A Computation Module for Optimizing Electricity Production in a Single Flash-Typed Geothermal Power Plant

Transcription

1 Proceedings of the 3rd Applied Science for Technology Innovation, ASTECHNOVA 2014 International Energy Conference Yogyakarta, Indonesia, August 2014 A Computation Module for Optimizing Electricity Production in a Single Flash-Typed Geothermal Power Plant Katherin Indriawati, Gunawan Nugroho, Bambang L. Widjiantoro, Totok R. Biyanto Engineering Physics Department, ITS Kampus ITS Keputih Sukolilo, Surabaya, Indonesia katherin@ep.its.ac.id; gunawan@ep.its.ac.id ABSTRACT The method for the determination of optimum operating condition of geothermal powerplant is developed in this paper. The purpose of the method is to develop a computation module in order to find maximum electricity production. The optimum process conditions are then defined according to the change of fluid properties in the wellhead. This will increase the powerplant efficiency or maintain the existing condition. The research is conducted in the Kamojang Geothermal Powerplant in West Java, Indonesia. The results show that the building model has the deviation level at 0.85% for thermodynamics model and 0.68% for load calculation. The optimization of net power increases the net output at 1.6 MW (2.6%) and lift up the load power at 300 kw (0.5%) compare to the measured net output and net load power. KEYWORDS: Geothermal power plant, single flash, optimization, computation module 1 INTRODUCTION Geothermal power plant (GPP) which has been built and operated in Indonesia is a single-flash type which is the most widely used technology today. According DiPippo (2008), in July 2004 there have been 135 single flash steam plant units operating in 18 countries. This is due to the ease and reliability of this type of plant. In Indonesia, the entire GPPs are built by using this technology. The operating parameters of a single flash system determine the efficiency of the production electric power. Thus, the efficiency can be maintained at its optimum condition if the operating conditions in the system are in optimal values. Therefore, to obtain the capacity of electricity with the optimum power, optimization technique is required in the determination of the production process parameters. There are previous researches had been conducted on the optimization of GPP. Siregar (2004) have done a power optimization study of the GPP for PLTP Sibayak, North Sumatra. Optimization on the research carried out for 3 new wells to be built at the plant, by determining the optimum pressure for the technical operation of the system. He analyze that the output from the turbine increases with lower condensing pressure as does the power consumption of the plant. Consequently, low condensing pressure will reduce the output from the power plant. In addition, Swandaru (2006) consider the design of thermodynamic analysis in Patuha, West Java. The results conclude that with the current supply from the existing nine wells, the turbine can produce a power output of 56,262 kw which is generated by a 6 bar separator pressure and a 0.08 bar-a condenser pressure. To obtain optimum output power and reduce equipment size, it is suggested thatthe power plant be designed using 6.5 bar separator pressure and a 0.1 bar-a condenser pressure. However, the determination of the optimum conditions have not been performed for various cases of wellhead enthalpy values (production wells). This problem has been discussed by Indriawati et al. 331

2 (2013) who concluded that selecting the variable conditions of the process depends on the dry level of geofluid. However, determination of optimum conditions in their paper is only done based on the graph analysis, instead of computational optimization techniques. Therefore, the computation module for electricity production optimization in a single flash is developed in this research to fill the gap. The calculation module described in this paper aims to facilitate the single operator flash power plant in determining optimum process conditions due to change of the geothermal fluid production wells. This is to maintain the efficiency of the electricity production process or even to improve the results. The research activities contain three stages, modeling and validation, optimization, and the optimization module programming. 2 METHODOLOGY 2.1 Process Production Modelling The process production of GPP is modelled based on thermodynamic laws and thus resulted in static models (steady). This step includes determination of any variables that affect the electrical power value. The processes undergone by the geofluid are best viewed in a thermodynamic state diagram. A temperature-entropy diagram for the single-flash plant is shown in Figure1. Geofluid under pressure at state 1 is flashed. The flashing process is modelled as one at constant enthalpy or isenthalpic process (h 1 = h 2 ). The total geofluid mass flow rate inside the plant is described in the following equation: 2 P m total = m max 1 (1) Pci where : m = wellhead mass flow rate(kg/s) total m max = wellhead maximum mass flow rate (kg/s) P = wellheadpressure (bar) = no input flow rate pressure (bar) P ci Figure 1: Temperature-entropy state diagram for the studied GPP 332

