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New laws offer nuclear industry incentives for existing power plant uprates
This year, the U.S. nuclear industry received a much-needed economic boost that could help preserve operating nuclear power plants and incentivize upgrades that extend their lifespan and power output.
Signed into law in 2022, the Inflation Reduction Act offers production tax credits (PTCs) for existing nuclear power plants and either PTCs or investment tax credits (ITCs) for new carbon-free generation. These credits could make power uprates—increasing the maximum power level at which a commercial plant may operate—a much more appealing option for utilities.
Iván Lux
Nuclear Science and Engineering | Volume 67 | Number 1 | July 1978 | Pages 107-119
Technical Paper | doi.org/10.13182/NSE78-A27241
Articles are hosted by Taylor and Francis Online.
A unified definition of a wide class of Monte Carlo reaction rate estimators is presented, since most commonly used estimators belong to that class. The definition is given through an integral transformation of an arbitrary estimator of the class. Since the transformation contains an arbitrary function, in principle an infinite number of new estimators can be defined on the basis of one known estimator. It is shown that the most common estimators belonging to the class, such as the track-length and expectation estimators, are special cases of transformation, corresponding to the simplest transformation kernels when transforming the usual collision estimator. A pair of new estimators is defined and their variances are compared to the variance of the expectation estimator. One of the new estimators, called the trexpectation estimator, seems to be appropriate for flux-integral estimation in moderator regions. The other one, which uses an intermediate estimation of the final result and is therefore called the self-improving estimator, always yields a lower variance than the expectation estimator. As is shown, this estimator approximates well to possibly the best estimator of the class. Numerical results are presented for the simplest geometries, and these results indicate that for absorbers that are not too strong, in practical cases the standard deviation of the self-improving estimator is less than that of the expectation estimator by more than 10%. The experiments also suggest that the self-improving estimator is always superior to the track-length estimator as well, i.e., that it is the best of all known estimators belonging to the class. In the Appendices, for simplified cases, approximate conditions are given for which the trexpectation and track-length estimators show a higher efficiency than the expectation estimator.