Life-cycle Cost Analysis and Optimization of Gas-turbine-based Power Plants by Sequential Quadratic Programming Method for Distributed Generation

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1 International Proceedings of Chemical, Biological and Environmental Engineering, V0l. 100 (2017) DOI: /IPCBEE V 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.: ; fax.: address: ydlee@kimm.re.kr 64

2 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 65

3 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, C total C f Z PEC C O& M 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 (1) 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 SC RC Parameter Unit Base Optimized Base Optimized Operating conditions r AC ɳ AC ɳ GT m a kg/sec m f kg/sec T Cold, e C Life-cycle LCOE $/MWh SC: Simple Cycle, RC: Regenerative cycle 66

4 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 CCGT Parameter Unit Base Optimized Operating conditions r AC ɳ AC ɳ GT m a kg/sec m f kg/sec ɳ ST ɳ Pump m w kg/sec P Pump bar Life-cycle LCOE $/MWh 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. 67

5 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. 68

6 Table 4: Optimized operating conditions of CCGT power plants depending on different economic assumptions Variables Unit Indonesia India China Korea Assumptions for cost calculation c f $/GJ r n % i eff % Optimized design conditions r AC ɳ AC ɳ GT m a kg/sec m f kg/sec ɳ ST ɳ Pump m w kg/sec P Pump bar Life-cycle LCOE $/MWh Conclusion Sequential quadratic programming (SQP) method has been successfully applied to the cost reducing analysis for gas-turbine-based power cycles; a new set of operating conditions is proposed targeting the minimum life-cycle cost of each cycle. The total cost rates of the simple cycle, regenerative cycle, and combined-cycle gas-turbine can be minimized by 7.4%, 10.9%, and 4.0%, respectively, being compared to the base case, when applying the new operating conditions. The effects of economic situations have been investigated, revealing that the higher return on investment rate causes the optimizer to locate the optimum operating conditions with higher efficiency components, and the natural gas price has the most significant influence on the total life-cycle cost of electricity. The newly proposed operating conditions suggest that in a place with higher fuel price, higher component efficiency s is preferred, while in the place with lower fuel price and higher escalation rate, cost effective design has more beneficial. For the future work, other optimization technique will be considered for better comparison. 6. Nomenclature fuel cost rate ($/sec) operation & maintenance cost rate ($/sec) objective function ($/sec) cost of fuel per unit energy ($/GJ-LHV) LCOE levelized cost of electricity ($/MWh) m NG P mass flow rate (kg/sec) natural gas pressure (bar) air compressor pressure ratio SQP T sequential quadratic programing temperature ( C) 69

7 ɳ specific capital investment cost rate ($/sec) isentropic efficiency 7. References [1] P. H. A. Mahmoud, A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms, Renew. Sustainable energy Rev, ( [2] B. Owens, The rise of distributed power, General Electric Company, pp. 7-41, [3] Aspen Plus, Aspen Plus 12.1 User Guide. Cambridge: Aspen Technology Inc; [4] Gas Turbine World 2013 GTW Handbook, Pequot Publishing Inc, Vol 30. [5] S. R. Singiresu, Engineering Optimization Theory and Practice, NJ: John Wiley & Sons Inc, [6] J.S. Alsumait, J.K. Sykulski, A.K. Al-Othman, A hybrid GA PS SQP method to solve power system valve-point economic dispatch problems, Appl Energy, vol. 87, pp , [7] L. T. Biegler, I. E. Grossmann, A. W. Westerberg, Systematic Methods of Chemical Process Design, NJ: Prentice Hall, [8] A.Bejan, G.Tsatsaronis, M.Moran, Thermal Design and Optimization, NY: John Wiley & Sons Inc, [9] P. Roosen, S.Uhlenbruck, K.Lucas, Pareto optimization of a combined cycle power system as a decision support tool for trading off investment vs. operating costs, Int J Therm Sci, vol. 42, pp , [10] P. Ahmadi, I. Dincer, M. A.Rosen, Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants, Energy, vol. 36, pp ,