influence of the heat recovery steam generator design parameters on the thermoeconomic performances...

INTERNATIONAL JOURNAL OF ENERGY RESEARCHInt. J. Energy Res. 2004; 28:1243–1254Published online 20 August 2004 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/er.1026

Influence of the heat recovery steam generator designparameters on the thermoeconomic performances of

combined cycle gas turbine power plants

Manuel Vald!eesn,y, Antonio Roviraz and Ma Dolores Dur!aan}

Departamento de Ingenier!ııa Energ !eetica y Fluidomec !aanica, Universidad Polit !eecnica de Madrid, E. T. S. Ingenieros

Industriales, Jos !ee Guti !eerrez Abascal, 2, 28006, Madrid, Spain

SUMMARY

This paper proposes a methodology to identify the most relevant design parameters that impact on thethermal efficiency and the economic results of combined cycle gas turbine power plants.The analysis focuses on the heat recovery steam generator (HRSG) design and more specifically on those

operating parameters that have a direct influence on the economic results of the power plant. These resultsare obtained both at full and part load conditions using a dedicated code capable of simulating a widenumber of different plant configurations.Two different thermoeconomic models aimed to select the best design point are proposed and compared: the

first one analyzes the generating cost of the energy while the second one analyzes the annual cash flow of theplant. Their objective is to determine whether an increase in the investment in order to improve the thermalefficiency is worth from an economic point of view. Both models and the different HRSG configurations analysedare compared in the results section. Some parametric analysis show how the design parameters might be varied inorder to improve the power plant efficiency or the economic results. Copyright# 2004 John Wiley & Sons, Ltd.

KEY WORDS: thermoeconomics; combined cycle; heat recovery steam generator; thermal optimization

1. INTRODUCTION

The growing energy demand and the need of shrinking costs have led to the design of highefficiency and quick installation power plants. The success of combined cycle gas turbine (CCGT)power plants lies on their high efficiency, low cost and short construction lead time. It is knownthat the design of CCGT power plants is very flexible and that there is a great number of differentheat recovery steam generator (HRSG) thermodynamic configurations. Their optimizationstrongly depends on the selected gas turbine (GT) and should be done using thermoeconomicmodels and simulation tools specifically designed for the study of this kind of plants.

Received 22 October 2003Accepted 19 January 2004Copyright # 2004 John Wiley & Sons, Ltd.

yE-mail: [email protected]

nCorrespondence to: M. Vald!ees, Departamento de Ingenier!ııa Energ!eetica y Fluidomec!aanica, Universidad Polit!eecnica deMadrid, E. T. S. Ingenieros Industriales, Jos!ee Guti!eerrez Abascal, 2, 28006, Madrid, Spain.

zE-mail: [email protected]}E-mail: [email protected]

Contract/grant sponsor: ENDESA

The aim of this paper is to elaborate a thermoeconomic model to improve the design and toeconomically assess CCGT power plants. This model is approached from two points of viewthat are discussed and compared: the generating cost and the annual cash flow of the powerplant. For that purpose, the first step consisted in the development of a mathematical model toperform CCGT simulations over the entire load range. In order to control all the parametersand the solution process a dedicated code was developed. The code accounts for special details,like controlling compressor inlet airflow using variable inlet guide vane (VIGV) modulation orsolving heat exchangers with more than one stream on the liquid side (parallel flow sections).

The model may be used to analyse the influence of the design parameters as Kehlhofer et al.(1999) and Horlock (1992) do for the whole cycle, Vald!ees and Rap !uun (2000), Rap !uun (1999) andFranco and Russo (2002) do for the HRSG or Finckh and Pfost (1992) do for the HRSG andthe gas turbine.

2. SIMULATION PROGRAM

2.1. CCGT power plants simulated

The CCGT configurations simulated in this work are the following:

* Single pressure level (1P).* Dual pressure level (2P).* Dual pressure level with reheating (2PR).

