Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out

Linear and nonlinear optical properties of dewetted SiGe islands

We propose to exploit the natural mechanical instability of thin solid films to form regular patterns of monocrystalline atomically smooth silicon and germanium nanostructures that cannot be realized with conventional methods. The solid-state dewetting dynamics is guided by pre-patterning the sample by a combination of electron-beam lithography and reactive-ion etching, obtaining precise control over number, size, shape, and relative position of the final Si1-xGex structures. Here we describe our progress in the spectroscopic investigation of individual dewetted Si1-xGex nanoislands: in the linear regime, bright Mie-type localized resonances are detected in the visible spectral range, with a spectral position that can be tuned by modifying the size of the nanoparticles. In the non-linear regime, instead, sizable third-harmonic generation is observed at the level of single islands. We believe that these results will be pivotal to a novel approach in spectral filtering, sensing and structural color with all-dielectric photonic devices

Realization of electron vortices with large orbital angular momentum using miniature holograms fabricated by electron beam lithography

Free electron beams that carry high values of orbital angular momentum (OAM) possess large magnetic moments along the propagation direction. This makes them an ideal probe for measuring the electronic and magnetic properties of materials, as well as for fundamental experiments in magnetism. However, their generation requires the use of complex diffractive elements, which usually take the form of nano-fabricated holograms. Here, we show how the limitations of the current fabrication of such holograms can be overcome by using electron beam lithography. We demonstrate experimentally the realization of an electron vortex beam with the largest OAM value that has yet been reported to the first diffraction order (L = 1000 ℏ), paving the way for even more demanding demonstrations and applications of electron beam shaping

Functional modeling for green biomass supply chains

The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim® simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance