Abstract

Abstract
Integrating wind and solar energy resources poses intermittency to power systems, which faces independent system operators with new technical and economic challenges. This study proposes a novel model to integrate the uncertainties of wind power on the supply side and roof-top solar photovoltaic (PV) on the demand side. To cope with their uncertainties, a demand response (DR) aggregator is proposed, which is enabled to participate in reserve markets. To this end, a new DR model is developed considering both customers' options to reduce and increase load through the DR aggregator. As such, besides improving the existing DR models (load shifting and curtailment), two DR programmes, i.e. load growth and load recovery, are mathematically modelled. Numerical studies indicate the effectiveness of the proposed model to reduce the total operation cost of the system and facilitate the integration of wind power and roof-top PV.

Abstract
Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios.

Abstract
The increase penetration of renewable energy sources (RES) into the European power system has introduced a significant amount of variability and uncertainty in the generation profiles raising the needs for ancillary services as well as other tools like demand response, improved generation forecasting techniques and changes to the market design. While RES is able to replace energy produced by the traditional centralized generation, it cannot displace its capacity in terms of ancillary services provided. Therefore, centralized generation capacity must be retained to perform this function leading to over-capacity issues and underutilisation of the assets. Large-scale reversible hydro power plants represent the majority of the storage solution installed in the power system. This technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in some specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to FRR, given their ability to control its operating point in pumping mode. This work does an extended analysis of a complete bidding strategy for Pumped Storage Power, enhancing the economic advantages of variable speed pump units in comparison with fixed ones.

Abstract

Abstract
The capability of an Autonomous Underwater Vehicle (AUV) or Remote Operated Vehicles (ROVs) to communicate with underwater sensor nodes or a docking station, for the exchange or transfer of data gathered during a survey mission, requires an high-speed short-range communication link. This is important because of the global attention to the underwater communication applications. To this end, underwater antennas will play a significant role in ensuring good data rates and propagation distances for these applications. In this paper, the performance of three antennas, specifically, loop, dipole and J-pole is assessed through simulation for usage in fresh and sea water operating in the High Frequency (HF) band. The antennas were designed in FEKO, an electromagnetic simulation software and their performance is assessed in terms of bandwidth and directivity. The results obtained shows that the J-pole antenna has significant advantages in term of the measured parameters over the other antennas. Experimental results of the reflection coefficient of the J-pole antenna in fresh water are given that agree well with the simulation results. © 2017 IEEE.