مقاله انگلیسی رایگان در مورد برنامه های فازی ژنتیکی برای شارژ اتومبیل های الکتریکی – الزویر 2018

Perceived as futuristic prototypes not so long ago, electric vehicles (EVs) and all the technology surrounding them have experimented an extraordinary growth in the last years, to the point that now EVs are seen as a real alternative to fossil-fuelled vehicles, producing an increasing impact on the economy and the environment. Clearly, they reduce the dependency on petrol and promote alternative more environmentally-friendly sources of energy. Also, they can act as valuable distributed energy resources, smoothing intermittency due to renewable energy sources and supporting grid-wide frequency stability (Kang et al., 2013). The use of EVs also presents several technological challenges, as deciding the optimal locations of new vehicle charging stations You and Hsieh (2014) or the development of smart systems that manage charging grids in order to optimally distribute and balance electricity consumption while satisfying EVs’ electricity demands. A great variety of problems arise depending, for instance, on the charging infrastructure or the objectives to be satisfied (Rahman et al., 2016). Recent reviews of methods and strategies to manage electricity grids and schedule EVs charging can be found in Hernandez-Arauzo et al. (2015). Here we consider a problem that consists in scheduling the charging of a set of EVs taking into account the characteristics of a real-life charging station described in Sedano et al. (2013). The station has been designed to be installed in private car parks under simplicity and economy criteria. Each parking place in the car park is owned by a particular user and has a charging point. Every charging point is connected to one of the lines of a three-phase electric feeder, with a centralised control that establishes the power available to the point at any time. There are power constraints limiting the number of EVs that can be simultaneously charging on one line. There is also a balance constraint that limits the difference in active charging points between different lines. It is this balance constraint that poses the most relevant difference with respect to other charging models in the literature. We will tackle a variant of the static scheduling problem for the charging station presented by Hernandez-Arauzo et al. (2015). Their problem statement assumes that the set of EVs that need to be charged within a planning horizon is known in advance, together with the exact arrival time, desired departure time and charging time of each EV. The goal is to provide a charging schedule for all the EVs that is feasible in the sense that all technical constraints hold and such that the total tardiness with respect to the desired departure times is minimised. This static version of the problem is of great importance, since it constitutes the basis for solving a dynamic and hence more realistic version. However, it assumes that charging times are exactly known in advance, an assumption that might be deemed as unrealistic. We thus propose in this work to narrow the gap between the academic model and the real situation by incorporating uncertainty into charging times.