Silvia Canale

Optimal Planning and Routing in Medium Voltage PowerLine Communications Networks

This paper deals with the problem of deploying a PowerLine Communication (PLC) network over a medium voltage (MV) power grid. The PLC network is used to connect the end nodes (ENs) of the MV grid to the service provider by means of PLC network nodes enabled as access points. In particular, a network planning problem is faced wherein we require to define the PLC network topology by deciding which MV network nodes are to be enabled as access points. An optimization problem is then formulated, which minimizes the cost of enabling the access points and maximizes the reliability of PLC network paths in a multi-objective optimization fashion. This work also considers resiliency (i.e., it guarantees the PLC network connectivity even in case of link faults) and capacity constraints (i.e., it checks that there are enough resources to transmit the estimated amount of traffic over the PLC network paths). As a byproduct, the optimization algorithm also returns the optimal routing. Simulations based on realistic MV network topologies validate the proposed approach.

Resilient planning of PowerLine Communications networks over Medium Voltage distribution grids

In this paper a network planning problem aiming to enable underground Medium Voltage (MV) power grids to resilient PowerLine Communications (PLCs) is faced. The PLC network is used to connect PLC End Nodes (ENs) located into the secondary substations to the energy management system of the utility by means of PLC network nodes enabled as Access Points. An optimization problem is formulated, aiming to optimally allocate the Access Points to the substations and the repeaters to the MV feeders. A multi-objective optimization approach is used, in order to keep in balance the needs of minimizing the cost of equipment allocation and maximizing the reliability of PLC network paths. Resiliency and capacity constraints are properly modeled, in order to guarantee the communications even under faulted link conditions. As a byproduct, the optimization algorithm also returns the optimal routing. Simulations performed on a realistic underground MV distribution grid validate the proposed approach.

Optimal electric vehicles to grid power control for active demand services in distribution grids

In this paper we outline a novel approach for the design of an electric vehicle (EV) aggregator, a controller whose objective is to optimally manage the charging operations of an EV fleet. The control strategy we derive is based on model predictive control and allows to achieve costs minimization, also enabling the aggregator (hence, the EV fleet) to participate to the provisioning of active demand services to upper level market players. Explicative simulations are presented and discussed in order to show the effectiveness of the approach and also to investigate the role of vehicle to grid power.

Electric vehicle trip planning integrating range constraints and charging facilities

This paper presents a strategy for multi-modal trip planning integrating the management of fully electric vehicle range and charging services along the trip. The network graph is modelled as the superposition of layers representing different transportation means and charging infrastructure, putting in evidence the interaction between the transportation and electricity distribution grids. The presence of energy constraints on the network nodes implies to formalize the trip planning problem as a resource constrained shortest path problem, and solve it through an ad-hoc decomposition strategy. The proposed approach is validated through the simulation of realistic test cases, showing its effectiveness and potential in satisfying complex user preferences, mitigating drivers perception about limited vehicle range and availability of charging infrastructure, smoothing the impact of massive fully electric vehicle charging on distribution grids.

A Future Internet oriented user centric extended intelligent transportation system

Intelligent Transportation Systems (ITS) are changing the way people plan a journey and travel around the world. Advanced mobility information systems, as well as intelligent multimodal mobility services, may take considerable advantage of consolidated technologies from emerging ICT frameworks. In this paper we propose an Extended Intelligent Transportation System (ExITS) consisting of a basic ITS equipped with a User Centric Control System (UCCS). The proposed ExITS relies on service personalization methodologies and is conceived as a Future Internet (FI) oriented, closed-loop, user centric architecture integrating and controlling ITS services. The proposed UCCS considers the trip planning service and takes into account both explicit and implicit user preferences in selecting travel solutions satisfying a given user request. The aim of the UCCS is to drive the trip planning service in proposing to the user travel typologies tailored to personal preferences. Implicit preferences are automatically inducted by similarity based unsupervised machine learning techniques and verified by a closed-loop control mechanism triggered by explicit user feedback.

Segmentation Based Pattern Recognition for Peri-Urban Areas in X Band SAR Images

In this paper Synthetic Aperture Radar (SAR) images in X-band were analyzed in order to infer ground properties from data. The aim was to classify different zones in peri-urban forestries integrating information from different sources. In particular the X band is sensitive to the moisture content of the ground that can be therefore put into relation with the gray level of the image; moreover, the gray level is related to the smoothness and roughness of the ground. An integration of image segmentation and machine learning methods is studied to classify different zones of peri-urban forestries, such as trees canopies, lawns, water pounds, roads, etc., directly from the gray level signal properties. As case study the X-SAR data of a forest near Rome, the Castel Fusano area, are analyzed.

X-SAR SpotLigh images feature selection and water segmentation

In this paper we address the feature selection problem for X-SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X-SAR images we select the most significant ones via a supervised machine learning approach: the 1-norm SVM. The selected features will be used for segmentation purposes, in order to segment water areas from the background. We shall see that the most relevant features are based on texture elements. So the segmentation is texture based and achieved with variational calculus and level set methods. The work is mainly focused on urban park X-SAR SpotLight images, where lakes and rivers are often present. The images are collected with the COSMO-SkyMed satellites constellation, equipped with a SAR sensor.

Integrating X-SAR images and anthropic factors for fire susceptibility assessment

In this paper we present an integrated approach to COSMO-SkyMed image analysis and classification exploiting integration of different data of the regions of interest, namely urban forestry areas, wide urban parks. The aim is to provide a methodology for exploiting complex data structures built upon multi resolution grids gathering together with optical and X-SAR images, also historical land exploitation and meteorological data, records of human habits, and several other information sources. Although these data are specifically gathered to built a fire susceptibility map, the method is quite general. Indeed, the contribution of the model and its novelty relies manly on the definition of a learning schema lifting different factors and aspects of the event to be identified (here fire causes), including physical, social and behavioral ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way.