The effect of renewable energy incorporation on power grid stability and resilience.

Contemporary proliferation of renewable power generation is causing an overhaul in the topology, composition, and dynamics of electrical grids. These low-output, intermittent generators are widely distributed throughout the grid, including at the household level. It is critical for the function of modern power infrastructure to understand how this increasingly distributed layout affects network stability and resilience. This paper uses dynamical models, household power consumption, and photovoltaic generation data to show how these characteristics vary with the level of distribution. It is shown that resilience exhibits daily oscillations as the grid's effective structure and the power demand fluctuate. This can lead to a substantial decrease in grid resilience, explained by periods of highly clustered generator output. Moreover, the addition of batteries, while enabling consumer self-sufficiency, fails to ameliorate these problems. The methodology identifies a grid's susceptibility to disruption resulting from its network structure and modes of operation.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process.

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Improving visibility depth in passive underwater imaging by use of polarization.

Results are presented that demonstrate the effectiveness of using polarization discrimination to improve visibility when imaging in a scattering medium. The study is motivated by the desire to improve visibility depth in turbid environments, such as the sea. Most previous research in this area has concentrated on the active illumination of objects with polarized light. We consider passive or ambient illumination, such as that deriving from sunlight or a cloudy sky. The basis for the improvements in visibility observed is that single scattering by small particles introduces a significant amount of polarization into light at scattering angles near 90 degrees: This light can then be distinguished from light scattered by an object that remains almost completely unpolarized. Results were obtained from a Monte Carlo simulation and from a small-scale experiment in which an object was immersed in a cell filled with polystyrene latex spheres suspended in water. In both cases, the results showed an improvement in contrast and visibility depth for obscuration that was due to Rayleigh particles, but less improvement was obtained for larger scatterers.