The current high energy consumption is a constant global risk due to the impact that this causes on the planet, multiple solutions from the technical, social and economic points of view have sought to mitigate these problems. One of these solutions is known as microgrid, which integrates renewable energy sources through power interfaces in order to provide quality electric power service. The use of DC / AC converters in microgrids is fundamental and therefore has been investigated from the topologies, modulation and control schemes. One of these techniques is known as Virtual Synchronous Generators or Synchronverter, where the inverter is searched for as a synchronous generator. Therefore, in the following article the Synchronverter technique and proportional resonant control is developed to operate inverters in parallel with in an electrical microgrid.

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Given a graph, like a social/computer network or the blogosphere, in which an infection (or meme or virus) has been spreading for some time, how to select the k best nodes for immunization/quarantining immediately? Most previous works for controlling propagation (say via immunization) have concentrated on developing strategies for vaccination pre-emptively before the start of the epidemic. While very useful to provide insights in to which baseline policies can best control an infection, they may not be ideal to make real-time decisions as the infection is progressing.In this paper, we study how to immunize healthy nodes, in presence of already infected nodes. Efficient algorithms for such a problem can help public-health experts make more informed choices. First we formulate the Data-Aware Vaccination problem, and prove it is NP-hard and also that it is hard to approximate. Secondly, we propose two effective polynomial-time heuristics DAVA and DAVA-fast. Finally, we also demonstrate the scalability and effectiveness of our algorithms through extensive experiments on multiple real networks including epidemiology datasets, which show substantial gains of up to 10 times more healthy nodes at the end.

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