3 After the flashing process, state 2, the steam is separated from the brine in the separator. The separation process is modeled as one at a constant pressure or an isobaric process. Steam from the separator (state 3) will then pass through a demister for further moisture and solid removal. The mass fraction of the mixture at state 2, x 2, can be calculated from: h2 h8 x2 = h h 3 8 where h 3 and h 8 are the saturated liquid enthalpy and the saturated steam enthalpy at separator pressure. The saturated steam in the demister outlet (state 4) is sent to a steam turbine-generator group to produce electricity. The saturated mixture, state 5, is cooled and condensed in a direct contact condenser which is maintained at a vacuum pressure. The electrical power developed by the turbine is given by where and are the total wellhead outlet mass flow rate and the turbine efficiency;h 4 and h 5 are turbine inlet enthalpy and turbine outlet enthalpy. The enthalpy at turbine outlet, h 5, is calculated by solving: h4 h5 t = (4) h h 4 5s where is the isentropic efficiency of a turbine. Adopting the Baumann rule, the isentropic efficiency for a turbine operating with wet steam is given by: t = td x 4 x 5 2 with the dry turbine efficiency, may be conservatively assumed to be constant at 85%. The net power output is calculated by subtracting the power output of the turbine with auxiliary power consumption. The auxiliary power consumption considered in this paper are from a cooling-water pump, a vacuum pump for NCG removal and a cooling tower fan. The power of the vacuum pump is calculated by the following equation: (2) (3) (5) ( ) [( ) ( ) ] (6) where w g is the weight (%) of the NCG, R u is the universal gas constant (8.314 kj/(kmol.k)), T cond is the temperature of the condensate in K, is the efficiency of the pump, M g is the molar mass of the gas, p atm and p cond are the atmospheric and the condenser pressures (bar-a) respectively. The cold water entering the condenser is at about 24 C while the hot water leaving the condenser is at about 0.7 C higher than the condenser temperature. The cooling water mass flow rate is: (7) where c p is the specific heat of the cooling water and T 1 and T 2 are the inlet and outlet temperature of the cooling water in the condenser respectively. The dry air mass flow rate is: (8) where is the mass of the hot water entering the cooling tower. 333

4 To calculate the power of the cooling water pump as well as the cooling tower fan, uses the equation: where p is the drop pressure of the pump/fan (Pa), is the density of the fluid (cooling water or dry air), pump and motor are the efficiency of the pump/fan and the motor of the pump/fan. 2.2 Optimization Module Once the system model is formulated in the modeling phase, the next is to use the mathematical model in the optimization. Optimization of electrical power production of GPP is done by determining the optimum value of the production process (called the process variable). The variation of simulated process variables is conducted to obtain the optimum model output of GPP. In addition, constraints are applied when calculating the output of the production process for a GPP considering the physical limitations and assumptions. At this stage, the initial step is to lower the objective function because the target of this optimization is to get maximum power from production process, then the net power output is chosen as one of the terms in the objective function. The other terms of the objective function is for minimizing the power consumption load (auxiliary power). For this purpose, the objective function in this research is: = W net kw aux where k is a constant which can be 0, 1 or 2 depends on the power load cost calculation. The value of k = 0 indicates that the applied objective function is the net power. The value of k 0 is intended to accommodate cases where the selected net power decreased slightly but more power down the load thus saving production costs associated with the load. The next step is to determine the candidates of a parameter optimization which can be used as variables in order to maximize the objective function of Eq. (10). The influence of the parameters will need to be proven by simulation on the production process. Furthermore, the candidate parameters which affect the value of the objective function will be defined as the process variable. They have to be determined in order to optimize the power production under the changes in fluid geothermal resources and environmental conditions. For this purpose, the output simulation is conducted for several candidate parameter values, which makes the candidate as input parameters for the model of production systems. After the optimization variables are established, the next step is to create a software program that implements optimization techniques numerically. The software is built under Matlab. The first code is the calculation of the electric power plant based on a model that has been validated. For this purpose, there are two Simulink programs, the plant output calculation program (for calculation only on the operating conditions) and the program for optimization calculations (calculations on many operating conditions using looping algorithm "for"). In this study, the optimization method is applied is due to direct search. This because the objective function is not in the analytical form. The objective function also involves thermodynamic tables in searching the net energy value of powerplant. Thus it would be difficult to determine the derivative of the objective function. This problem is handled by direct search optimization method. The method of direct search optimization does not require any information about the gradient of the objective function. Unlike traditional optimization methods which use information about the gradient or higher-order derivatives to find the optimal points, direct search algorithms search for the set of points around the current point, looking for a point where the objective function value is smaller or the maximum compare to the current points. (9) (10) 334

