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Table 1 Economic inputs for the optimization. Parameter Base value 1.67 106 5.92 105 8.50 05 [35] 5.15 105 [25]a 4.76 105 [25]a the authors believe the former approach would be a closer and more accurate approximation of the real world case. Moreover, the associated costs with transmission lines between the gas tur- bine plants and the city were also ignored since these facilities are normally built in close proximities to the load. 2.2.10. Performance characteristics Table 2 shows the technical data used to characterize the performance of various components in the simulation. The storage efficiency of compressed air storage facilities can be expressed using two parameters: energy ratio and heat rate. Energy ratio indicates the amount of energy (off-peak electricity) that the com- pressor of the plant consumes per unit of energy that the expander generates during the peak hours. Heat rate expresses the amount of fuel burned per unit of peak electricity generated by the expan- der, similar to conventional gas turbines. The values used for heat rate and energy ratio are for a typical CAES facility as reported Elec- tric Power Research Institute (EPRI) [13]. It is of note that these parameters may vary based on the design and operation of the storage facility in real world. Heat rate of compressed air storage facilities is much lower compared to SCGT and CCGT since all the energy generated by the expander is converted to electricity in a CAES plant while up to two thirds of this energy is consumed by the compressor in conventional gas turbines. It was assumed that up to 70% of the input energy into the compressor of the DCAES plant could be recovered and utilized to satisfy the heat load. The authors acknowledge that this value might change the capital cost of the HRU; the higher this value, the more sophisticated HRU required and the higher the capital cost. Therefore, a conservative value of 70% was used as the heat recovery efficiency of the HRU. In addition, the economics of DCAES might be enhanced if a thermal energy storage facility is utilized to store the excess waste heat for later use; however, this opportunity is not considered in this study for the sake of simplic- ity. A value of 80% (higher heating value) was used for the thermal efficiency of the boilers of the district energy system. 2.2.11. Optimization model A mixed integer linear optimization code was developed in MATLAB to minimize the levelized cost of satisfying the hourly electric and heat loads over the one year simulation period. Both the size and dispatch of the various system components (wind farm, compressor, expander, cavern, SCGT, CCGT, compressed air pipeline, HRU, and HOB) were optimized to minimize the value of the objective function shown in Eq. (3). This equation shows the total cost to satisfy the annual electric and heat loads at an hourly resolution. The terms in the first set of curly brackets show the levelized capital cost of various components of the system while the terms in the second set of curly brackets show the sum- mation of the associated hourly fuel cost. Only the savings in fuel cost of the boiler as a result of the operation of the heat recovery is included in the objective function. At each effective fuel price, the optimization code finds the optimal size for various compo- nents and their optimal dispatch strategy to satisfy the electric and heat load at the minimal cost over the one year period of Table 2 Performance characteristics of various components of the CAES and DCAES systems. a b The base case for the cavern represents a depleted natural gas reservoir capable of storing enough air for 12 h of continuous electricity generation at a rate of 131 MW, as estimated by Electric Power Research Institute [36]. c The estimates for capital cost of the pipelines are based on a regression model developed by Sean McCoy [29] for 263 on-shore natural gas pipeline projects in US. The values used in this study are for 25, 50, and 100 km pipelines in the Central Region of the United States, as classified by the US Energy Information Adminis- tration for natural gas pipeline regions. a smaller boiler would be required to meet the heat load. Conse- quently, the value used to calculate this savings in the capital cost of the boiler was chosen to reflect this effect (marginal capital cost). In addition, all fixed and variable operating costs were ig- nored because of the fact that at the high carbon prices which would be necessary to justify building capital intensive CAES and DCAES plants, these operating costs would be negligible compared to the much higher fuel costs.6 Therefore, only the capital and asso- ciated fuel costs of various system components were considered in the economic analysis. Another simplification in this study is the capital cost of the transmission lines. These lines are required to transmit electricity from the wind farm to the electric load and to the compression train, and from the expansion train to the electric load. Since our scope was to only model electric load of a portion of a city instead of an entire city, it was rational to assume that large transmission lines would already be built between the wind farm, municipal re- gion, and the geological formation to integrate the valuable wind energy into the electric grid for the entire city. Therefore, it is assumed that these lines would exist anyway regardless of the spe- cific CAES and DCAES facilities modeled. The authors acknowledge the possible inaccuracies associated with this intentional simplifi- cation. The alternative to this approach was considering the capital cost of these transmission lines in the economic analysis; however, 6 As a case in point, the variable and fixed operating and maintenance costs (VOM and FOM) for a combined cycle gas turbine plant is $1.3/MWh and $10.8/kW year as reported by Greenblatt et al. [23]. Assuming a capacity factor of 80%, heat rate of 7170 kJ/kWh and a natural gas price of $5.0/GJ, the levelized VOM, FOM and fuel costs of this plant would be $1.30, $1.54 and $35.85 per MWh of electricity generated, respectively. The VOM and FOM costs of the plant would not vary with market price of natural gas while the fuel cost would. Therefore, the authors do not expect excluding the VOM and FOM costs would introduce major inaccuracies in the results of the simulations performed, especially at high fuel prices or associated emission taxes required for economic superiority of CAES and DCAES plants over conventional gas turbines. The estimates for the specific capital cost of compressor and expander are based on the capital cost of the McIntosh CAES plant estimated by the Electric Power Research Institute (EPRI) [25]. This cost was adjusted for the cost of the salt cavern [13] and the cost ratio of expander and compressor to estimate the costs of expander and compressor separately. The ratio of the specific cost of expander to compressor is assumed 1.08, average of the values used by Fertig and Apt [12] and Greenblatt et al. [23]. Negligible 10 41,457D 1,449,340 Negligible Negligible [23] [29]c H. Safaei et al. / Applied Energy 103 (2013) 165–179 171 Reference CapExWind ($/MW) CapExSCGT ($/MW) CapExCCGT ($/MW) CapExExp ($/MW) CapExComp ($/MW) CapExMarginal ($/MWh) Cav CapExBase ($)b Cav CapExMarginal ($/MW) HOB CapExHRU ($/MW) CCR (%) CapExPipe ($) (L = 50 km and 250 < D < 700 mm) VOM cost of all components FOM cost of all components [35] [35] 1.5 102 6 [13] 12.22 10 [36] 4 5.010 [37] Parameter HRCCGT (GJ/MWh) HRSCGT (GJ/MWh) HRExp (GJ/MWh) ER Value Reference 7.17 [35] 11.02 [35] 4.19 [13] 0.75 [13] gHOB 80% gHOB 70%PDF Image | 20 kW ORC Turbine Off-Design Performance Analysis
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