Demand response (DR) is an important factor contributing to achieve a balance between energy production and demand in Smart Grids. DR plays a key role in the use of residential energy allowing to improve the load management, electrical grid reliability, to reduce energy demand during peak hours and to minimize the use of energy in face of increasing energy prices. This article proposes a Model Predictive Control (MPC) strategy to manage the energy resources of a residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, the presented approach encompasses degradation issues of the Energy Storage System (ESS), the cost of the electricity, renewable energy generation, and other operational constraints. The developed control strategy is able to maximize microgrid economical benefit, while minimizing the degradation of the ESS, reducing electricity consumption during the day, and fulfilling the different operational constraints. The proposed strategy is validated in an experimental renewable-energy based microgrid platform for different climatic conditions. The obtained results demonstrate and verify the effectiveness of the proposed control and management strategy.

This work shows prospects of long-period fibre grating applications as transducers for fuel conformity analysis. The proposed long-period grating transducer was employed to assess the gasoline conformity in commercial gas stations. Grating responses were used to train and validate a radial base function topology of an artificial neural network. The obtained results show that fibre optic sensors supervised by artificial neural networks can integrate systems for smart sensing with high applicability in the petrochemical field. The radial base function had reached a correct classification probability of approximately 94%. The device applicability in the analysis of hydrated ethanol fuel was also investigated by measuring the concentration of ethanol in ethanol–water mixtures. The results showed that the developed transducer can be used to infer the ethanol–water concentration with a resolution of up to 0.23%.