Plug-in electric vehicles (PEVs) present environmental and energy security advantages versus conventional gasoline vehicles. In the near future, the number of plug-in electric vehicles will likely grow significantly in the world. Despite the aforementioned advantages, the connection of PEV to the power grid poses a series of new challenges for electric utilities. This paper proposes a comprehensive approach for evaluating the impact of different levels of PEV penetration on distribution network investment and incremental energy losses. The proposed approach is based on the use of a large-scale distribution planning model which is used to analyze two real distribution areas. Obtained results show that depending on the charging strategies, investment costs can increase up to 15% of total actual distribution network investment costs, and energy losses can increase up to 40% in off-peak hours for a scenario with 60% of total vehicles being PEV.Index Terms-Distribution investment, distribution network planning, electricity distribution, network energy losses, plug-in electric vehicles.
SUMMARYNowadays, the amount of distributed generation (DG) connected to distribution networks is increasing significantly. In a European context, one of the main drivers for this growth is the support of electricity generation from renewable energy sources (RES) and combined heat and power (CHP) plants. Hence, these DG units mostly consist of non-controllable generation. Distribution is a regulated activity which is, at least, legally and functionally unbundled from the generation activity. These issues, together with the fact that distribution networks were not originally designed to accommodate generation, pose significant challenges on distribution network planning and operation. One of the major concerns of distribution system operators (DSOs) is the impact that large penetration levels of DG may have on distribution network costs. This paper presents a quantification of the impact of DG on distribution network costs in three real distribution areas. Different scenarios of demand and generation have been analysed for each region. Two possible situations are taken into account in each scenario: maximum net demand and maximum net generation. The computation of the distribution network costs was carried out by means of two large-scale distribution planning models called reference network models (RNMs).
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