LIFE CYCLE ANALYSIS AND OPTIMISATION OF A COMBINED CYCLE BASED ON THE INDUSTRIAL TRENT 50

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1 LIFE CYCLE ANALYSIS AND OPTIMISATION OF A COMBINED CYCLE BASED ON THE INDURIAL TRENT 5 Jenny Persson Lund Institute of Technology Dept. of Heat and Power Eng. P.O. Box 118, SE-221 Lund SWEDEN cimjp7@m.lth.se ABRACT In cooperation with Rolls-Royce and Lund University, a combined cycle power plant was modelled, simulated and optimised in the new software framework, the GTPOM tool, developed through the recent EC FP5 programme. The goal was not only to calibrate its component models but also to show how this new tool can be used. This paper presents the gas turbine and combined cycle whole plant modelling and its life cycle cost optimisation within the GTPOM tool. In addition, this paper discusses the software tool bearing in mind that the existence of other commercially available software packages for the purpose. The other thermal engineering software package referred in this project is a product of Thermoflow Inc and is a combination of three software modules, GTPRO, GTMAER and PEACE. In this project, data from this package was used to calibrate the CCGT plant model in the GTPOM tool. Rolls-Royce Industrial Trent 5 gas turbine was modelled in the GTPOM tool with the abilities to calculate both designpoint and part-load performance for given ambient temperature and exhaust loss. The CCGT power plant with a 2-on-1 configuration was modelled in the GTPOM tool. The component model library developed in the GTPOM project was used, however the component models were modified and improved through calibrations with the reference GTPRO data. The life cycle cost and through life economic parameters of the CCGT whole plant in GTPOM have also been analysed, and then this whole plant model was used as the non-optimised benchmark model for a study in which the model was optimised in the GTPOM tool using a GA optimiser in order to maximise the Internal Rate of Return (IRR). NOMENCLATURE Abbreviations CCGT Combined Cycle Gas Turbine DLE Dry Low Emission EC European Commission GA Genetic algorithm GT Gas Turbine GTPOM Gas Turbine Plant Optimisation HP High Pressure HRSG Heat Recovery Steam Generator IP Intermediate Pressure IRR Internal Rate of Return LMTD Logarithmic Mean Temperature Difference LP Low Pressure PEACE Plant Engineering and Construction Estimator Steam turbine Designations C Correction factor for number of tube rows h Specific enthalpy mf Mass Flux Pr Prandtl number Re Reynolds number x 1,2,3,4 Steam turbine coefficients Y output α Heat Transfer Coefficient ε Ratio of total surface area to tube surface area INTRODUCTION When a gas turbine is used to produce electricity the heat loss from the exhaust is not negligible. One way to use this waste heat is to combine the gas turbine cycle with one more thermal cycle, like a steam cycle. The total efficiency achieved is then higher than that of one cycle alone. The cycle that is 1

2 working at the higher temperature, in this case the gas turbine, is called the topping cycle and the cycle that uses the waste heat, the steam cycle, is the bottoming cycle. The configuration is called a combined-cycle gas turbine (CCGT). The main challenge in designing a CCGT plant is how to transfer the heat from the gas turbine exhaust to the steam cycle to achieve optimum benefits. The focus is on the heat recovery steam generator in which the heat transfer takes place and the performance depends on how much money that is put into it, i.e. there is a balance between efficiency and capital cost. In order to know which solution to choose, a complete economic life cycle analysis has to be carried out. Calculating by hand would be too time demanding and it is therefore necessary to use some computer method to analyse the best solution. The purpose of this project was to model a benchmark CCGT power plant with aero-derivative multi-shaft gas turbines in the new developed software package GTPOM tool and evaluate the result of the life cycle analysis and the effect of optimisation. The benchmark plant configuration needed to be realistic with reference plant information so that the effect of the optimisation can be evaluated properly with comparison to the reference plant design concept. The task was also to show the ability of the tool and how it can be used, i.e. when the GTPOM tool is the preferable tool to use compared to other software. THE PROCESS SIMULATION TOOL GTPOM The process simulation software used in this project is called GTPOM tool and was previously developed through an European funded project and Rolls-Royce, Alstom Power, Simtech, Energy E2, Lund University, University of Genoa and University of Newcastle Upon Tyne were involved as the programme partners. The GTPOM tool is a derivative of IPSEpro, which is a process simulation software product of Simtech, with enhanced capability in plant life cycle cost analysis and optimisation. While the standard IPSEpro is a system for calculating heat balances and simulating processes, GTPOM project gave it additional functionalities, i.e. a special component library, GA (genetic algorithm) optimiser and a detailed life cycle cost analysis module, PSEconomy. The