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120 Table 6.3 Relative solution times for each numerical model Model Infinite-NTU Implicit Relative Time 1 2.9 Numerical Model Performance: Variable Input Simulation Cyclic steady-state Second-Law efficiency computing with constant forcing functions is a consistent means for evaluating numerical model performance. It does not, however, accurately reflect the conditions under which the numerical models will be used in predicting the performance of storage systems used in solar-thermal power systems. Variation in solar radiation levels, power cycle performance and storage system operating strategies will result in varying storage system inputs. In this section model accuracy is quantified in the context of a “typical” long-term simulation using weather data as the forcing function. For the cyclic steady-state case, the error incurred by the infinite-NTU and implicit models was less than 3% for numerical meshes as coarse as 100 nodes. To better evaluate storage model performance under variable conditions, the infinite-NTU and implicit numerical models were tested for 30 day operating periods in TRNSYS with a full charge-discharge cycle occurring diurnally with varying numbers of nodes. The charge times were roughly analogous to those used in the previous cyclic steady-state tests. The solar field model, developed by Patnode [2006], was set to deliver fluid at constant temperature and variable flow rate throughout the day using weather data for July in Daggett, CA as input. During hours with sufficient solar radiation for the solar field to achieve the specified outlet temperature (400oC), the storage tank was charged. During hours with insufficient solar radiation for the solar field to operate, the storage system discharged until empty to a model of the SEGS VI steam Rankine power cycle [Patnode, 2006]. These tests were again performed with each model for two cases, a “large-NTU” (NTU~500) tank and a “small-NTU” (NTU~100) tank.PDF Image | Organic Rankine Cycle Solar-Thermal Powerplants
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