5 One technique on direct search optimization method used in this study is the grid search. The steps are in the following: a) Determination of the appropriate grid in the design space; in this case the grid is structured as a combination of optimization variable values that have been set on the previous stage. The range of values is around the value of the parameter initial design. In this study the number of grid used is 300. b) Evaluate the objective function at all points of the grid; i.e. at each defined grid point, the objective function of Eq. (10) is calculated. Thus, this step produces a set of objective functions with various combinations of process parameters (grid). c) Applying constraints on the set of objective functions; in this case the inappropriate grid with predetermined constraints must be removed from the set of solutions to form a new set of objective functions that meet the constraints. d) Finding the associated grid points with the maximum value of the objective function; in this case the search for the maximum value of the objective function on the set generated in step c using MATLAB functions max. This value is the optimal value of the objective function (in this case is the net power) and grid which is associated with that value is the recommended process parameters in order to generate maximum net power. To facilitate the operator in using optimization methods, the optimization module is made user friendly by GUI format. GUI is a graphical user interface that facilitates interaction between users in this case the operator to program the application in this case the optimization module of GPP. 3 RESULTS AND ANALYSIS 3.1 Model Validations Kamojang geothermal field produces nearly superheated dry steam. Value of the average reservoir temperature is about 245 C and the average reservoir pressure is approximately 35 barg. In this study, it is assumed that the geothermal fluid is accumulated in the SRS on the verge of saturation or vapor dominated. For the purposes of simulation models, we apply the enthalpy of the geothermal fluid at 2778 kj/kg in order to obtain the quality of steam at For the case of PLTP Kamojang unit 4, in order to produce mass flow rate flowing into the unit geofluid, the production is kg/s (430 tons/hr) or equal to the average value of the measurement of the mass flow rate of geothermal fluid from the SRS, then the selected value m max = 126 kg/s with P ci = 36 bara. Assuming that there is no pressure drop in the lines of SRS - separator, then the value of P can be considered equal to the value of the separator pressure. Based on the measurement data and modeling processes using electricity production with reference to the state of the temperature-entropy diagram, the calculation results is shown in Table 1. In the calculation column, separator and condenser temperature is an input variable while the temperature of cooling water is considered constant at 22 C (a constant equal to the value of dry bulb temperature of the environment). For validation purposes, the same input data with measurement results is used as shown in measurement column. The result of output (gross) turbine-generator is MW. Thus, the deviation of the thermodynamic models to measured data is only 0.85%. While the results of the calculation of the electric power load (lossless transformer) is 1.87 MW to power (net) GPPof MW. This again shows that the deviation of calculation load is only 0.68%. Table 1: Summary of data measurement and calculations Description Measurement Calculation Separator temperature ( C) (input) Condenser temperature ( C) (input) Turbine mass flow rate (kg/s) 119,09 119,60 335

6 Turbine input pressure (barg) 10,1 10,9 Output (gross) turbine-generator (MWe) 63,55 63,01 (output) Cooling water input temperature ( C) 24,7 22 (konstanta) Cooling water mass flow rate (kg/s) Hot water temperature enter the cooling tower ( C) 49,5 49 Based on the validation of Table 1, it can be concluded that the building model is good to represent the energy conversion process on PLTP Kamojang unit 4. Accordingly, the model can be used in the calculation of optimization of electrical power production. Based on the model, there are two parameter candidates which have the opportunity to become the optimization variable in order to produce maximum power at the PLTP Kamojang unit 4. Both candidates parameters are: separator pressure and condenser pressure. 3.2 Simulation of Plant Optimization The calculation of objective function is done based on Eq. (10) by using a value of k = 2, and the optimization variables such as the pressure in the separator and condenser. The results of the objective function for the geofluid enthalpy of 2778 kj/kg is described in Figure 2. It is shown that the maximum value of the objective function is based on 11-bar pressure separator and a condenser pressure of 0.1 bar-a. By using these conditions, the net electrical power output of MW or increase 2.19 MW from electric power calculations using the measurement conditions. If the obtained optimum operating conditions compare to measured operating conditions (Table 1), i.e bar-pressure separator and 0.14 bar-a condenser pressure, it can be concluded that the measured operating conditions in the PLTP Kamojang unit 4 is close to the optimum condition. Figure 2: Objective functions for geofluids entalphy of 2778 kj/kg 336