economic module, PSEconomy, includes the ability to determine the whole system capital cost, economic scenario, and operational scenario, and to calculate and optimise life cycle cost and through-life economic performance of the plant, based on the cycle configuration, operational and economic scenarios selected. As the overall GTPOM tool, there are four key functions as follows: Thermodynamic process modelling capability Economic analysis capability Optimisation capability Overall software integration The optimisation capability enables the configured cycle to be optimised in accordance with the specific requirements of the user. The ability to optimise through life cost performance can be used to maximise the economic benefit of the system. Genetic algorithm (GA) is used as the optimisation technique. The principle of GA was developed by JH Holland and is based on the concept of survival of the fittest [i]. Reference [ii] gives a good overview of this method and it also gives an example in the heat and power plant field. The variable to be optimised i.e. efficiency or internal rate of return is called the objective function while the variables or parameters to be intentionally varied i.e. approach temperatures, pressures etc. are called decision variables. First, multiple calculations for sampling purposes happen with randomly selected values of decision variables within allowable ranges specified, and each value of the decision variables is coded into a binary string called phenotype. Then the selection of the phenotypes as the natures of individuals starts by finding the fitness of each individual according to the objective function e.g. maximizing the efficiency. Only the best individuals will survive to breed. Different combinations of two parents (phenotypes) create offspring and the new generation is being evaluated. Also a small population goes through a mutation, a small change in the binary code to generate solutions that were not included in the starting population [iii]. Using GA a complex mathematical plant model can be optimised without requiring derivative information, however according to [iv] the GAs are computationally expensive and since GA is based on a random process the efficiency of solving a given problem is impossible to predict. However, the beauty of GA in optimisation problems is its robustness when a system with multiple decision variables has non-linear behaviour and/or multiple optima. Compared to full/pure random search, in addition, it is relatively less time consuming to seek a global optimum. CCGT PLANT MODELLING AND OPTIMISATION In this study, the Rolls-Royce Trent 5 was modelled in the GTPOM tool as a preparation for the combined cycle modelling. The Trent 5 is an industrial gas turbine, with Dry Low Emission (DLE), derived from the aero engine Trent 8 that powers some of the Boeing 777 aircraft. In order to simulate gas turbine performance at both design and off-design points including all effect of ambient conditions and pressure losses, it would be ideal to model each major part of the gas turbine precisely in the GTPOM tool, i.e. compressors, combustor and turbines etc. This would however be time consuming, and the main objective of this study is optimisation of a combined cycle plant with an existing gas turbine and without optimisation of the gas turbine itself. Instead, data for the GT is provided from Rolls-Royce s performance software called etrent, and was coded to the GTPOM tool as a lumped component. Polynomial equations to express the engine characteristics were used and the benefit with this is that it is possible to obtain very good accuracies within restricted boundaries; pinlet = 127 mm H 2O 4 C Tambient 5 C p 4 mm H O exhaust The relative humidity and the ambient pressure is set to ISO standard and cannot be changed in the model developed in this study at this moment in time, however due to [v] and [vi] the 2 2

3 NG OBJECT. FUNCT. (NG) GT NG HEX (NG) change of these parameters has a negligible influence on the gas turbine performance. When the power is greatly decreased the engine starts to behave differently from the approximation with the polynomial equation (probably because of bleed flow) and the error increases significantly. Therefore it was decided to put a lower limitation on the power output of 4% of maximum power output. In order to carry out CCGT plant modelling, necessary information to be calculated in the gas turbine model for specified ambient conditions and given intake/exhaust losses is as follows: GT shaft power output GT fuel flow rate GT exhaust flow rate GT exhaust temperature GT exhaust composition (Gas fuel feed pressure to GT interface) The gas turbine power output is normally specified as input, however it can be trimmed based on maximum power limitations based on the specified ambient conditions and given intake/exhaust losses. The efficiency of the engine can be calculated using the fuel flow rate, fuel s lower heating value and the power output at the GT output shaft. The exhaust flow rate, temperature and composition are for thermal balancing downstream from the gas turbine. When the ambient temperature and the power output change, this will affect the fuel consumption and therefore