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Life-cycle Cost Analysis and Optimization of Gas-turbine-based
Power Plants by Sequential Quadratic Programming Method for
Distributed Generation
Satriya Sulistiyo Aji 1
, Young Duk Lee 1,2+
and Kook Young Ahn 1,2
1 University of Science & Technology (UST), Department of Environmental and Energy Mechanical
Engineering, 156 Gajeongbuk-ro, Yuseong-gu, Daejon South Korea 2 Korea Institute of Machinery & Materials (KIMM), 156 Gajeongbuk-ro, Yuseong-gu, Daejon South Korea
Abstract. The purposes of this study are to analyze and to find the way to reduce life-cycle cost of
electricity of gas-turbine power plants for wide spread of distributed power generation by employing
mathematical optimization. Three kinds of power cycle, which are based on gas-turbine, have been
thermodynamically simulated and optimized from cost viewpoint. To understand the effects of economic key
parameters, such as natural gas price, return on investment rate, and escalation rate, on the optimum
operating condition and the total cost, case study has been carried out by taking four different countries'
economic situations into account: Indonesia, India, China, and South Korea. A commercial software ASPEN
Plus® and Sequential Quadratic Programming (SQP) method are used to complete the energy balance and to
minimize the total cost rate, respectively. Results reveal that 4-10% life-cycle cost reduction can be achieved
when the new design conditions are applied to the gas-turbine power plants; the conditions are suggested by
the SQP method targeting minimizing cost. Through the results we can concluded that the efficiency
enhancement has significant effect on cost reduction for Chinese and Korean cases mainly due to their high
fuel price, while initial investment cost is of importance for Indonesian and Indian cases; the new design
condition, a cost effective one, can be derived and employed for the cases.
Keywords: distributed generation, life-cycle cost, gas-turbine, optimization, sequential quadratic
programming (SQP)
1. Introduction
Interests on the distributed power generations have been increasing during the past few years, not only
because of the economic and technical beneficiary, but also their possibility of reducing environmental
footprint of the existing power plants [1]. Medium-size gas-turbines are considered as the most
technologically and economically matured among the dispatchable and non-dispatchable technologies for
distributed generation [2]. However, small- or medium-size gas-turbine power plants suffer from high
investment cost comparing to the large centralized power plants; moreover, relatively low electrical
efficiency is inevitable; thus resulting in a high fuel cost. Therefore, the cost minimization is essential for the
wide spread of the decentralized power generation.
Regarding the mentioned issue, this study has analyzed the life-cycle cost of electricity of gas-turbine
power plants by means of mathematical optimization, particularly focusing on the distributed power
generation. To carry out the thermodynamic and economic analysis, commercial software ASPEN Plus® [3]
is used to complete the mass and energy balance of gas-turbine power cycles. Commercials gas-turbine
models SGT-700 and SCC-700, manufactured by Siemens, are used for the base-case simulation [4]; these
gas-turbines show high electrical efficiency of 52.3% for the combined-cycle (SCC-700) at 45.2MW of
electricity production. After fulfilling the mass and energy balance, a mathematical optimization has been
+ Corresponding author. Tel.: + 82 42 868 7945; fax.: +82 42 868 7284.
E-mail address: [email protected]
International Proceedings of Chemical, Biological and Environmental Engineering, V0l. 100 (2017)
DOI: 10.7763/IPCBEE. 2017. V100. 11
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carried out and a new set of design conditions for three kinds of power cycles, simple gas-turbine,
regenerative gas-turbine, and combined-cycle gas-turbine is proposed, targeting the minimum life-cycle cost.
Sequential quadratic programming (SQP) method is employed to minimize the levelized life-cycle cost
of electricity. Finally, case study is carried out to investigate the effects of natural gas price, rate of return on
investment, and escalation rate on the optimal operating conditions of combined-cycle gas-turbine power
plants.
2. Analysed System
For comparative study, Simens’ industrial gas turbine SGT-700 model has been chosen as a base model
for the simple gas-turbine, which generates 32 MW at nominal operating condition; the schematic is shown
in Figure 1. Aspen Plus® inbuilt models such as, COMPR blocks are used to model the compressor and
turbine, and RSTOIC reactor block is used to model the combustor. The natural gas is assumed as a fuel; a
complete combustion is assumed for the combustion process.
