change in frequency is dependent on theinertia of the machine. A simple simulation,using the block diagram seen in fig.33, of theswing equation (eq. 40) can give indication of the influence of inertia on the frequency

43

drop. In the swing equation simulations the GT power output is simplified as a firstorder transfer function on the form G(s) = 1

1+Ttswhere Tt is a time constant.

Figure 33 – Simulink block diagram for simulating the swing equation.

In the simple simulations we can vary the inertial constant H (1<H<10) and the timeconstant Tt (1<Tt<5) which yields the results in fig. 34.

Figure 34 – Swing equation simulation results when varying inertial constant H (upper)and time constant of the turbine Tt (lower).

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7 Discussion

The goal of this thesis was to develop a model for island grid scenario simulations, validatethe simulations with the help of measurement data and further investigate the capabilitiesof the Siemens SGT-750 and SGT-800 in such settings. The models implemented andsimulations carried has proven to be an easy, rather quick, cheap and effective way topredict GT performance of the specified cases. The results adds to the body of, andattests to the capability of simulations as previous publications have shown [19, 20, 22,26].

7.1 The island grid

An island grid system was modelled and four basic cases were validated to real life testdata showing similar results in frequency response (figs. 20-23). Since the main purposewas to evaluate the dynamics of the gas turbines, the grid designed (figs.14-17) is notfitting for a deeper analysis of the power system dynamics, including active power andvoltage control since these factors have been neglected. An additional weakness of themodel limiting its applicability, is the fact that no additional rotational machinery, e.gsynchronous motors, are present. This may have caused greater frequency dips than whatwould have occurred if additional rotational machines were present contributing inertia.For e.g in [25] the power system tested had an inertial constant (H) of 6 s, which couldbe argued as reasonable, however in this thesis it could detract from the goal of solelyevaluating the GTs performance. The results from the simulations carried out in thisthesis will be representative of smaller grids consisting solely of GTs supplying power, suchas off shore applications and industry power supply. For local power grids, specifics ofadditional suppliers, predicted demand, start time and more need be considered, however,the model can provide useful predictions of the limits of the GTs for such applications.The complete model for the simulation, however, included highly specific GT modelswhich were able to predict real life behaviour of a myriad of parameters. The frequencyresponse as an indicator was studied, since it will be of utmost importance for endusers and will be a result of the total dynamics of the GT. The fact that the Dymolaand Simulink results presented similar results despite the simple generator models mayfurther serve to justify the reliability of the model. Specifically the similarities betweenthe Dymola and Simulink results are shown in fig. AI.1 where it is seen that the differencesin frequency response are of the magnitude 0.01n0.

7.2 Maximum capability simulations

Some of the main consequences and causes of the results were mentioned in the resultsection (sec. 6.3), a discussion on the results will follow.

We start by noting that this work has focused on two specific GT models, it hassuccessfully evaluated their potential in an island grid setting. As a consequence ofusing highly specific models not all results are generalizable. It is, however, clear that:The inertia of the GT has a substantial impact on maximum frequency drop and that

45

temperatures, in particular TIT, are limiting factors when increasing loads. It is notsurprising that this is the case, since GTs are designed for high temperatures in orderto maximize efficiency and power output. Were the turbines to accept much highertemperatures, their rated power outputs would be higher. In an attempt to quantify thefrequency sensitivity to load increases we introduced the machine specific κ-parameterwhich was empirically calculated. The κ-parameter can be seen, merely, as an uncertainpredictor of frequency drop of a GT for a given fraction of maximal power. As soon asevents occur that evokes sudden responses in the control system or the turbine leavesstable operation this a linear κ function will not be a reliable predictor. For that reasonwe can speculate that a κ presenting a high linearity (e.g r2 ≈ 1) is indicative of operationwithin a region stable for the machine. In particular κ was linear within regions whereonly the FLC was governing the engine. This and and the following discussion points tothe fact that κ ∝ H.

Moreover, the bare bones model shown in fig.33 was presented in order to get a generalunderstanding of the impact of rotor inertia and a turbine time constant Tt on frequencyresponse. The swing equation simulations (fig.34), assumes the behaviour of the GT canbe represented by a first order transfer function of one time constant Tt, although this is asimplification that was not derived, the results gives indications of the impacts of inertiaand GT dynamic response time. We see that an increase in inertia only decreases themagnitude of the frequency drop at constant Tt with diminishing returns. The value ofTt however has an impact on the timescale of recovery and oscillations. These propertiesties into the simulation results in that the time of recovery is rather similar for bothmachines indicating advantageous control response and machine dynamics. In the caseswhere the governor limits the energy flow, however, the recovery time is effected (eg. fig.24).

