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Chapter 6 Table 6.1: General design data adopted for the 100 MWE (nominal) solar tower system consid- ered here, after Refs. [10, 25, 26]. The plant is assumed to be located in Daggett (CA), latitude 34.9o , longitude −116.8o , average direct normal irradiation (annual) 2791 kWh m−2 . ηPB is the power block thermal efficiency, ηREC,th and αREC the receiver thermal efficiency and absorptivity, respectively. ǫavail and ǫrefl refer to the heliostats availability and reflectivity, both values accounting for the average field performance. 115 ηPB [%] 40 94 ηREC,th [%] 88 0.99 ǫrefl [−] 0.90 0.25 f max [−] 1.2 QREC,inc 0.25 f max [−] 1 mPB 1 fmax[−] 1 xTES weather data in the TMY3 format, containing data for various locations with an hourly sampling, are considered in this work. The value of ηopt is evaluated hourly as a function of the solar position but, as shown in Eq. 6.1, it is also dependent on the solar field characteristics (SF). The same is obviously true for the total reflective area ASF. The dimension of the solar field can be better expressed in terms of the solar multiple (SM) value, that is, the ratio of the receiver design thermal output to the power block design thermal input. As the solar field size is increased, there will be a growing number of hours throughout the year whereby the available solar power exceeds the power block design power. In these conditions, the TES system is used to harvest (part of) the exceeding energy, until defocusing (part of) the heliostats migh become necessary. Thus, a techno-economic optimal combination of the solar field size and of the storage capacity has to be determined for the given plant and location [28]. In particular, the solar power harvesting system constituted by the solar field, the tower, and the receiver, is the most capital intensive part of any solar energy project, and its optimization is therefore critical for the minimization of the overall costs [8, 10, 29]. The SM is thus used as the key parameter in the following analysis, and four solar fields characterized by SM = 1.5, 2, 2.5, and 3.5 are designed for the same hypothetical plant, i.e., starting from the specfications reported in Tab. 6.1. Apart for the nominal characteristic indicated by the SM, however, the detailed design of the components involved is necessary in order to define both ASF and the ηopt (t, loc, SF) relation ap- pearing in Eq. 6.1. In the present work, the PTGen program available within SAM [25, 26], and based on the DELSOL3 code [30, 31], is adopted to this end. Solar fields with a surround radially- staggered layout are considered. Even though several other geometries have been proposed in the literature [8], this arrangement is chosen here for the sake of simplicity. The solar field modelling assumptions adopted in this study, together with the resulting designs, are reported in A.1. Summarizing, since all the computations involved by Eq. 6.1 can be carried out off-line once the solar field has been designed, QREC,inc,av is eventually computed as a known, time-varying input for the plant model. Also the price of the produced electricity P depends on known hourly TOD factors, in turn deter- mined by the selected tariff, on the hour of the day, on the day of the week, and on the season, Wel, gross [MWE] αREC [%] ǫavail [−] f min [−] QREC,inc f min [−] mPB x [m] TES,min 154PDF Image | New Concepts FOR Organic Rankine Cycle Power Systems
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