the efficiency. After modelling the gas turbine as a lumped component the whole CCGT plant model was carried out. As the component models in GTPOM tool needed to be calibrated first a plant configuration with an existing reference dataset from one of the conventional commercially available CCGT plant simulation tools, GTPRO was sought. Referring to existing GTPRO datasets, a 2-on-1 CCGT configuration with multi-pressure steam cycle as the bottoming cycle was chosen based on discussions held in Rolls-Royce. This means that the CCGT plant consists of two gas turbines (Trent 5), two Heat Recovery Steam Generators (HRSG) and one steam turbine (). The water/steam system chosen consists of three different pressure levels where the water with the lowest pressure goes to the deaerator (this water does not go into the turbine directly without further pressurisation). This configuration is known as 2.5 pressure system. An overview (screen shot) of the CCGT plant modelled in the GTPOM tool is shown in Figure G HEX HEX HEX HEX HEX HEX HEX HEX HEX mass[kg/s] h[kj/kg] p[bar] t[ C] Power output kw Net efficiency % COE /kwh HRSG efficiency % Figure 1 Screen shot for the CCGT plant in GTPOM tool. The model is a 2 on 1 configuration, however to avoid unnecessary equations only one GT and HRSG has been modelled and the flow has then been doubled before entering the steam turbine. In order to use the data from GTPRO as reference for the Life Cycle Analysis some of the component models had to be modified. The heat exchanger models where modified in three different aspects: 1. Reynolds number on water side In the present GTPOM the assumption that all heat exchangers consists of U-tubes where the flow is divided into each tube. water mass flow mass flux = Number of U tubes tube area Equation 1 However the data from GTPRO showed that the number of tube rows was not equal to the number of rows per water side flow pass. Therefore the mass flux becomes: water mass flow mass flux = Number of inlets tube area Equation 2 This change increased the Reynolds number and thereby the heat transfer coefficient on water side. 2. Correction factor for number of tube rows The second adjustment that was done was that the heat transfer coefficient seemed to be slightly too low when the configuration consists of more then one tube row. Therefore the assumption was made that the equation 1 for calculating the heat transfer coefficient was based on one tube row, Equation 3. According to [vii] the heat transfer coefficient is increasing when the number of tube rows is increasing since there then is a higher probability that the gas side exchange heat with the wter side.,625,333 α =,3 Re Pr ε Equation 3, 375 Table 1: Correction factor for number of tube rows. Number of rows C After these corrections were done, the overall heat transfer coefficient were more accurate compared to the GTPRO reference data. 3. Correction of heat exchanger specific cost The heat exchanger specific cost (Euro per unit heat transfer area) was reviewed and has been adjusted. 1 The equation used in GTPOM is based on an in-line arrangement. C 3

4 Also some adjustments for the original GTPOM steam turbine unit model were carried out. The investment cost of the however was changed and the one that it was decided to use is based on a simple exponential cost curve decaying as unit size grows. This equation has been found to have high accuracy when bidding projects. Specific Cost = x + ( ) 1 x4 x3 + Y Equation 4 The CCGT plant in this project was modelled with an aircooled condenser that was not originally in the GTPOM library. A simple air-cooled condenser had been created during the previous GTPOM project, but it was never implemented into GTPOM library. This simplified air-cooled condenser model has been utilised with modification in this project. Equations were added to calculate the heat transfer coefficient on air-side while the heat transfer on steam/water side was set to a fixed value. When both the heat transfer coefficients on each side had been calculated the overall heat transfer coefficient could be found, and with the logarithmic Mean Temperature Difference and heat duty the heat transfer area could be calculated. Same type of exponential function (Equation 4) that was used for the cost calculation was then used to calculate the investment cost for the condenser based on the heat transfer area. Another component that was used was a gas fuel compressor. The required fuel gas pressure from the gas fuel skid depends upon fuel composition, fuel temperature, ambient temperature and engine loading. Fuel temperature limits are specified to avoid any condensation in the fuel system. The fuel should be delivered between 38 C and 149 C and at least 2 C above the maximum dewpoint of the fuel. The required fuel pressure for the Trent is from [viii] simplified to an equation based on load. This curve, Figure 2, has a good accuracy between 3% and 1% load, which is in this case sufficient because of the low power limitation for the GT model (down to 4% load). Fuel pressure requirments x 2 Table 2 Economic Scenario Construction 24 months Operation 15 years Inflation Rate Not considered Discount Rate 6.5 % An outline for the operational scenario