Their respective energy-temperature diagrams are shown in Figure 1, where some designvariables of the HRSG}pinch points (PP), approach points (AP) and the gas to steamtemperature difference (DT) at the superheaters}are defined.

2.2. Full load simulation

The full load simulation is achieved by means of mass and energy balances applied to everycomponent of the CCGT. The results are the thermodynamic properties of the steam and thegas at every point of the cycle, the CCGT power and efficiency and the heat exchanged at eachelement of the HRSG.

Table I shows the design parameters needed to run the full load simulation.

2.3. Part load simulation

CCGT part load simulation requires the prediction of the performance of the cycle elements atevery operating condition. Once the full load simulation has been done the following data mustbe introduced or calculated: the characteristic curves of every turbomachine, the UA product(global coefficient of heat transfer by the heat transfer area) and the ambient conditions(pressure and temperature).

Mu *nnoz et al. (2002) have studied thoroughly the design and behaviour of turbomachines; El-Gammal (1991) and Stamatis et al. (1990) supply dimensionless curves that can be used toextrapolate the performance of gas turbines in whatever condition. The equations that describethe HRSG performance can be found, for example, in Kehlhofer et al. (1999) or in Rap !uun(1999). The variation of the heat transfer coefficient U as the gas turbine load is modified is

Copyright # 2004 John Wiley & Sons, Ltd. Int. J. Energy Res. 2004; 28:1243–1254

M. VALDES, A. ROVIRA AND M. D. DURAN1244

described by Rap !uun (1999) and by Vald!ees and Rap !uun (2000). Finally, Kostyuk and Frolov(1998) and Cotton (1998) give some correlations for the steam turbine performance prediction.

The program gradually reduces the gas turbine load to obtain the new steam and gas points,power, efficiency and heat transfer of the cycle elements. Part load simulation combines VIGVmodulation and fuel–air ratio control. In the first case, the airflow rate is varied during part-loadoperation, which avoids the reduction of turbine inlet or exhaust temperature. In the secondcase, turbine inlet and exhaust temperatures gradually decrease with a reduction of fuel–airratio. Section 4.2 shows part load results.

∆THP

PP

∆T

AP

Q

PPHP

APHP

∆TLP

PPLP

APLP

Q

T

T

(a)

(b)

SH HP

SH EV EC

∆THP

Q

∆TLP

PPHP

APHPPPLP

APLP

T

(c)

SH HP RH EV HP

HP ST

SH LP EC2 HP EV LPEC LP

EC1 HP

EV HP SH LP EC2 HP EV LPEC LP

EC1 HP

Figure 1. Energy temperature diagram and schematic of the configurations of the power plants considered.(a) 1P; (b) 2P; (c) 2PR.


THE HEAT RECOVERY STEAM GENERATOR DESIGN PARAMETERS 1245

3. THERMOECONOMIC MODEL

Improving the CCGT efficiency usually increases the cost of the plant. The followingthermoeconomic analysis intends to achieve a trade off between high efficiency and acceptablecost. Several works regarding this field of research can be found in the literature: Dechamps(1995), Frangopoulos (1998), Frangopoulos and Vasilias (1992), Tsatsaronis (1993) andAgazzani and Massardo (1997).

3.1. Model description

Cash flow model: The annual cash flow B, the incomes I and the fuel cost Cfuel are:

B ¼ I � Cfuel � Camort � Co2m ð1Þ

I ¼ S %WWh ð2Þ

Cfuel ¼ F%WW

Zh ð3Þ

This paper is focused on the HRSG design, which is determined by the thermodynamicdesign parameters shown in Figure 1. Once a particular set of design parameters is chosen,the UA product of each HRSG section can be calculated. This is the reason why it isadvisable to divide the amortization cost in two components: the first one CF is characteristicof the CCGT elements (gas turbine, economizers, evaporators, etc.) while the secondone is associated with HRSG area variations, assumed to be proportional to the UAproduct.

