0191 -9067/81 /030305-02$02.00/0 Copyright ° 1981 SUNS AT Energy Council A TECHNICAL NOTE ON THE SPS ENERGY ANALYSIS OF HERENDEEN ET AL. ROBERT C. SPEAR Department of Biomedical and Environmental Health Sciences University of California Berkeley, California 94720 GEORGE M. HORNBERGER Department of Environmental Sciences University of Virginia Charlottesville, Virginia 22903 In an editorial in Science (1) on the Solar Power Satellite (SPS). Grey commented that at present there were but two rational questions: “(i) Does the concept have sufficient future promise to warrant finding out whether it is practical and (ii) if so, what type of research and how much of it should be done?” Recently, Herendeen et al. (2) reported the results of an energy analysis of the SPS which basically addressed Grey’s first question. Fortheir principal measure of promise, Herendeen et al. relied on the energy ratio determined by dividing the projected energy output of the system by the projected energy requirements for its construction and operation. Their analysis explicitly considered the effects of uncertainty in the parameters of their model and they utilized Monte Carlo simulation to determine the distribution of the energy ratio resulting from this parametric uncertainty. We have carried Herendeen’s analysis one further step which allows us to give a more refined answer to Grey’s first question and to comment on Grey’s second question. In particular, we asked which of Herendeen’s eighteen parameters seem to be most important to the future promise of the SPS, as measured by the energy ratio, and what are the resulting implications regarding research or design priorities. Basic to our analysis is the assumption that the SPS must have an energy ratio greater than 2 in order to be an attractive future alternative. Then, using the equations and parameter distributions of Herendeen et al. (3), we carried out 700 Monte Carlo trials and classified each set of eighteen parameters as to whether it led to success (ER^2) or failure (ER<2). The degree of separation of the parent cumulative distribution, F(E) of a parameter E is used as a measure of sensitivity of the ER to that parameter (4). That is, if Fl^) =F(^S) = FILES'), where S denotes success and S failure, then success or failure are insensitive to G over its range of variation defined by FUEL (This is a sufficient but not necessary condition. Examples can be constructed in which the univariate distributions do not separate but in which there is induced covariance between two parameters which turns out to be important to success or failure. No such interactions occurred in the present case.) We employed the statistic dmM=Sn(E'A) -Sm(E\S) to measure the separability under the sue-
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