The results overall, implies that the SGT-750 and SGT-800 are well suited for islandoperation and provides resilience to large power fluctuations. Their resilience is shownby their instant load increase capabilities which are over 50% of their rated power re-spectively, and as seen in fig.25 they both recover from these load increases in around10 s. The SGT-800 allows for smooth operation over a wide range conditions. Althoughthe SGT-750 does not provide the same load increase capability and frequency stability,in absolute terms, as the SGT-800 the relative capabilities are rather similar. The res-ults would indicate that both the SGT-750 and SGT-800, on a reasonably dimensionedpower grid, would persevere through heavy transients. This is evident from thre resultsin fig.26. We note that the line marked T?-30o c*, is an out liar. This simulation tookinto consideration the increased capability at lower ambient temperatures, for the SGT-750 approximately 15 % and the GT was not able to recover. We can argue, however,that this event only would occur if the system was under dimensioned. The logic goesthat a power system of two GTs, designed to handle a trip out of half the power pro-duction without a total blackout, would not calculate the rated power output based onarctic conditions. This is due to the fact that such a power system only would be ableto deliver full rated power at temperatures below -30oC, which would seem unlikely.

The intrinsic differences between single and double shaft GTs, such as the torque/speed

46

relationship [37] and efficiency at lower loads and, again, inertia must also be taken intoconsideration when considering the application [21]. Furthermore we see that the limitingfactor of load increase will ultimately be the temperature limit, this fact has been pointedout in the literature ([22, 23, 25, 29]), and implies the model acts realistically. One ofthe main limitations of GTs are the turbine materials resistance to extreme temperatures[40, 45], thus GTs are designed to run as hot as possible without causing unnecessarystress. High TIT increases the efficiency ([17, 40, 45]) and therefore it is desirable that theoperational temperature is kept high, for this reason it is easy to see why the temperaturecontroller will be the limiting factor in violent transient events.

7.3 Hydrogen simulation results

The hydrogen simulation cases (results in figs. 27, 28) showed overall acceptable opera-tion. The lower fraction hydrogen mixes behaved similarly to the NG operation, whichcan be traced back to the energy density and Wobbe index. In the simulations the CCwas simply fed the required energy rate resulting in greater opening of the fuel valve andthus a higher volume rate. This fact was emphasized when increasing the fuel pressure(fig. 32), the higher pressure allowed for a quicker response, in turn delivering a higherenergy rate. The higher degree of opening of the fuel valves may also make controllingthe flow accurately difficult due to the characteristics of valves. Operation on low Wobbeindex gasses may as such require special valves and tuning of the control system.

The rather simple combustion chamber of the model does not take into considerationintricate physics of combustion e.g heat transfer, laminar flame speed etc. and theanalysis of such mechanisms are beyond the scope of this thesis.

Lower concentration hydrogen fuel tests have been successfully conducted at Siemensand are being successfully investigated, higher amounts H2 fuels still require compre-hensive research and tests however. The results produced summarily points out therequirements for higher pressure systems and/or larger volume components for operationon high inert gas content fuels. These procedures may be costly, since larger volumesmay require redesign of some components,high pressure tanks and compressors. Furtherinvestigations regarding the formation of NOx should also be considered in order to geta complete outlook on the matter [36].

Aside from the possible challenges in GT design, the problems of production, storageand transportation remains [32, 33]. For hydrogen to become a realistic option capableof replacing fossil fuels, these problems need to be solved.

