Presentation on theme: "9. NUMERICAL EXAMPLES METHODS OF ENERGY SYSTEMS OPTIMIZATION"— Presentation transcript:
1 9. NUMERICAL EXAMPLES METHODS OF ENERGY SYSTEMS OPTIMIZATION OPTI_ENERGYSummer School: Optimization of Energy Systems and ProcessesGliwice, 24 – 27 June 2003METHODS OF ENERGY SYSTEMS OPTIMIZATION9. NUMERICAL EXAMPLES9.1 Thermoeconomic Operation Optimization of a System
2 9.1.1 Description of the system A combined cycle cogeneration system that covers the needsof a refinery in electricity and steam.Two-way interconnection with the utility grid.Main components:Two gas-turbine electricity generators of 17 MWe each.Two exhaust-gas boilers recovering heat from the gas turbine flue gases.One steam-turbine electricity generator of 16 MWe.Two steam boilers of 60 ton/h each.Two steam boilers of 30 ton/h each.
3 Fig. 9.1.1. Simplified diagram of the combined-cycle cogeneration system.
4 Table 9.1.1. Steam grades used in the refinery. Description of the systemTable Steam grades used in the refinery.
5 9.1.2 Primary energy sources Electricity supply from the utility grid.Fuel gas (FG):A by-product of the refinery process.The largest primary energy source.It consists of light hydrocarbons (methane to butane) anda small percentage of hydrogen (about 5% by volume).It is available at low pressure (LPFG) and high pressure (HPFG).It cannot be stored. If not used, it is burned in the flares.(continued)
6 9.1.2 Primary energy sources (continued)Fuel oil (FO).Commercial industrial grade fuel oil (900 kg/m3, 370 cSt at 50°C max)of low sulfur content (0.7% by weight, maximum).The second largest primary energy source for the refinery.Propane.A sellable final product.Its use as a fuel in the refinery depends on propane storage availability and its selling price.There is actually a trade-off between FO and propane, and the use of one or the other depends on their selling price.
7 Energy conversionThe various fuels are converted to heat, steam and electricity.Process heat needs are covered by fired heaters using FG and/or FO or by steam.Steam is produced by steam boilers, and by waste heat boilers in the process units as well as in the cogeneration system.Four grades of steam are produced. If the quantity of steam directly produced at a certain grade is not sufficient, then it is supplemented by desuperheating, which causes an exergy destruction and consequently must be avoided whenever possible.
8 9.1.4 The need for operation optimization The energy needs of the refinery can be satisfied by several primary energy sources through various energy conversion systems.Important considerations:Electricity can be produced (within certain limits) either by the gas turbines or by the steam-turbine generator. The optimum load distribution is requested.Gas-turbine generators produce electricity and steam simultaneously. Thus, increased gas turbine level of electricity production results in an increase of steam availability, reducing the required production of steam by the steam boilers.Increasing the level of electricity production by the steam-turbine generator results in reduced steam availability, thus increasing the required production of steam boilers.(continued)
9 9.1.4 The need for operation optimization Important considerations (continued):Electricity can be exported to the utility grid. The quantity of the exported electricity affects the operation of the gas turbines, steam turbine and boilers.Production and consumption of the various steam grades must be kept in balance to avoid degrading steam of higher levels to lower levels at a loss (i.e. without production of mechanical work).A heuristic approach or past experience only is not capable of determining the optimum mode of operation.The application of an optimization procedure is necessary.
10 9.1.5 The Optimization objective Minimization of the capital and operating cost at any instant of time:(9.1.1)(9.1.2)Inequality constraints on the independent variables:(9.1.3)
11 9.1.5 The Optimization objective (continued)Net electric power produced by the cogeneration system:(9.1.4)Total electric power supplied by the cogeneration systemand the utility grid:(9.1.5)An analysis and simulation of the system including mathematical simulation of the main components and important auxiliary equipment has been performed.
12 9.1.6 Considerations on capital and operation expenses The introduction of capital depreciation, maintenance and personnel costs in the objective function has an impact on the optimum point only if these costs can be expressed as functions of independent variables.The available information led to the following.Four main subsystems are considered:1: fuel-oil boilers,2: steam-turbine generator,3: gas-turbine generator No. 1 with exhaust boiler,4: gas-turbine generator No. 2 with exhaust boiler.(continued)
13 9.1.6 Considerations on capital and operation expenses (continued)Capital cost:(9.1.6)Maintenance and personnel costs:(9.1.7)where(9.1.8)
14 9.1.7 Description of the computer program The direct application of a mathematical programming algorithmhas been used.The computer program consists of the following parts:Main programOptimization algorithm GRG2Constraints subroutine GCOMPObjective function FZComponent simulation packageFile DSTEAM
15 Results for typical load conditions Numerical resultsResults for typical load conditionsUsual practice (example):Optimum mode of operation (for the same load conditions):
16 Example of Sensitivity Analysis Fig Effect of unit cost of electricity purchased from the gridon the optimum operating point.