Fig. 1: Flow-sheet of gas-turbines power plants, A) Simple cycle, B) Combined cycle
The temperature of exhaust gas leaving the turbine is higher than the air leaving the compressor, it can be
used to heat the exiting stream of compressor by means of regenerator to increase the thermal efficiency,
which known as regenerative cycle. The flow sheet of regenerative cycle is shown in Figure 2. HEATX
block is use for the counter-flow heat exchanger.
Fig. 2: Flow-sheet of regenerative gas-turbines power plants
Another way to increase the thermal efficiency is made by externally recovering the waste heat to
produce steam in the heat recovery steam generator (HRSG) to drive the steam turbine. As shown in Figure 1,
a single pressure combined-cycle gas-turbine is used for the system simulation. The HRSG consists of
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economizer, evaporator, and superheater sections, which are modelled by HEATX blocks, and a single
pressure drum is modelled by FLASH2 block. The commercial data obtained from Siemens SCC-700 is used
for the validation of the combined-cycle gas-turbine model.
3. Cost Calculation and Optimization
The sequential quadratic programming (SQP) is one of the most recently developed and perhaps one of
the most effective methods of optimization [5]. The SQP essentially solves the nonlinear problems by which
the approximation is made of the Hessian of the Lagrange function using Newton’s updating method [6], to
generate a quadratic sub problem that is easier to solve. The problem converges when the Karush-Kuhn-
Tucker (KKT) conditions are satisfied [7] Since the SQP is not a feasible-point method, the initial guess does
not need to be in a feasible range of the constraints; this is indeed the advantageous of employing the SQP.
Cost calculation of this paper has followed the procedure suggested in Ref. [8]; the purchased equipment
costs, fuel costs, and operation & maintenance costs are considered, covering 20-year-lifetime of the power
plants. Several methods have been proposed to express purchase cost equipment in terms of operating
conditions; here we use the cost function suggested by Ref. [9]-[10], and then update the cost to the current
year by using cost index. Since the target of the optimization is to minimize the cost rate of gas-turbine
power plants, the objective function is defined as,
MOPECftotal CZCC &
(1)
The basic assumptions for the economic calculation is summarized in Table 1.
Table 1: Assumptions used during the cost analysis
Parameter Unit Value
Return on investment rate % 7.0
Plant’s capacity factor % 91.3
Annual O&M cost rate % 6.0
Fuel escalation rate % 2.4
Goods escalation rate ( % 2.1
LNG price in South Korea $/GJ 17.24
4. Results and Discussion
By fixing the turbine inlet temperature and power output at 1145℃ and 32.8MW (simple cycle &
regenerative cycle), 45MW (CCGT), the SQP algorithm varies the decision variables in a suitable range and
determines the new design conditions of gas-turbine power plant, minimizing the total cost rate of the system
within the constraints.
Table 2: Optimized operating conditions of simple and regenerative gas turbine
Parameter Unit SC RC
Base Optimized Base Optimized
Operating conditions
rAC - 18.70 24.93 18.7 10
ɳAC - 0.91 0.906 0.910 0.928
ɳGT - 0.90 0.927 0.900 0.925
ma kg/sec 90.56 88.04 95.1 91.0
mf kg/sec 1.81 1.61 1.73 1.53
TCold, e °C - - 501.6 555.8
Life-cycle
LCOE $/MWh 214.5 198.6 207.7 184.9
SC: Simple Cycle, RC: Regenerative cycle
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Fig. 3: Cost minimization of gas-turbine cycle
As presented in Table 2, the SQP optimization suggests the new operating conditions of simple cycle and
regenerative cycle gas-turbine in the direction of higher efficiency of components; this is because more
efficient components lead to less fuel consumption and therefore the fuel cost, since the fuel cost dominantly
influences the total cost rate. As setback higher equipment is unavoidable, however the effect is not as
significant as the fuel cost.
It is noted that in regenerative cycle, the pressure ratio of compressor is newly set at lowest value of
variation to maximize the thermal energy recovery in the heat exchanger.