7.4 Reflections and areas of improvement

Allowing temperatures that exceeds the maximum operational is common practice incertain cases during transients. This, however, may cause uncertainty on the implicationsof the results concerning the higher load increases (in particular the high temperaturegrid trip cases A-800 in fig. 26). The control systems may be tuned in order to allowsuch events, however, in this thesis no additional tuning of the control systems wereperformed. In fig. 30, we see some leeway after the initial peak and although the VGV

47

position is a function of several variables, this may give us a clue that it is not perfectlytuned, since a more aggressive opening of the VGVs might have lowered the temperature,which is desirable for longevity and stable operation. Although these events only tookplace for short periods of time (<10 s.), a majority of which was in single digit percentabove the operational limit. They should be accounted for and quantified in the controlsystem model more meticulously in order to give a definite limit in terms of time andtemperature. This could help in approximating the additional wear on the components.Although the validation scenarios of the model showed good correlation (sec. 6.1), both toactual site data and previous Simulink models, a larger set of data would be preferred toinvestigate how more extreme cases correlate to the simulations. While a GT is a complexand highly dynamic system, the dynamics are well understood and in detail described atlength in the literature (eg. [40, 45]) and mathematical simulation models have proventime again successful at predicting transient behaviour (e.g [20, 21, 23, 26, 29]). Fromsec. 6.4, we saw that the the time constant Tt had substantial impact of the time frame offrequency fluctuations. The fact that the comparisons in figs. 20-23, corresponds closelyin time to the test rig data (i.e peaks and valleys in frequency occur at similar times)indicates that the models are successful in capturing the fundamental dynamics of theGTs they represent. The high specificity of the SGT-750 and SGT-800 Dymola modelsand the results presented in conjunction with the theoretical and practical knowledgeon GTs in general speaks to the validity of the results. Taking all discussed points intoconsideration, the results are representative, although a deeper analysis of the controlssuch as the response time of the TIT controller would be preferred.

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8 Conclusions

The work presented in this thesis set out to construct an island grid model to evaluategas turbine performance during transients. Investigations of the effects of hydrogen fuelimplementation in transient events were also to be covered. The model was built, suc-cessfully tested and validated against on site recorded data and other GT models. Severalspecified simulation cases were carried out and the results can be summarized by the fol-lowing conclusions: (i) The Siemens SGT-750 and SGT-800 gas turbines are, accordingto the finding, capable of handling sudden power increases corresponding to >50 % of therated power.(ii) The factors eventually limiting the SGT-750 from responding quicker toload increases are the speed of the gas generator and TIT, for the SGT-800 the TIT andTOT will eventually cap out the rate of power increase. This can somewhat be mitigatedby allowing short periods of temperature above maximum operational at transients. (iii)Hydrogen fuels consisting of 25%, 50% and 100% H2 showed no significant differencesin transient events, however, high contents of inert gas would require higher pressuresand/or enlarged gas system and valves.

The results should be viewed as indications of the capabilities of the SGT-750 andSGT-800 when subjected to large transients but also a reference to areas of improvement.

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9 Future work

The vast and complex topic that is gas turbine performance has, in this thesis, been con-densed and applied to produce concrete results and predictions. This work, nevertheless,opens the door to new, interesting and important questions. We have provided a generaloutlook on the performance of the SGT-750 and SGT-800, future work should immersein the specifics left out of this work and and continue developments of expansive projectsimilar to this one. Material, heat transfer, combustion and CFD research will likelycontinue pushing the physical limits of GTs which is crucial to improve performance.Aside from the vast research going on on these subjects future work on GT performanceare suggested to include: A Dymola implementation and validation of a leading governorunit. This unit should be capable of alerting several units if instability or trips occur inorder to prime the other units and possibly avoid violent loading. A validation of hydro-gen combustion would be suggested in order to be able to quickly test hydrogen transientoperations in Dymola, this would possibly include a review of the combustion system ofthe model. The relative simplicity of the model could also allow an optimization studyof transient handling, this could include VGV operation water or steam injection andcontrol system parametric studies.

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Appendix I.

Table AI.1 – Fuel compositions and Wobbe index for the fuel gasses considered.

Fuel NG H2 (25) H2(50) H2(75) H2(100)CH4 wt% 92 84 78 - -C2H6 wt% 4.8 7 6 - -C3H8 wt% 0.5 5 5 - -n-C4H10 wt% 0.2 - - - -N2 wt% 1.5 - - 82 -CO2 wt% 1 - - - -H2 wt% - 3 11 18 100Iw(MJ/Nm3) 53 51.2 47.7 17.6 48.3

Figure AI.1 – Comparisons of Simulink and Dymola simulations of instant load increases(one turbine trips), SGT-800 20-40 MW (upper, SGT-750 15-30 MW (lower), both at ISOconditions.