1 Optimizing Electric Vehicle Coordination over a Heterogeneous Mesh Network in a Scaled-down Smart Grid Testbed Bishnu Prasad Bhattarai, Student Member, IEEE, Martin L´ evesque, Student Member, IEEE, Martin Maier, Senior Member, IEEE, Brigitte Bak-Jensen, Senior Member, IEEE, and Jayakrishnan R. Pillai Member, IEEE Abstract—High penetration of renewable energy sources and electric vehicles (EVs) creates power imbalance and congestion in the existing power network and hence causes significant problems in the control and operation. Despite investing huge efforts from the electric utilities, governments, and researchers, smart grid (SG) is still at the developmental stage to address those issues. In this regard, a smart grid testbed (SGT) is desirable to develop, analyze, and demonstrate various novel SG solutions, namely demand response (DR), real-time pricing, and congestion management. In this work, a novel SGT is developed in a laboratory by scaling a 250 kVA, 0.4 kV real low voltage distribution feeder down to 1 kVA, 0.22 kV. Information and communication technology (ICT) is integrated in the scaled- down network to establish real-time monitoring and control. The novelty of the developed testbed is demonstrated by optimizing EV charging coordination realized through the synchronized exchange of monitoring and control packets via an heterogeneous Ethernet-based mesh network. Index Terms—Demand Response, Controllable Load, Electric Vehicle, Smart Grid Testbed, Smart Grid Communications. I. I NTRODUCTION I NCREASED environmental awareness in recent years has encouraged rapid growth of green technology developments such as renewable energy sources and electric vehicles (EVs) in existing power grids to reduce global carbon emissions. This scenario creates system level power imbalance as well as localized congestion in distribution networks, and in turn creates critical problems in the control and operation of exist- ing grids. Many electric utilities, governments, and researchers are investing huge efforts towards addressing those issues by transforming existing power grids into smart grids (SGs) with the objective of integrating information and communication technology (ICT) into the existing power network to enable two-way flows of power, information, and control among multi-objective SG actors. Despite extensive efforts towards implementing SGs, they are still at the developmental stage. Very few attempts have been done towards implementing smart grid testbeds (SGTs). In [1], the authors proposed a This work was supported by NSERC Strategic Project Grant No. 413427-2011, Forskel Grant No. 10782, and FQRNT Doctoral Research Scholarship No. 165516. Bishnu Prasad Bhattarai, Brigitte Bak-Jensen, and Jaykrishnan R. Pillai are with the Department of EnergyTechnology, Aalborg University, Denmark. Martin L´ evesque and Martin Maier are with the Optical Zeitgeist Laboratory, INRS, Canada. Corresponding author: Bishnu Prasad Bhattarai, Aalborg University, 9220 Aalborg, Denmark (email: pbb@et.aau.dk). wireless smart grid lab (named SmartGridLab) to design and analyze new protocols in a lab environment. One significant novelty in their power testbed design is the control of in- telligent power switches (IPSs) to dynamically route energy based on intermittent renewable energy sources. However, the work focused on the design of a SGT without showing significant experimental results. Furthermore, the obtained network performance following this design would eventually not allow to compare to a real power distribution network since it is designed to a single household. A system to monitor and control an office environment and couple it with the SG has been proposed and implemented in a living lab environment [2]. To remotely control appliances, a ZigBee-based wireless network was used, and significant economic savings in the order of 11-15% have been obtained by optimizing appliance operations based on price signal. Some interesting lessons learned were noted, including that the ZigBee network perfor- mance can be affected by other devices such as microwaves, and that their proposed system scales as the number of devices increases. In [3], the authors proposed a cognitive radio system architecture and microgrid testbed. SGTs enable to find new issues and validate the feasibility of new SG mechanisms. Despite extensive efforts towards SGTs have been driven, notable progress on the practical demonstration of EV charging coordination has not been realized yet, which is the focus in this work. Most of the researches on EV charging management so far were done based on simulation and calculation [4]–[8]. In [4], a decentralized EV charging algorithm was proposed for consumer comfort maximization. However, this approach does not respect network constraints and economic aspects of EV charging. To overcome the aforementioned problems, the authors in [5] proposed an optimum EV scheduling based on the minimization of EV charging cost. EV charging schedules were prepared by minimizing charging cost for EV owners by simultaneously maximizing the aggregator benefits. However, the research application was limited to scheduling only and cannot adapt with real-time operating conditions. In [6], a co- ordinative approach was proposed for EV charging taking care of consumer requirement and minimization of the total EV charging cost in real-time operating conditions, the network constraints were not considered though. An EV charging algorithm, proposed in [7], implemented a local voltage and current sensitivity based EV charging to overcome the aforementioned network problems. However,