As depicted in Figure 3, the total cost rate can be reduced by 7.4%, 10.9%, and 4.0% for simple cycle,
regenerative cycle, and combined-cycle gas turbine, respectively by adapting the new suggested design
conditions of each cycle. The new design condition of gas-turbine combined-cycle is presented in Table 3.
The effects of economic parameters on the optimum condition of gas-turbine power plants are
investigated by employing the different values in cost calculation, reflecting the different economic situation
of four different countries. New design conditions of gas-turbine power plants are proposed as given in Table
4, and respective cost rates are shown in Figure 4.
Table 3: Optimized operating conditions of combined-cycle gas-turbine
Parameter Unit CCGT
Base Optimized
Operating conditions
rAC - 18.7 22.6
ɳAC - 0.91 0.897
ɳGT - 0.90 0.921
ma kg/sec 88.12 86.32
mf kg/sec 1.752 1.62
ɳST - 0.85 0.90
ɳPump - 0.85 0.74
mw kg/sec 13.34 13.51
PPump bar 28 25.6
Life-cycle
LCOE $/MWh 167.2 160.7
As summarized in Table 4, higher escalation rate results in an increase of the cost associated with the
initial investment costs as well as the operation & maintenance costs; however the effect is very small;
therefore the life-cycle cost of electricity is not changed for all the analyzed countries. The highest
contribution was made by the fuel cost.
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Fig. 4: Cost rate of optimized CCGT for each analyzed country
Fig. 5: Effect of return on investment rate and natural gas price on the LCOE of gas-turbine power cycles
The influence of return on investment is not apparent as shown in Figure 5, mainly because natural gas
price is the main contributor to the overall life-cycle cost of the power plants. The higher rate of return on
investment, the optimum operating conditions tend to move to more efficient design; in this case, the
influence of fuel cost decreases while the capital investment cost and operation & maintenance cost increase.
Therefore, thermodynamically optimal designs are more profitable to be installed in the country where fuel
price is high.
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Table 4: Optimized operating conditions of CCGT power plants depending on different economic assumptions
Variables Unit Indonesia India China Korea
Assumptions for cost calculation
cf $/GJ 4.31 6.63 10.8 17.24
rn % 4.1 5.03 3.0 2.1
ieff % 11.0 14.0 8.0 7.0
Optimized design conditions
rAC - 18.91 20.9 21.65 22.6
ɳAC - 0.859 0.900 0.900 0.896
ɳGT - 0.900 0.901 0.917 0.921
ma kg/sec 94.56 87.5 86.4 86.3
mf kg/sec 1.82 1.68 1.64 1.62
ɳST - 0.85 0.9 0.9 0.900
ɳPump - 0.60 0.60 0.64 0.74
mw kg/sec 13.4 13.2 13.1 13.5
PPump bar 28.15 32.2 28.3 25.6
Life-cycle
LCOE $/MWh 57.6 83.2 111.2 160.7
5. Conclusion
Sequential quadratic programming (SQP) method has been successfully applied to the cost reducing
analysis for gas-turbine-based power cycles; a new set of operating conditions is proposed targeting the
minimum life-cycle cost of each cycle. The total cost rates of the simple cycle, regenerative cycle, and
combined-cycle gas-turbine can be minimized by 7.4%, 10.9%, and 4.0%, respectively, being compared to
the base case, when applying the new operating conditions. The effects of economic situations have been
investigated, revealing that the higher return on investment rate causes the optimizer to locate the optimum
operating conditions with higher efficiency components, and the natural gas price has the most significant
influence on the total life-cycle cost of electricity. The newly proposed operating conditions suggest that in a
place with higher fuel price, higher component efficiency’s is preferred, while in the place with lower fuel
price and higher escalation rate, cost effective design has more beneficial. For the future work, other
optimization technique will be considered for better comparison.
6. Nomenclature
fuel cost rate ($/sec)
operation & maintenance cost rate ($/sec)
objective function ($/sec)
cost of fuel per unit energy ($/GJ-LHV)
LCOE levelized cost of electricity ($/MWh)
m mass flow rate (kg/sec)
NG natural gas
P pressure (bar)
air compressor pressure ratio
SQP sequential quadratic programing
T temperature (°C)
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specific capital investment cost rate ($/sec)
ɳ isentropic efficiency
7. References
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