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This paper focuses on the impact of the charge of Plug-in Electric Vehicles (PEVs) on the dynamic response of power systems and proposes an efficient solution to control electric vehicle chargers, by dynamically allocating the available power in an optimized way. The proposed approach is based on an Additive-Increase-Multiplicative-Decrease (AIMD) stochastic decentralized control strategy to efficiently and seamlessly manage the charge of a high number of PEVs with little communication efforts. A modified version of the New England network is utilized to validate the proposed control through a variety of scenarios and control setups. Index Terms-Plug-in electric vehicles, loading margin, frequency control, decentralized control.

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The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and more recently many statistical or machine learning algorithms, and data-driven methods in general, are becoming subject of intense research. While generally the power research community focuses on power forecasting at the level of single plants, in a short future horizon of time, in this time we are interested in aggregated macro-area power generation (i.e., in a territory of size greater than 100000 km 2 ) with a future horizon of interest up to 15 days ahead. Real data are used to validate the proposed forecasting methodology on a test set of several months.

The number of Plug-in Electric Vehicles (PEVs) is increasing worldwide, as well as are the rates with which vehicles can be charged. This poses the question regarding how many PEVs may ultimately be connected simultaneously for charging, and how quickly the load of PEVs can increase, before the existing power grids show stability issues. In particular, we denote the first as a static limit, and has mainly an impact in terms of node voltages, while the second is a dynamic limit, which mainly affects the frequency of the system. In this paper, we shall use a transient stability model of power systems to assess both limits for a realistic power transmission system, and conclude that the static limit is actually the most critical one.