Emanuele Crisostomi

A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies

Motivated by the problems of charging a number of electric vehicles via limited capacity infrastructure, this article considers the problem of individual load adjustment under a total capacity constraint. For reasons of scalability and simplified communications, distributed solutions to this problem are sought. Borrowing from communication networks (AIMD algorithms) and distributed convex optimisation, we describe a number of distributed algorithms for achieving relative average fairness whilst maximising utilisation. We present analysis and simulation results to show the performance of these algorithms. In the scenarios examined, the algorithms performance is typically within 5% of that achievable in the ideal centralised case, but with greatly enhanced scalability and reduced communication requirements.

A Google-like model of road network dynamics and its application to regulation and control

Inspired by the ability of Markov chains to model complex dynamics and handle large volumes of data in Googles PageRank algorithm, a similar approach is proposed here to model road network dynamics. The central component of the Markov chain is the transition matrix which can be completely constructed by easily collecting traffic data. The proposed model is validated using the popular mobility simulator SUMO. Markov chain theory and spectral analysis of the transition matrix are then shown to reveal non-evident properties of the underlying road network and to correctly predict consequences of road network modifications. Preliminary results from possible applications are shown and simple practical examples are provided throughout this article to clarify and support the theoretical expectations.

Plug-and-Play Distributed Algorithms for Optimized Power Generation in a Microgrid

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.

Stochastic Park-and-Charge Balancing for Fully Electric and Plug-in Hybrid Vehicles

Motivated by the need to provide services to alleviate range anxiety of electric vehicles, we consider the problem of balancing charging demand across a network of charging stations. Our objective is to reduce the potential for excessively long queues to build up at some charging stations, although other charging stations are underutilized. A stochastic balancing algorithm is presented to achieve these goals. A further feature of this algorithm is that it is fully decentralized and facilitates a plug-and-play type of behavior. Using our system, the charging stations can join and leave the network without any changes to, or communication with, a centralized infrastructure. Analysis and simulations are presented to illustrate the efficacy of our algorithm.

From Remote Experiments to Web-Based Learning Objects: An Advanced Telelaboratory for Robotics and Control Systems

This paper describes the current evolution of the telelaboratory facilities at the University of Pisa. In particular, starting from a standard environment providing remote access to a set of experiments, the telelaboratory is now organized as a collection of learning objects, i.e., modular didactic units designed following specific learning objectives within control systems and robotic fields. The telelaboratory has a remote Web-based access which can be used both as a simulation environment and as a remote way of performing real physical experiments. The developed telelaboratory is based on free open-source software such as Scilab/Scicos, Comedi, and real-time application interface patch for Linux kernels. In-house software tools, such as a Java Web hyper modular interface system, graphic environment tools, and a virtual laboratory interface based on Java applets, have been developed as a support for the learning process.

Cooperative Regulation and Trading of Emissions Using Plug-in Hybrid Vehicles

We present a new approach to regulate traffic-related pollution in urban environments by utilizing hybrid vehicles. To do this, we orchestrate the way that each vehicle in a large fleet combines its two engines based on simple communication signals from a central infrastructure. Our approach can be viewed both as a control algorithm and as an optimization algorithm. The primary goal is to regulate emissions, and we discuss a number of control strategies to achieve this goal. Second, we want to allocate the available pollution budget in a fair way among the participating vehicles; again, we explore several different notions of fairness that can be achieved. The efficacy of our approach is exemplified both by the construction of a proof-of-concept vehicle and by extensive simulations, and is verified by mathematical analysis.

Optimal power scheduling in a Virtual Power Plant

This paper proposes a novel approach where the Energy Management System of a Virtual Power Plant decides the optimal power scheduling not on the basis of some predefined policies, but upon the solution of an optimisation problem. The scheduling decision is dynamic as it depends on variable factors, not fully predictable, such as renewable sources availability, electrical energy price, controllable and uncontrollable loads demand and possibility of storing or releasing stored energy. The optimal solution is computed according to a novel cost function that explicitly takes into account only direct costs. Theoretical findings and expectations are proved through simulations of a realistic scenario.

Physical Characterization of Acoustic Communication Channel Properties in Underwater Mobile Sensor Networks

A methodology to predict underwater acoustic channel communication properties (capacity, bandwidth, range) from the environmental conditions in the ocean is proposed. The methodology is based on the use of acoustic propagation models coupled to a set of equations proposed firstly by Stojanovic [1]. A parametric study of channel characteristics as a function of changing environmental conditions is presented, showing in particular how channel range and/or source transmission power are influenced by the relative position of source and receiver with respect to the ocean temperature thermocline. This kind of results is crucial to adaptively configure the relative position of mobile nodes (typically AUVs – Autonomous Underwater Vehicles) in underwater sensor networks, with the final goal of mitigating the effects of environmental changes on the network communication capabilities.

On Optimality Criteria for Reverse Charging of Electric Vehicles

Ever increasing expectations regarding the penetration level of electric vehicles (EVs) are driving several areas of research related to EV charging. One topic of interest treats EVs not only as controllable loads but also as storage systems, which can be used to mitigate the load on the grid during peak times by offering power. This is known as vehicle to grid (V2G). Since returning energy to the grid affects mobility patterns, V2G has an associated environmental cost. In this paper, to investigate this issue, we formulate the problem of returning electrical load to the grid as an optimization whose goal is to return the desired energy in a fashion that minimizes the cost on the environment. We show that this optimization is highly complex, and in some circumstances, the cost of V2G can be prohibitive.

AIMD-like algorithms for charging electric and plug-in hybrid vehicles

Motivated by the expected increase of the penetration level of Electric Vehicles (EVs), and the wider usage of renewable energies, this paper investigates policy to share the available power to charge EVs. This paper follows the preliminary work [1] of the authors, where AIMD (Additive Increase Multiplicative Decrease) [2] based techniques were first proposed to charge EVs in a distributed way. The same mathematical framework is adopted in this paper, but the algorithms are tailored to deal with new scenarios of interest, as illustrated in detail in Section III.