Micro smart grids and electromobility charging optimisation with a distributed agent application: demonstration

In recent years, several countries and organisations increased their e↵orts on the use of renewable energy and electromobility [1]. Reaching the goal of a more environmentfriendly energy production together with less expensive transportation by using electromobility concepts is much more complicated as expected. Energy from renewable energy sources (e.g., wind or water) have the disadvantage of being highly variable and volatile. Furthermore, the occurrence of renewable energy and energy demand is highly asynchronous, such that the most energy from renewable sources is produced around noon and midnight, while energy demand commonly peaks around the morningand evening time. The dephased occurrence aggravates any successful integration of renewable energy sources into today’s energy infrastructures. In fact, there are stakeholders that share the opinion that future distribution systems will not be as stable as present systems, but significantly su↵er from the fluctuating occurrence of heterogeneous energy sources [2]. For solving these issues, energy providers intend to use electric vehicles (EVs) as cheap and distributed ‘bu↵ers’ for surpluses of energy and to replace expensive backup power plants that are used to provide regulatory energy. Yet, the first generation of field-test projects [5] proofed that neither energy grid infrastructures nor the capability of electric vehicles will feature the requirements for this application any time soon. On the other hand, concepts of using renewable energy in a more local context like micro smart grids to save line and