Tuning the focal point of a plasmonic lens by nematic liquid crystal

A theoretical and numerical investigation of tunable plasmonic nano-optic lens on the basis of liquid crystal are proposed as a new method of active modulating the output beam. The focal length can be controlled easily by exposing plasmonic nano-optic lens to constant external electric field. The physical principle of this phenomenon is evaluated from the phase of Fabry-Perot (F-P) resonance in slits and electro-optical effect of liquid crystal. Our numerical simulations reveal that large tuning range of the focal length up to 725 nm can be achieved. The results in this article provide a potential way to realize tunable plasmonic lens, which can be applied as an efficient element in ultrahigh nano-scale integrated photonic circuits for miniaturization and tuning purposes

Hybrid forecast and control chain for operation of flexibility assets in micro-grids

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets

Damage detection via shortest-path network sampling

Large networked systems are constantly exposed to local damages and failures that can alter their functionality. The knowledge of the structure of these systems is, however, often derived through sampling strategies whose effectiveness at damage detection has not been thoroughly investigated so far. Here, we study the performance of shortest-path sampling for damage detection in large-scale networks. We define appropriate metrics to characterize the sampling process before and after the damage, providing statistical estimates for the status of nodes (damaged, not damaged). The proposed methodology is flexible and allows tuning the trade-off between the accuracy of the damage detection and the number of probes used to sample the network. We test and measure the efficiency of our approach considering both synthetic and real networks data. Remarkably, in all of the systems studied, the number of correctly identified damaged nodes exceeds the number of false positives, allowing us to uncover the damage precisely

MODIFIED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR ELECTROMAGNETIC ABSORBER DESIGN

designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade-offs between thickness and reflection coefficient of absorbers. Particle swarm optimization method in comparison with most of optimization algorithms such as genetic algorithms is simple and fast. But the basic form of Multi-objective Particle Swarm Optimization may not obtain the best Pareto. We applied some modifications to make it more efficient in finding optimal Pareto front. Comparison with reported results in previous articles confirms the ability of this algorithm in finding better solutions. 1

Exploiting Blockchain for Smart Charging of Electric Vehicles: A Proof of Stake Algorithm

Innovations in renewable energy production, electric vehicles (EVs) and smart meters are pushing the energy sector towards decentralisation. In this context, determining the potential of distributed ledger technology (DLT) through smart contracts and interoperability between different sections of the grid is an open point. Implementation of blockchain as a DLT into the electricity distribution network with the combination of multiple components, assists decentralisation and the connection of end-users directly to the grid. In this paper, a new method is proposed to charge EVs based on fuzzy logic and distributed ledger using a proof of stake algorithm. In this way, the risk of manipulation and loss of data, which could be detrimental to the energy sector, is reduced. The decentralisation of controlling EV’s charging is achieved using this technique and the data floating inside the network is immutable and protected by the consensus mechanism of blockchain