ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.Comment: 9 pages, 8 figures. [v2] Update timezone issue with Fig. 8 where x-axis and background load was shifted by 3 hour

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Coupling of Real-Time and Co-Simulation for the Evaluation of the Large Scale Integration of Electric Vehicles into Intelligent Power Systems

This paper addresses the validation of electric vehicle supply equipment by means of a real-time capable co-simulation approach. This setup implies both pure software and real-time simulation tasks with different sampling rates dependent on the type of the performed experiment. In contrast, controller and power hardware-in-the-loop simulations are methodologies which ask for real-time execution of simulation models with well-defined simulation sampling rates. Software and real-time methods are connected one to each other using an embedded software interface. It is able to process signals with different time step sizes and is called "LabLink". Its design implies both common and specific input and output layers (middle layer), as well as a data bus (core). The LabLink enables the application of the co-simulation methodology on the proposed experimental platform targeting the testing of electric vehicle supply equipment. The test setup architecture and representative examples for the implemented co-simulation are presented in this paper. As such, a validation of the usability of this testing platform can be highlighted aiming to support a higher penetration of electric vehicles.Comment: 2017 IEEE Vehicle Power and Propulsion Conference (VPPC

Wireless Authentication Solution and TTCN-3 based Test Framework for ISO-15118 Wireless V2G Communication

Vehicle to grid (V2G) communication for electric vehicles and their charging points is already well established by the ISO 15118 standard. The standard allows vehicles to communicate with the charging station using the power cable, i.e. a wired link, but it is improved to enable wireless (WLAN) links as well. This paper aims to provide an implementation accomplishes a wireless authentication solution (WAS). With that the electric vehicles can establish V2G connection when approaching the charging pool, then identify and authenticate the driver and/or the vehicle. Furthermore, the paper presents a TTCN-3 based validation and verification (V&V) framework in order to test the conformance of the prototype implementation against the standard

Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches

SMART GRIDS LABORATORIES INVENTORY 2015

A smart electricity grid opens the door to a myriad of new applications aimed at enhancing security of supply, sustainability and market competitiveness. Gathering detailed information about smart grid laboratories activities represents a primary need. In order to obtain a better picture of the ongoing Smart Grid developments, after the successful smart grid project survey initiated in 2011, we recently launched a focused on-line survey addressed to organisations owning or running Smart Grid laboratory facilities. The main objective is to publish aggregated information on a regular basis in order to provide an overview of the current facilities, to highlight trends in research and investments and to identify existing gaps.JRC.F.3-Energy Security, Systems and Marke

Deployment of Autonomous Electric Taxis with Consideration for Charging Stations

Autonomous electric vehicles are set to replace most conventional vehicles in the near future. Extensive research is being done to improve efficiency at the individual and fleet level. There is much potential benefit in optimizing the deployment and rebalancing of Autonomous Electric Taxi Fleets (AETF) in cities with dynamic demand and limited charging infrastructure. We propose a Fleet Management System with an Online Optimization Model to assign idle taxis to either a region or a charging station considering the current demand and charging station availability. Our system uses real-time information such as demand in regions, taxi locations and state of charge (SoC), and charging station availability to make optimal decisions in satisfying the dynamic demand considering the range-based constraints of electric taxis. We integrate our Fleet Management System with MATSim, an agent-based transport simulator, to simulate taxis serving real on-demand requests extracted from the San Francisco taxi mobility dataset. We found our system to be effective in rebalancing and ensuring efficient taxi operation by assigning them to charging stations when depleted. We evaluate this system using different performance metrics such as passenger waiting time, fleet efficiency (taxi empty driving time) and charging station utilization by varying initial SoC of taxis, frequency of optimization and charging station capacity and power

Towards ‘Smarter’ Systems: Key Cyber-Physical Performance-Cost Tradeoffs in Smart Electric Vehicle Charging with Distributed Generation

The growing penetration of electric vehicles (EV) into the market is driving sharper spikes in consumer power demand. Meanwhile, growing renewable distributed generation (DG) is driving sharper spikes in localised power supply. This leads to growing temporally unsynchronised spikes in generation and consumption, which manifest as localised over- or undervoltage and disrupt grid service quality. Smart Grid solutions can respond to voltage conditions by curtailing charging EVs or available DG through a network of cyber-enabled sensors and actuators. How to optimise efficiency, ensure stable operation, deliver required performance outputs and minimally overhaul existing hardware remains an open research topic. This thesis models key performance-cost tradeoffs relating to Smart EV Charging with DG, including architectural design challenges in the underpinning Information and Communications Technology (ICT). Crucial deployment optimisation balancing various Key Performance Indicators (KPI) is achieved. The contributions are as follows: • Two Smart EV Charging schemes are designed for secondary voltage control in the distribution network. One is optimised for the network operator, the other for consumers/generators. This is used to evaluate resulting performance implications via targeted case study. • To support these schemes, a multi-tier hierarchical distributed ICT architecture is designed that alleviates computation and traffic load from the central controller and achieves user fairness in the network. In this way it is scalable and adaptable to a wide range of network sizes. • Both schemes are modelled under practical latency constraints to derive interlocking effects on various KPIs. Multiple latency-mitigation strategies are designed in each case. • KPIs, including voltage control, peak shaving, user inconvenience, renewable energy input, CO2 emissions and EV & DG capacity are evaluated statistically under 172 days of power readings. This is used to establish key performancecost tradeoffs relevant to multiple invested bodies in the power grid. • Finally, the ICT architecture is modelled for growing network sizes. Quality-of- Service (QoS) provision is studied for various multi-tier hierarchical topologies under increasing number of end devices to gauge performance-cost tradeoffs related to demand-response latency and network deployment