CIRED Workshop - Rome, 11-12 June 2014 Paper 0240 Paper No 0240 Page 1 / 5 TECHNICAL IMPACTS OF ELECTRIC VEHICLES CHARGING ON AN ITALIAN DISTRIBUTION NETWORK Matteo DE MARCO Erotokritos XYDAS Charalampos MARMARAS Politecnico di Torino– Italy Cardiff University – UK Cardiff University - UK matteo.demarco@gmail.com XydasE@cardiff.ac.uk MarmarasC@cardiff.ac.uk Dr. Liana M. CIPCIGAN Prof. Maurizio REPETTO Dr. Maurizio FANTINO Cardiff University – UK Politecnico di Torino– Italy Istituto Mario Boella – Italy CipciganLM@cardiff.ac.uk maurizio.repetto@polito.it Maurizio.fantino@ismb.it ABSTRACT To prevent climate changes, the leaders of European Union declared environmental targets for 2020, known as 20-20-20 targets. These targets aim at a 20% reduction in greenhouse gas emissions and energy consumption of the 2020 projected levels, as well as a 20% increase in the renewable energy penetration in the total energy production. The electrification of transport offers a good opportunity to decrease CO 2 emissions and increase the national energy security. Electric vehicles (EVs) are a mobile source of electricity demand, charging for relatively long periods of time and as a result of this, EVs could place significant coincident demand on the system. This increase in the system's peak demand may cause transformer and cables overloading and violation of the voltage limits. The main aims of this research was to evaluate the impact of Electric Vehicles on a real Italian distribution network, located in the north Italy, considering different levels of EVs penetration, initial battery state of charge and EVs charging scenarios. INTRODUCTION Environmental factors are driving Europe to adopt new policies and regulations towards a low carbon future [1]. As estimated in [2], road transport is a significant contributor to greenhouse gas emissions, being responsible for approximately 23% of all CO 2 emissions of EU Members. Electromobility offers a tempting alternative solution for transportation, given the fact that the “tank-to-wheel” efficiency of an electric vehicle is about three times higher than the efficiency of an internal combustion engine (ICE) vehicle [3]. EU funded projects like “Green eMotion” [4] and MERGE [13, 15], promote the electrification of transport by researching all possible aspects of EV penetration in societies. Electric vehicle charging is a new type of electricity demand for the electric power systems, with special characteristics. Large scale integration of EVs requires an efficient management of the charging infrastructure and analysis tools are required to determine the effects of adding large numbers of mobile loads to the grid. Even small clusters of uncontrolled vehicles charging at peak periods could significantly stress the distribution system, slowing EVs adoption and requiring major infrastructure investments [6]. According to literature, several scenarios exist regarding the charging profiles of the electric vehicles. In the early stages of EV integration, the number of the EVs is expected to be relatively small, and the EV owners will charge their vehicles mainly at home, without any controlled scheme [5]. With the EV number gradually rising, the EV owners could be incentivised to charge their vehicles following a Dual-Tariff scheme, to reduce their energy cost and at the same time decongest the peak hours [5]. In more mature stages of EV integration, advanced control algorithms for EV charging will be applied to optimize the network operation and increase the contribution of Renewable Energy Resources (RES) to the power system [5-11]. The main aim of this research was to study the impact of the different EV charging scenarios on a real Italian distribution network. Three different scenarios were considered, the Uncontrolled, the Dual -Tariff and the Smart charging scenario. A case of EV charging from renewables was also studied, considering EV charging events at a commercial area using power from Solar Panels. NETWORK TOPOLOGY The modelled network consists of a three-phase source at 132kV; 132kV/15kV and 15kV/0.4kV transformers, 29 commercial loads connected at medium voltage and 26 residential loads connected at low voltage. Each low voltage load consists of 82 to 322 customers depending on the power demand. Figure 1 shows a schematic representation of the network. The network was modelled in GridLAB-D software, a power distribution system simulation and analysis tool. GridLAB-D was developed by the U.S. Department of Energy (DOE) at Pacific Northwest National Laboratory (PNNL) under funding for Office of Electricity in collaboration with industry and academia [12].