CIRED Workshop - Rome, 11-12 June 2014 Paper 0451 Paper No 0451 Page 1 / 5 ADVANCED METERING INFRASTRUCTURE FOR REAL-TIME COORDINATION OF RENEWABLE ENERGY AND ELECTRIC VEHICLES CHARGING IN DISTRIBUTION GRID A. Bouallaga 1,2,3 , R. Kadri 1,4 , V. Albinet 1,3 , A. Davigny 1,3 , F. Colas 1,4 , V. Courtecuisse 5 , A. Merdassi 6 , X. Guillaud 1,7 , B. Robyns 1,3 Laboratoire d’Electrotechnique et d’Electronique de Puissance de Lille (L2EP), Université Lille Nord de France, Lille, France 1 SEOLIS, 336 Avenue de Paris, 79000 Niort, France 2 HEI, 13 rue de Toul, F-59046, Lille, France 3 Arts et Métiers ParisTech Centre de Lille, 8 Boulevard Louis XIV, 59 046 Lille, France 4 GEREDIS Deux-Sèvres, 17 Rue des Herbillaux, 79000 Niort, France 5 OPAL-RT TECHNOLOGIES, 1751 Richardson, Suite 2525 Montréal, Québec, Canada H3K 1G6 6 École Centrale de Lille, Cité Scientifique, 59651 Villeneuve-d'Ascq, France 7 anouar.bouallaga@hei.fr , Riad.kadri@ensam.eu , Valentin.albinet@hei.fr , Arnaud.davigny@hei.fr , Frederic.colas@ensam.eu , vcourtecuisse@geredis.fr , asma.merdassi@opal-rt.com , xavier.guillaud@ec-lille.fr , Benoit.robyns@hei.fr ABSTRACT In this paper, we have designed distributed energy test platform with an advanced metering infrastructure to help assessing Electric Vehicles supervision strategy influence in real test network and evaluating communication constraint. Our designed test platform consists of following major components: real time simulator with power amplifiers, Medium Voltage network model in ePHASORsim, Electric Vehicle’s emulators, and an advanced metering infrastructure information network containing smart meters, data concentrator (hub) and information system. 1. INTRODUCTION France has made the development of Electric Vehicles (EVs) and Plug-in Hybrid Vehicles (PHEVs) an important priority of its policy to reduce Greenhouse Gas emissions. In 2009, the government launched a national program to host 2 million of EVs /PHEVs by 2020. However, large penetration of EVs in distribution network would result in potential problems on power quality (power losses, voltage drops, overloads, etc.) and generates significant investment costs [1] [2]. In this context, Seolis (Energy supplier in the French department of Deux-Sevres) initiated a project called VERDI “Renewable Energies and Electric Vehicle in Smart Distribution Networks” to deal with EVs energy demand. VERDI project’s research activities focus on developing methods for optimizing and supervising the EVs load in distribution network. Moreover, VERDI’s work aims to develop smart EVs recharging infrastructure that allows limiting environmental and financial impact by avoiding EVs charging during peak hours. Thus, a supervision strategy of EVs load has been developed to provide ancillary services to the Distribution System Operator (DSO). The studies presented in [3] and [4] showed that an adequate EVs load control using Fuzzy Logic Supervisor enables to reduce the energy transmission costs and CO 2 emissions. These objectives were achieved by smoothing power peaks caused by EVs and increasing coordination of EVs and wind-photovoltaic power sources. In this study, supervision strategy is evaluated using an Hybrid Demonstrator linking real time simulator with physical components. It allows to:  Assess supervision’s strategy influence in real test network,  Assess communication constraints,  Validate technical principles by interfacing the experimental platform with smart meters and EVs charging station. This paper is organized as following: at first, the supervision strategy principle is explained in section 2. After that, the case study characteristics are presented; it consists of test system specifications and Medium Voltage network (MV) simulation. The experimental test platform structure is given in section 4. Afterwards, experimental results are shown in section 5 and finally, conclusions and perspectives are presented in Section 6. 2. SUPERVISION STRATEGY The supervision strategy objective is to control the Electric Vehicles (EVs) load in order to limit the energy transmission costs of the Distribution System Operator (DSO). To achieve this goal, we have to promote the local consumption of wind and photovoltaic (PV) power by coordinating them with EVs load, to maximize EVs charging during cheaper energy period and to avoid exceeding subscribed power.