17 Example of Sensitivity Analysis Fig Effect of unit cost of fuel oil on the optimum operating point.
18 9.1.9 Conclusions on the example The application of an optimization procedure to a complex system is very beneficial: if the common practice is replaced by the optimization procedure, a very significant reduction in operating expenses can be achieved with no need of additional investment.The simplifying assumptions leave much room for further development and improvement of the procedure and the software.In a further development, the limits of the system under optimization may be extended to include the refinery processes.Off-line optimization has been applied, which is satisfactory when the plant operates at nearly constant conditions for relatively long periods of time. For frequent changes of conditions however, on-line optimization is necessary.On-line optimization requires fast simulation and optimization software.
19 9. NUMERICAL EXAMPLES METHODS OF ENERGY SYSTEMS OPTIMIZATION 9.2 Thermoeconomic Design Optimization of a System
20 9.2.1 Description of the system and main assumptions The system consists of a gas-turbine unit with regenerative air preheater,and a heat recovery steam generator (HRSG).Main Assumptions:a. The air and combustion gases behave as ideal gases with constant specific heats.b. For combustion calculations, the fuel is considered as methane.c. All components, except the combustion chamber, are adiabatic.d. Pressure and temperature losses in the ducts connecting the components are neglected. However, a pressure drop due to friction is taken into consideration in the air preheater (both streams), combustion chamber and the HRSG.e. Mechanical losses in the compressor and turbine are negligible.
21 Fig. 9.2.1. Flow diagram of the gas-turbine cogeneration system.
22 Table 9.2.1. Thermodynamic parameters for the system. Net shaft power:Steam flow rate:Steam condition:p9 = 20 bar, saturatedFeedwater conditions:p8 = 20 bar T8 = 25°CTemperature difference:Fuel properties (CH4)Molar mass:Mf = kg/kmolLower heating value:Hu = kJ/kgSpecific chemical exergy:Conditions at the combustor inlet:T10 = 25°CReference environment:p0 = bar T0 = 25°C(continued)
23 Table 9.2.1. Thermodynamic parameters for the system. (continued)Other pressures and temperaturesp1 = barT1 = 25°Cp7 = barT7min = 25°COverall heat transfer coefficient in the air preheater: U = kW/m2KProperties of air and exhaust gas for compression and expansion calculations (ideal gas model):cpa = kJ/kg·KRa = kJ/kg·Kcpg = kJ/kg·KRg = kJ/kg·KEfficiency of the combustor : (i.e. thermal losses 2%)Exit/inlet pressure ratios in components due to frictionAir preheater – air side:rAa = 0.95Air preheater – exhaust gas side:rAg = 0.97Combustor and HRSG:rB = rR = 0.95
24 9.2.2 Preliminary Calculations Steam temperature:T9 = Tsat(20 bar) = °CPreheated water temperature:Useful heat rate (product of the system):Useful heat rate of the economizer:Useful heat rate of the evaporator:
25 9.2.3 Thermodynamic Model of the System It consists of 21 equations including 47 quantities(pressures, temperatures, mass flow rates, heat transfer area, etc.).Examples:
26 9.2.3 Thermodynamic Model of the System (continued)Quantities involved:Parameters given or already calculated: 21Number of equations available:Number of unknown quantities(independent variables): 5Selected independent variables:
27 Installed capital cost functions of components Economic model of the systemInstalled capital cost functions of componentsCompressor:Air preheater:Combustor:Turbine:HRSG:
28 9.2.4 Economic model of the system Annualized capital cost of a componentincluding depreciation and maintenance:(9.2.4)Total annual cost of the system:(9.2.5)whereCr installed capital cost of component r,FCR annual fixed charge rate,maintenance factor,cf cost of fuel per unit of energy,t time period of operation during a year.
29 Fig. 9.2.2. Functional diagram of the system. Thermoeconomic Functional Analysis of the systemFig Functional diagram of the system.
30 Functions (products) of the units Compressor:Air preheater:Combustor:Turbine:HRSG:Junction:
31 Additional functionsFunction from the environment:Functions to the environment:Distribution of mechanical exergy(due to pressure difference from the environment):Shaft power from the turbine to the compressor:
32 Additional functions(continued)Thermal exergy due to temperature increasein the compressor:Thermal exergy from exhaust gases:Product of the air preheater given to the junction:Combustion function given to the junction:Thermal exergy from the junction to the turbine:Thermal exergy from the junction to the HRSG:
33 9.2.5 Thermoeconomic Functional Analysis of the system (continued)Cost balance for each unit considering a break-even operation(physical or monetary costs):(6.2.27)The system of equations is solved for the unit product costs, cn.The costs are distributed to the units and to the final productsby the function distribution network.
34 9.2.6 Statement of the optimization problem Optimization objective function(minimization of the total cost rate of the system):(9.2.28)Equality constraints: the thermodynamic and economic model of the system.Inequality constraints:(9.2.29)
35 Basic procedure for solution of the optimization problem by the Functional Approach1. Select an initial set of values for x.2. Determine the values of y by the system of equality constraints.3. Evaluate the Lagrange multipliers.4. Check the necessary conditions. If they are satisfied to an acceptable degree of approximation, then stop. Otherwise, select a new set of values for x and repeat steps 2-4.
