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Photovoltaic (PV) powered Electric Vehicle Charging Stations (PVCS) have received extensive attention recently due to the complementary relationship of PV energy and electric vehicles. This paper proposes a methodology aimed at assisting a Charging Point Operator (CPO) in determining the size of the main components of such PVCS. The modular structure of the method gives flexibility for possible use on a new sizing problem by modifying key parameters such as the EV charging demand (i.e., arrival/departure times and energy needed to fill the battery), the EV charging strategy or the business model, independently from each other. It is of particular interest for a CPO that sizes many PVCS operated in the same environment (for example, a car park at a workplace). In that case, the CPO first has to apply the method on a representative charging station. Next, he can re-use parts of the obtained results to drastically speed up (from weeks to hours) the sizing of the other charging stations. The proposed method has been applied to the EVCS of an industrial research complex in southern France. The input dataset used to apply the method consists of more than 32,000 charging transactions spanning over 6 years with 350 EV users and 80 charging points. Three charging strategies with different levels of complexity are investigated, including Mean Power, Plug and Charge, and Solar Smart Charging. The considered business model is based on the maximization of the self-production rate. The numerical findings reveal that employing a straightforward charging strategy, such as Mean Power, leads to a substantial reduction of nearly half in the required size of the PV plant compared to the basic Plug and Charge mode. In addition, our analysis demonstrates that Solar Smart Charging has the potential to decrease the PV plant size by nearly three times. [ABSTRACT FROM AUTHOR]

In 2009, the European Centre for Disease Prevention and Control (ECDC) estimated that multidrug-resistant (MDR) bacterial infections were responsible for 25,000 extra-deaths per year. In 2015, another report estimated that 12,500 extra-deaths were attributable to MDR bacteria every year in France. Recently, the United Nations claimed that resistance to antimicrobials was a global scourge, forecasting 10 million deaths in 2050. Surprisingly, our antibiotic resistance surveillance system in Marseille, France, did not allowed us to observe similar trends. We herein compared our data on extremely drug-resistant (XDR)/pandrug-resistant (PDR) patient extra-deaths to evaluations and predictions from these reports. First, we retrospectively collect and analyze antibiotic resistance data produced by our settings between November 2009 and March 2015 to look for 30-day deaths attributable to XDR/PDR strains belonging to 11 bacterial species/genus. In parallel, we performed a PubMed literature search to look for articles published prior to July 2016 and describing human deaths due to PDR strains. Overall, 35,723 patients were infected by at least one bacterial species/genus of interest and 85 by XDR/PDR strains. Of these patients, only one death was attributable to a XDR bacterial infection in a patient with strong comorbidities and two consecutive septic shocks. Our literature review shows that only four articles described human deaths due to PDR bacteria. All together, these data allowed us to conclude that there is a large discrepancy between the real count of deaths attributable to XDR/PDR bacteria and alarmist predictions.