7 Objective Function 3.3 Results of Optimization Module Three-dimensional image of the objective function with constraints of turbine exhaust steam quality above 0.85 are shown in Figure 3. It is shown that for any constant condenser pressure, the value of the objective function with respect to pressure separator is a convex function that has the optimum value. This is similar to the results of the analysis of gross plant output to variable pressure separator. And for every constant pressure separator, the value of the objective function with respect to pressure is also a convex function that has the optimum value. This is in contrast with the results of the analysis of gross plant output to variable condenser pressure. It can be concluded that the objective function is a convex function that has a maximum value at the optimum point. Furthermore, three-dimensional contour in Figure 3 is a feasible set for this optimization problem. The maximum value is related to the feasible set. And the search results of the maximum objective function is which is at 12.5 bar-a separator pressure and 0.12 bar-a condenser pressure. The optimum operating conditions produce a net power output of 62.7 MW and an auxiliary power of 2.2 MW. Comparison of the optimum operating conditions are obtained by the program with the operating conditions when the measurements are taken. Results show that small differences in pressure separator (which is about 1.5 bar-a or 13.7% of the measurement) and the condenser pressure (at 0.02 bar-a or 14.3% of the measurement). Thus, it can be concluded that the operating conditions in PLTP Kamojang unit 4 are close to the optimum condition Condenser Pressure (bar-a) Separator Pressure (bar-a) 0.3 Figure 3: Graphic of objective function with constraints of turbine exhaust quality 337

8 Figure 4: Main menu display of the optimization module The optimization module of single flash power production systems is made by utilizing the features provided by the Matlab GUI. The display of the main menu is shown in Figure 4. There are pictures plant consisting of several components on the main menu: production wells, separator unit (separator and/or demister), power house (turbine and generator), cooling unit end (condenser and cooling tower), and injection wells. If the area of each component is selected, it will display a window that contains the values of the parameters and process operating conditions running in the component after program execution (by pressing the "run"). On the main menu, besides the Exit button to quit the calculation module, there are also four buttons that leads to a new window, namely: Process diagram, that leads to a new window on the process diagram of single flash plant. Simulink, which deliver to the Simulink program window where the plant output calculation is executed. The process parameters, which delivers a new window containing the values of the process parameters which are required in order to calculate the output Optimization, which deliver to the optimization program window for the calculation of the optimum operating conditions and output of the plant at optimum conditions. 4 CONCLUSION Based on the obtained results, it can be concluded that the objective function is (Eqs. (10)) proved able to accommodate two purposes, namely to maximize the production of electric power and to minimize the power load. The value of k = 3 in the objective function is proved to be capable of producing a maximum net power with minimum power auxiliary. The separator and condenser pressure are the established optimization variables and affecting the objective function in the convex-shaped function with a maximum value at the optimum operating conditions. 5 ACKNOWLEDGEMENTS The authors would like to thank the Directorate General of Higher Education, Ministry of Education and Culture, which supports research in this paper. 338

9 REFERENCES Katherin, I., Bambang, L.W. and Suyanto (2013). Determination of the Optimum Operating Condition for Electricity Production Optimization in Geothermal Power Plant Based on Thermodynamic Analysis. 1 st International Conference on Mechanical Engineering ICOME 2013, Sept th, Mataram, Indonesia. Parlindungan, H.H.S. (2004). Optimization of electrical power production process for the Sibayak Geothermal Field, Indonesia. Geothermal Training Programme report number 16, United Nations University, Reykjavík, Iceland Ronald, D. (2008). Geothermal power plants : Principle, applications, case studies and environmental impact, 2 nd ed. Elsevier, Oxford. Roy, B.S. (2006). Thermodynamic analysis of preliminary design of power plant unit I Patuha, West Java, Indonesia. Geothermal Training Programme reports number 7, United Nations University, Reykjavík, Iceland. Roy, B.S. (2009). Modelling and optimization of possible bottoming units for general single flash geothermal power plants. MSc. Thesis. United Nations University, Geothermal Training Programme, Reykjavík, Iceland. 339