36 9.2.7 Application of the modular approach Module 1: CompressorParameters and variables:Simulation model: Eqs. (A.1), (A.2), Appendix A in the text.Module 2: Combustor and turbineParameters and variables:Simulation model:Eqs. (A.7) – (A.9) and (A.11) – (A.13).
37 Module 4: Heat recovery steam generator Application of the modular approachModule 3: Air preheaterParameters and variables:Simulation model: Eqs. (A.10), (A.18) (A.19).Module 4: Heat recovery steam generatorParameters and variables:Simulation model: Eqs. (A.14), (A.15) (A.20), (A.21).
38 Numerical resultsTable Optimization results for the nominal set of parameter values.
39 Table 9.2.3. TFA: values of functions at the optimum point (in kW). Numerical resultsTable TFA: values of functions at the optimum point (in kW).Table TFA: values of Lagrange multipliers and unit product costsat the optimum point (in $/106 kJ).
40 Sensitivity analysisTable Sensitivity of the optimal solution to the fuel price and capital cost.
41 Sensitivity analysisTable Sensitivity of the objective function to the independent variables:, %.
42 compressor pressure ratio. Sensitivity analysisFig a. Effect of fuel price and capital cost on the optimum value ofcompressor pressure ratio.
43 compressor isentropic efficiency. Sensitivity analysisFig b. Effect of fuel price and capital cost on the optimum value ofcompressor isentropic efficiency.
44 preheated air temperature. Sensitivity analysisFig c. Effect of fuel price and capital cost on the optimum value ofpreheated air temperature.
45 the objective function. Sensitivity analysisFig c. Effect of fuel price and capital cost on the optimum value ofthe objective function.
46 9.2.10 General comments derived from the example The application of three methods for the optimization of thermal systems has been demonstrated through this example. All three approaches have been successful in the particular application.The direct use of an optimization algorithm is the simplest way, because it requires the least effort in system analysis, but it gives no information about the internal economy of the system (physical and economic relationships among the components).Scaling of the variables and of the objective function is usually required in order to achieve convergence to the optimum point.Since no method can guarantee convergence to the global optimum, there is need to start the search from different initial points. If the same final point is reached, then we are more or less confident that this is the true optimum.
47 9. NUMERICAL EXAMPLES METHODS OF ENERGY SYSTEMS OPTIMIZATION 9.3 Environomic Analysis and Optimization of a System
48 9.3.1 Description of the system and main assumptions Main Characteristics of the SystemFuel oil is considered in this example, because it is more polluting than the natural gas.The system produces a specified amount of electric power.The system is equipped with a flue gas desulfurization (FGD) unit for SO2 abatement. Its operation requires electricity, water and limestone.The size and the capital cost of the FGD unit depend largely on the exhaust gas flow rate. Therefore, it is less expensive to desulfurize a partial flow at the maximum possible degree than the total flow at a lower degree.
49 Fig. 9.3.1. Gas-turbine system with flue gas desulfurization unit.
50 9.3.1 Description of the system and main assumptions Mass and volume flow ratesthrough the FGD unit:(9.3.1)Degree of SO2 abatement:(9.3.2)wheredesirable degree of SO2 abatement,mass, volume flow rate of exhaust gases through the FGD unit,total mass, volume flow rate of exhaust gases,initial mass flow rate of SO2 :(9.3.3)final mass flow rate of SO2 (after abatement).
51 Two thermodynamic objectives Statement of the optimization problemsTwo thermodynamic objectivesMaximization of the cycle efficiency:(9.3.4)Maximization of the net power density, defined as:(9.3.5)where(9.3.6)Independent variable:(9.3.7)Comment:and w increase continuously withand
52 9.3.2 Statement of the optimization problems Thermoeconomic objective is the minimization of theannual cost of owning and operating the system:(9.3.8)Independent variables:(9.3.9)Environomic objective:(9.3.10)Independent variables:(9.3.15)
53 9.3.2 Statement of the optimization problems Capital cost of the FGD unit:(9.3.11)Cost of resources for the first year:(9.3.12)(9.3.13)First year penalty for emitted SO2:(9.3.14)
54 Table 9.3.1. Parameter values for optimization of the system. Numerical results and commentsTable Parameter values for optimization of the system.
55 Table 9.3.2. Optimization results. Numerical results and commentsTable Optimization results.VariableO b j e c t i v erC25.8328.110.3910.8215.2216.14*#0.84600.8555rB0.98200.9839T3 (K)1467.41478.60.89470.8993__0.95000.40560.42020.36360.37500.39000.4034* Equal to the thermoeconomic optimum value.# Equal to the environomic optimum value.
56 9.3.3 Numerical results and comments Comments on the resultsThe environomic optimum values of all the independent variables are higher than the thermoeconomic optimum values.The thermoeconomic and environomic optima of rC are in between the values corresponding to the maximum efficiency and the maximum net power density.The cycle efficiency obtains a higher value with the environomic optimization than with the thermoeconomic optimization.