selected is shown in the table below. Case 1 and 2 models the plant at design point (1% load) while case 3 and 4 are based on a part load operation (7% load). It was assumed that each power generation system achieves an average availability of 95% hence the total number of running hours per year is The cases are the same as presented in [ix] except for the selling price that has been slightly changed. The selling price is based on virtual prices for the UK and was decided after discussion within Rolls-Royce. Table 3 Operating scenario Operating Operating Power output Electricity selling Case hours/year % price cents/kwh The fuel price is a very significant factor in the economics but is also very difficult to predict. The fuel price used in GTPRO was considered to be overestimated due to the European market and therefore the data used in this project was taken from [ix] which is an average future gas price prediction derived from a number of gas price predictions for EU Member States taken from public domains. The O&M costs for the genset and the were merged to one variable cost depending on the whole power plant power delivery and defined in free equations in the PSE model. Other costs such as staff and insurance were defined in PSEconomics. Table 4 shows the including HRSG and condenser and fixed O&M costs for the power plant. These numbers are based on previous examples and calculations. Table 4 O&M costs 5 4 Pressure [bar] % 4% 5% 6% 7% 8% 9% 1% Load Figure 2 Required fuel pressure. LIFE CYCLE CO AND ANALASYS The software module PSEconomics as a part of the GTPOM tool was used for the whole plant Life Cycle Analysis and Optimisation. In PSEconomics the economic scenario and operating cases were defined after discussion in Rolls-Royce. Below is a table of the input data to PSEconomics. The O&M costs for the genset and the including HRSG and condenser was added up to one cost divided by the power output of the plant. When the economic scenario was set up it was chosen to optimise the plant according to the IRR. The IRR is defined as the discount rate which sets the net present value of a series of cash flows over the planning horizon equal to zero and is used as a profit measure. The IRR gives the return of an investment when the capital is in use as if the investment consists of a single outlay at the beginning and generates a stream of net benefits afterwards. The total capital investment was calculated and compared with the GTPRO data and the accuracy (5% lower for GTPOM, see Table 5) was very good considering that in reality these figures can differ by up to 2% from different tenders when bidding project. The difference in IRR is mostly because of the 4

5 fuel cost in GTPRO was lower than that which was set in this project. Table 5 Capital investment and IRR comparison GTPOM GTPRO Total capital investment 94.95% 1% (base) Internal Rate of Return 1.3% 13.7% The optimisation of the whole power plant was done for two different cases. Case one (DV 11) used 11 decision variables to optimise the plant, and case two (DV 12) used one more decision variable when the plant was optimised. RESULTS Comparison of plant efficiency before and after optimisation, Table 6, shows that the IRR was improved by.4-.5% units by the optimisation. Table 6 Results of optimisation Power output Net efficiency HRSG efficiency COE IRR Capital Investment Before 1% (base) base 71.43% base 1.3% 1% (base) After, DV % -.21%- points 68.83% -.1 c/kwh 1.7% 96.76% After, DV % -.25% points 69.15% -.1 c/kw/h 1.8% 95.96% Figure 3 shows how the investment costs for all the components have changed after the optimisation. The investment cost for the heat exchangers was reduced by 18% compared to the non-optimised plant. Since the gas turbine has not been optimised in this work the investment cost for the genset was set to a fixed value and has not been changed during the optimisation. Investment cost Investment cost breakdown Base case DV 11 DV 12 Figure 3 Construct cost Plant engineering Power TD Buildings The lower investment cost will decrease the efficiency of the plant, however the IRR will be improved through the decrease in cost. The TQ diagram below shows the difference before and after optimisation with 11 decision variables. The thick lines represent the optimised case. The recovered heat in the HRSG is less for the optimised 48 MW and 5MW heat has been recovered in each case. Genset HRSG C TQ diagram kw Figure 4 Heat duty Gas side Before optimisation Gas side after optimisation Water side before optimisation Waterside after optimisation The change of the temperature approaches in the heat exchangers affects the areas and thereby the investment cost. Figure 5 compares how the area in each heat exchanger has changed after the optimisation and it shows that the overall heat transfer area has decreased m 2 Area of HRSG LTE IPE2 IPB HPE2 IPS1 HPE3 IPS2 HPB1 HPS3 Figure 5 Base DV 11 DV 12 CONCLUSIONS The result from the optimisation shows that both the HP pressure and IP pressure were increased from 53 (HP) and 11 bar (IP) to 83 (HP) and 13 bar (IP) respectively. The LP pressure decreases from 1.19 bar and goes towards the lowest value 1 bar, which is close to ambient pressure. This is what we expected since