Camort ¼ CF þX

ðkecUAecÞ þX

ðkevUAevÞ þX

ðkshUAshÞ� � ið1þ iÞn

ð1þ iÞn � 1

� �ð4Þ

Finally, the operating and maintenance cost Co�m is estimated as 10% of the total plant cost,as pointed out by Naughten (2003).

Table I. Design parameters for the full load simulation.

Ambient conditions Temperature and pressureCompressor (gas turbine) Compression ratio, isentropic efficiency, pressure loss and air

mass flowCombustion chamber (gas turbine) Combustion efficiency, pressure loss, exhaust temperature or air–

fuel ratioTurbine (gas turbine) Isentropic efficiency, exhaust pressure and pressure lossSuperheaters and reheaters Gas to steam terminal difference of temperatures, pressure loss

and percentage of heat exchanged (if there are two or more withinthe same pressure level)

Evaporators Pressure, pinch point, approach point and pressure lossEconomizers Pressure loss and percentage of heat exchanged (if there are two

or more within the same pressure level)Tempering Limiting temperature and pressure lossSteam turbine Exhaust pressure (extraction or condensation)Deareator Saturation pressureWater extraction for gas turbinerefrigeration

Heat exchanged and water flow



Generating cost model: The generating cost is calculated dividing the sum of the costs by theannual energy produced:

CkWh ¼Cfuel þ Camort þ Co2m

%WWhð5Þ

3.2. Comparison of the cash flow and the generating cost models

The thermoeconomic analysis is performed from two different points of view. If an optimizationwas done to find the best design point conditions the results would be different depending on themodel considered (although both models would be able to find a high efficiency for the cycle at areasonable cost).

The difference between the models lies on the objective of the optimization. If there is a strongcompetition inside the electrical market and therefore it is difficult to sell the generated energy,the generating cost should be minimized. This can be done balancing adequately theamortization cost and the power achieved. Otherwise, in a regulated market with fixed energyprices, where it is easy to sell the energy, the optimization should lead to a more profitablesolution, maximizing the cash flow. In this case, an improvement in the efficiency with itsresulting gain of power can increase the incomes so that they overcome the amortization cost.

The equations below show mathematically the difference between both optimizationstrategies.

As it can be seen in the following section, the more efficient the power plant is, the bigger thetotal HRSG heat transfer area is. The optima for each model satisfy:

dB

dA¼ 0 ð6Þ

dCkWh

dA¼ 0 ð7Þ

From Equations (1), (2) and (5), the cash flow is:

B ¼ ðS � CkWhÞ %WWh ð8Þ

The derivative with respect to the area of this expression is:

dB

dA¼ �

dCkWh

dA%WWhþ ðS � CkWhÞ

d %WW

dAh ð9Þ

This equation, evaluated in the optimum of the generating cost (Equation (7)) becomes:

dB

dA

� �CkWh;min

¼ ðS � CkWhÞd %WW

dAh ð10Þ

which is positive provided that S>CkWh and d %WW=dA > 0:Then the cash flow and the generating cost curves should be like those represented in Figure 2,

where the maximum cash flow area is higher than the minimum generating cost area (A2>A1).This means that when maximizing the cash flow, the increase of the cost in order to achievehigher efficiency is more than balanced with the increase of power.



4. RESULTS

4.1. Full load results

The CCGT described in Section 2.1 have been simulated to calculate all the thermodynamicvariables and to obtain their gross power, efficiency, generating cost, cash flow and UAproducts. These results are obtained for a particular set of design parameter values called the‘design point’.

Table II shows the CCGT design parameters and the economic data used to perform thethermoeconomic analysis.

Table III shows the results for the selected cycles at the design point. The single pressureCCGT power plant results (generating cost and cash flow) are used as the 100% reference pointagainst which the results of the other cycle configurations are compared.

It is interesting to predict how the design parameters may be varied in order to improve theefficiency without decreasing the economic results or, on the contrary, how to improve theeconomic results with a small change in the efficiency. Figures 3–5 show some of the results forthe variation of the design parameters.

