Abstract-This paper introduces distributed algorithms that share the power generation task in an optimized fashion among the several Distributed Energy Resources (DERs) within a microgrid. We borrow certain concepts from communication network theory, namely Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, which are known to be convenient in terms of communication requirements and network efficiency. We adapt the synchronized version of AIMD to minimize a cost utility function of interest in the framework of smart grids. We then implement the AIMD utility optimisation strategies in a realistic power network simulation in Matlab-OpenDSS environment, and we show that the performance is very close to the full-communication centralized case.

The paper proposes a stochastic model to analyse the dynamic coupling of the transmission system, the electricity market and microgrids. The focus is on the impact of microgrids on the transient response of the system and, in particular, on frequency variations. Extensive Monte Carlo simulations are performed on the IEEE 39-bus system, and show that the dynamic response of the transmission system is affected in a non trivial way by both the number and the size of the microgrids

Predictive Maintenance, Prognostics and Reliability Centered Maintenance approach are becoming more and more important in the railway sector to reduce costs of operation and increase reliability and safety. In fact they are fundamental to optimize the maintenance process, defining new measures and algorithms to locate faults, monitor health conditions of subsystems and estimate residual life of components. However there is a tradeoff to identify: new measures and new algorithms imply new sensors and new processing devices, and these items have their own cost and their own reliability; this issue has to be taken into account to evaluate the global benefit. In some cases, however, it's possible to use existing sensors and existing processing hardware to extract new information from already available data. It's clear that this is usually the best option because the benefit can be achieved with little or not cost at all. This paper describes the result of a study performed with the aim of detecting arcing events without the need of additional equipment mounted on board the train. A set of data relative to voltage and current collected by trains in high-speed lines together with a set of measurements coming from photosensors are available. The data are processed by the use of an advanced classification technique, namely Support Vector Machines, with the aim of extracting important information such as the time coordinate related to anomalies in the overhead contact line and the status of the contact strip of the pantograph.

Abstract-This work presents a channel model for the broadband characterization of power lines in presence of time variation of the loads. The model is characterized by taking into account both measured and geometrical channel characteristics and can easily be used to take into account also the presence of noise. The channel is described by a two-port equivalent described by a scattering matrix determined from a wavelet-based expansion of the input and output quantities. Upper and lower bounds for the response of the channel in presence of time-varying loads are determined in a fast and efficient way avoiding time consuming Monte Carlo simulations. The bounds determination allows the estimate of noteworthy quantities for the tuning of currently used modulation schemes for power lines communications such as orthogonal frequency-division multiplexing.

In this paper we present a novel method for daily short-term load forecasting, belonging to the class of “similar shape” algorithms. In the proposed method, a number of parameters are optimally tuned via a multi-objective strategy that minimizes the error and the variance of the error, with the objective of providing a final forecast that is at the same time accurate and reliable. We extensively compare our algorithm with other state-of-the-art methods. In particular, we apply our approach upon publicly available data and show that the same algorithm accurately forecasts the load of countries characterized by different size, different weather conditions, and generally different electrical load profiles, in an unsupervised manner