it needs, less steam from the boiler to the deaerator (lower saturation temperature). To receive a high HRSG efficiency the temperature difference should be kept low and the area of the heat exchangers large, however a large area also means a large investment cost and it is therefore a balance between cost and efficiency that needs to be considered. The bottoming cycle optimisation resulted in a total capital cost reduction of the bottoming cycle of up to 3% (HRSG 18% and steam turbine 1.5%). The improvement of the IRR through the optimisation in this present work has remained much more modest degree,.4%-points to.5%-points, compared to the previously presented IRR improvements by the case studies in the GTPOM programme [ix]. There are several reasons for this: The gas turbine capital cost has been dominating the majority of the total capital investment cost of the whole plant. The previous GTPOM programme optimised the topping cycle as well but this has not been done in the optimisation in this present work because the gas turbine component was agreed to be modelled as a lumped component model in order to calibrate the GTPOM tools component models for bottoming cycle with reference CCGT plant information. 5

6 The benchmark model of the power plant in the GTPOM tool was calibrated and compared against the GTPRO data which had already been optimised in the thermoflow software PEACE and therefore the result was expected to give a modest degree of improvement. The gas turbine exhaust conditions differ both from the values in GTPRO and the reference data from etrent. However even though it differs by up to 1% the effect on the IRR will not be of this order because of other parameters i.e. fixed costs that have a much greater impact on the IRR then the errors in exhaust conditions. Although the Trent 5 model has been successfully implemented and the component models for the bottoming cycle part in the GTPOM tool library have been calibrated and improved through this present work, the above results and findings has been conversely implying that optimising whole CCGT plant including optimisation of gas turbine itself is important in order to drastically improve IRR of a CCGT whole plant. that the author had to spent significant time to find suitable GA parameter settings for the problem given as a part of the preparation for the optimisation. Therefore when creating and analysing a typical CCGT plant configuration, some of the existing commercially available software packages like GTPRO with its rich previous example database could be more user-friendly, guiding and giving relatively less experienced users to more conservative but realistic system configurations. However when it is wished to develop a new configuration e.g. HAT (Humid Air Turbine) cycles, or to model a detailed gas turbine with each component as an own unit, the GTPOM tool is an excellent tool because of its flexibility. The tool also gives the user full control over the model i.e. equations for heat and mass balance, investment costs and maintenance costs etc., and he/she will be able to create special requirements and/or make new components which would not be possible in other commercial available tools like GTPRO. In conclusion, continuous usage and exploration of the potential of a very flexible tool like GTPOM tool is worth doing despite some of the issues identified in user-friendliness. Current conclusions about the tool GTPOM: GTPOM is a very flexible tool to make detailed thermodynamic performance evaluation and detailed through-life economic analysis and optimisation. It has the ability to improve either the thermodynamic inputs or the economic scenario depending on what is to be achieved. In this present work the author experienced some convergence problems when running the calculations of the whole CCGT model in the GTPOM tool. In addition, it must be noted ACKNOWLEDGMENT I would like to thank those who have helped me here at Rolls Royce and also the people at Lund University that have been to great help during this project. RERERENCES i Holland JH, Adaptation in natural and artificial systems, The MIT Press, Cambridge, Massachetts, 1992 ii Fredriksson Möller B., Optimisation and Integration of Post-Combustion CO2 Capture in Gas Turbine-based Power Plants, Ph.D. Thesis, Lund Institute of Technology, 23 vii Holman J.P., Heat Transfer, Sixth Edition, McGraw-Hill International Editions, Singaprore, 1986 viii Rolls-Royce, Trent Application Handbook, 1998 ix Rolls-Royce, et. Al, Thermo-Economic optimisation of Whole Gas Turbine Plant, Project N NNE , Contract N ENK5-CT2-79 iii Arriagada J. On the Analysis and Fault-Diagnosis Tools for Small-Scale Heat and Power Plants, Ph.D. Thesis, Lund Institute of Technology, 23 iv Codeceira A., Pilidis P., An assessment of Power Plants Using Genetic Algorithms, Paper 21-GT-56, Proc. Of ASME Turbo Expo 21, June 4-7, New Orleans, U.S.A v Mesbahi E., et. Al, A Unique Correction Technique for Evaporative Gas Turbine (EVGT) Parameters, Paper 21- GT-56, Proc. Of ASME Turbo Expo 21, June 4-7, New Orleans, U.S.A vi Mesbahi E., et. Al, An online and Remote Sensor Validation and Condition Monitoring System for Power Plants, Proceedings of CIMAC Congress 21, , Hamburg, Germany 6