Figure 3 shows the efficiency and the generating cost variations vs the design parametervariations in the 1P cycle case. The PP is the most influencing variable on the efficiency while theterminal difference of temperatures (DT) has a marginal influence. Starting from the originaldesign point, a decrease in the four selected design parameters leads to an increase in the efficiency.This in turn entails lower generating costs except for the DT case, where the slight improvement inthe efficiency does not compensate the increase of the amortization cost necessary to decrease DT.

Figure 4(a) shows that an improvement in the efficiency implies an increase in the total heattransfer area and thus in the investment. This is not always rewarded by lower generating costs,as it can be seen in Figure 4(b), again for the DT case.

Figure 5 shows the efficiency and the cash flow variations vs the variations of the highpressure (HP) design parameters in the 2P cycle case. A maximum is found in the efficiencycurve of Figure 5(a) when the high drum pressure is modified. This means that, from athermodynamic point of view, it would be advisable to increase the HP level. However, this

A≡η A1 A2

CkWh

B

Figure 2. Maximum cash flow and minimum generating cost.



result is not properly confirmed in the economic analysis: the maximum of the drum pressurecurve in Figure 5(b) is not so far from the initial design point (0%). Therefore, the HP levelincrease should be lower than that suggested by the thermodynamic results.

There are several maxima in the cash flow curves of Figure 5(b) that are relatively near one ofeach other. If a cash flow optimization was done, all these maxima would be found in the same0% point. This kind of parametric studies show thus how far the design point is from theoptimum. There are several ways to undertake an optimization. Liszka et al. (2003) present aparametric study of the HRSG. Vald!ees et al. (2003) use genetic algorithms in order to find anoptimum by varying all the design parameters at the same time. Franco and Russo (2002) usethe Simplex method with the same purpose.

4.2. Part load results

Figure 6 shows the GT and CCGT power and efficiency when the gas turbine load of the 1Pcycle is reduced.

Table II. Parameters at the design point.

Parameter 1P 2P 2PR

r 20ZC 0.85DPC (mbar) 20Tamb (K) 288.15Pamb (bar) 1.013ZCC 0.95DPCC (%) 4Tinlet (K) 1430ZT 0.91DPT (mbar) 40ma (kg s

�1) 300PHP (bar) 50 70 70PPHP (K) 10 10 10APHP (K) 10 10 10DTHP (K) 30 30 30PLP (bar) } 10 10PPLP (bar) } 10 10APLP (K) } 10 10DTLP (K) } 20 25Pdea (bar) 0.2Pcond (mbar) 50S (c/kWh) 3.90F (c/kWh) 1.30h (hour) 7200n (year) 15i (%) 10CF (M) 16.8Cec UA ( (kWK�1)�1) 10 500Cev UA ( (kWK�1)�1) 8000Csh UA ( (kWK�1)�1) 19 250Cinst ( kW

�1) 23 000



Table III. Results of the simulation.

Variable 1P 2P 2PR

PGT (MW) 105.3ZGT 0.3818mf (kg s

�1) 5.69TexhGT (K) 776.3WST (MW) 37.6 39.3 42.5WCCGT (MW) 142.9 144.6 147.8ZCCGT 0.518 0.524 0.536TexhHRSG (K) 459 416 421UAshHP (kWK�1) 240 247 196UAevHP (kWK�1) 981 945 882UAec2HP (kWK�1) } 478 380UAec1HP (kWK�1) 509 195 156UAsh2LP (kWK�1) } } 176UAsh1LP (kWK�1) } 27 39UAevLP (kWK�1) } 542 596UAecLP (kWK�1) } 75 87B (%) 100 103.1 132.1CkWh (%) 100 100 97.9

Figure 3. Efficiency (a) and generating cost (b) vs design parameters variations for the 1P cycle.

