Sonja Stüdli

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.

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.

Optimal real-time distributed V2G and G2V management of electric vehicles

This paper exploits the analogy between the electrical grid and modern communication networks to implement Electric Vehicle (EV) battery charging scheduling algorithms inspired by popular communication network techniques. In preliminary works, a similar approach was used to manage the Grid-to-Vehicle (G2V) active power flows. In this paper, we extend this framework to both implement the Vehicle-to-Grid (V2G) concept and to provide reactive power compensation capabilities that do not affect charging times. The ability of the proposed algorithms to optimally share the available/desired power in a fair way, with minimum communication requirements, in a very uncertain, dynamically changing framework, is illustrated through several examples for different scenarios of interest.

Distributed Load Management Using Additive Increase Multiplicative Decrease Based Techniques

Due to the expected increase in penetration levels of Plug-in Electric Vehicles (PEVs), the demand on the distribution power grid is expected to rise significantly during PEV charging. However, as PEV charging in many cases may not be time critical, they are suitable for load management tasks where the power consumption of PEVs is controlled to support the grid. Additionally, PEVs may also be enabled to inject power into the grid to lower peak demand or counteract the influence of intermittent renewable energy generation, such as that produced by solar photovoltaic panels. Further, PEV active rectifiers can be used to balance reactive power in a local area if required, to reduce the necessity for long distance transport of reactive power. To achieve these objectives, we adapt a known distributed algorithm, Additive Increase Multiplicative Decrease, to control both the active and reactive power consumption and injection. Here, we present this algorithm in a unified framework and illustrate the flexibility of the algorithm to accommodate different user objectives. We illustrate this with three scenarios, including a domestic scenario and a workplace scenario. In these scenarios the various objectives allow us to define a type of “fairness” for how the PEVs should adapt their power consumption, i.e. equal charging rates, or charging rates based on energy requirements. We then validate the algorithms by simulations of a simple radial test network. The simulations presented use the power simulation tool OpenDSS interlinked with MATLAB.

Line point registration: a technique for enhancing robot localization in a soccer environment

The Standard Platform League (SPL) provides an environment that is essentially static; with the exception of other robots and the audience, the area in which a robot is expected to localise itself is quite favourable. However, a large number of the predefined landmarks in the given world model can be perceived as ambiguous in many scenarios, with the prime example being field line markings. In this paper a technique is presented that implicitly disambiguates these detected field line objects in order to use them for localization purposes.

Efficient localization for robot soccer using pattern matching

One of the biggest challenges in the RoboCup Soccer Standard Platform League (SPL) is autonomously achieving and maintaining an accurate estimate of a robot’s position and orientation on the field. In other robotics applications many robust systems already exist for localization such as visual simultaneous localization and mapping (SLAM) and LIDAR based SLAM. These approaches either require special hardware or are very computationally expensive and are not suitable for the Nao robot, the current robot of choice for the SPL. Therefore novel approaches to localization in the RoboCup SPL environment are required. In this paper we present a new approach to localization in the SPL which relies primarily on the information contained within white field markings while being efficient enough to run in real time on board a Nao robot.

From vehicular platoons to general networked systems: String stability and related concepts

Abstract Networked systems and their control are highly important and appear in a variety of applications, including vehicle platooning and formation control. Especially vehicle platoons have been intensively investigated. An interesting problem that arises in this area is string stability, which broadly speaking means that an input signal amplifies unboundedly as it travels through the vehicle string. However, various, not necessarily equivalent, definitions are commonly used. In this paper, we aim to formalise the notion of string stability and illustrate the importance of those distinctions on simulation examples. A second goal is to extend the definitions to general networked systems.

On-off based charging strategies for EVs connected to a Low Voltage distributon network

The development of appropriate Electric Vehicle (EV) charging strategies has been identified as an effective way to accommodate an increasing number of EVs on Low Voltage (LV) distribution networks. Most research studies to date assume that future charging facilities will be capable of regulating charge rates continuously, while very few papers consider the more realistic situation of EV chargers that support only on-off charging functionality. In this work, a distributed charging algorithm applicable to on-off based charging systems is presented. Then, a modified version of the algorithm is proposed to incorporate real power system constraints. Both algorithms are compared with uncontrolled and centralized charging strategies from the perspective of both utilities and customers.

A network controlled load management scheme for domestic charging of electric vehicles

This paper proposes a end-to-end load management scheme for domestic charging of electric vehicles (EVs) based on the bidirectional communication capabilities of the smart grid. In particular, the paper considers the case of using EV fleets for night-time valley-filling of the daily load curve. The novel concept of discontinuous EV charging based on small energy bursts is introduced that utilizes the benefit of the statistical multiplexing to accommodate higher number of vehicles and to provide differentiated energy supply against a variable energy-budget within a pre-defined time. The paper also suggests two energy scheduling algorithms to ensure priority and/or fairness among the contending vehicles. A wide-area smart grid communications network based on WiMAX technology has been used as a proof of concept for this study. Simulations are conducted using an integrated OPNET model to jointly examine the performances of the energy scheduling algorithms and the communications network for a large-scale EV charging system. The results show that the proposed scheme can efficiently support the EV charging load in an equitable manner with a very low communications overhead.