Figure 4. Efficiency and generating cost vs total UA variations for the 1P cycle.



Figure 7 compares the efficiency and the cash flow of the 1P cycle at part load for twodifferent pinch point design values. The 100% reference value for the cash flow series is thedesign point (100% load) with PP ¼ 10 K: It can be noticed that the cash flow begins to benegative when the load is under 55%. In such condition, the generating cost is higher than thepower grid selling price.

Figure 5. Efficiency and cash flow vs design parameters variations for the high pressureHRSG section of the 2P cycle.

0.55

0.50

0.45

0.40

0.35

0.30

0.25

102030405060708090100

Load

-200

-150

-100

-50

0

50

100

150

200

Eff PP=10 Eff PP=13

Cash Flow PP=10 Cash Flow PP=13

η

Figure 6. Gross power and efficiency of the gas turbine and the CCGT power plant (1P cycle)when the gas turbine load is modified.



5. CONCLUSIONS

In this paper two kinds of analysis for CCGT power plants have been performed: onethermodynamic and the other thermoeconomic.

As a common conclusion for all the simulations, it can be said that the results stronglydepend on the gas turbine selected and on the initial design parameters. Furthermore,these results cannot be extrapolated to other gas turbines or initial design parametersbecause of the complexity and huge number of the different CCGT power plantconfigurations.

The results suggest that it is of major relevance to find a balance between the efficiency and theeconomic results. It can be noticed that, when the design parameters are modified, thegenerating cost or cash flow series have minima or maxima better defined than the efficiencyseries. Thus, it is easier to find economic optima than thermodynamic ones. Moreover, all thecash flow maxima would be in the same point if a cash flow optimization was done, and all thegenerating cost minima would be in the same point if a generating cost optimization wasundertaken.

Generating cost and cash flow optimizations do not lead to the same design point. Therefore,it is important to specify the optimization objective. In the case of a strong competition in aliberalized electricity market, a generating cost optimization should be done; but in a regulatedmarket, the cash flow optimization is better because this way more incomes are got due to thehigher power reached.

Finally, thermoeconomic part load studies should be done in order to predict which is theprofitable load range of the power plant.

0

160000

140000

120000

100000

80000

60000

40000

20000

102030405060708090100

Load

kW

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

W TG (kW) W CCGT (kW) Eff TG Eff CCGT

η

Figure 7. Efficiency and cash flow for the 1P cycle in two different pinch point designswhen the gas turbine load is modified.



NOMENCLATURE

A =area (m2)AP =approach point (K)B =annual cash flow (h)Camort =amortization cost ( h year�1)Cfuel =fuel cost ( h year�1)CF =fixed cost (h)Cinst =overall capital cost per kW ( hkW�1)CkWh =generating cost ( hkWh�1)Co–m =operating and maintenance cost ( hyear�1)F =fuel cost per kWh ( hkWh�1)h =plant working hours per year (h)i =discount rateI =incomes ( h year�1)k =cost of a single HRSG section per UA product ( (hkWK�1)�1)ma =air mass flow (kg s�1)mf =fuel mass flow (kg s�1)n =economic life of the plant (years)P =pressure (bar)DP =pressure loss (bar)PP =pinch point (K)Q =heat exchanged (kW)S =selling price to the grid (h kWh�1)T =temperature (K)DT =gas to steam terminal difference of temperatures (K)U =global coefficient of heat transfer (kW (Km2)�1)%WW =annual mean gross power (kW)

Greek letters

Z =efficiencyr =compression ratio

Subscripts

amb =ambient conditionsC =compressorcond =condenserCC =combustion chamberGT =gas turbinedea =deareatorec =economizerev =evaporatorexh =exhaustHP =high pressure



Inl =inletLP =low pressuresh =superheaterST =steam turbine

ACKNOWLEDGEMENT

The authors wish to acknowledge the financial support of ENDESA to this work.

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influence of the heat recovery steam generator design parameters on the thermoeconomic performances...

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