Multi-Objective Optimization of Off-Grid E-Bikes Charging Stations Powered by PhotoVoltaics

The integration of renewable energy in the power supply chain of Electric Vehicles (EVs) is fundamental in order to decarbonize the transportation sector. Yet, this poses additional threats to the smooth functioning of power systems. In the case of e-bikes, the load is modest and it becomes conceivable to exploit as much as possible distributed renewable power generation coupled with storage. For this reason, attention has recently been growing towards the development of off-grid charging stations for Light EVs (LEVs) powered by renewables. For this kind of charging stations, the power supply for the e-bikes can arrive solely from renewable power production or storage and it is not guaranteed that there is power available for the recharge whenever the demand occurs. Hence, the design of such systems needs to consider two conflicting objectives, which are the minimization of the costs and of the number of not served e-bikes. Based on such premise, this work contributes to the multi-objective optimization of off-grid charging stations for e-bikes. A Genetic Algorithm is employed to determine the most appropriate rated power of the installed PhotoVoltaic (PV) systems and of the energy storage, by incorporating statistical methods to estimate the daily number of e-bikes requiring charging, hence making the optimization process more reflective of actual usage patterns. Under the assumed conditions, the optimized solution guarantees a high quality of service, as the number of uncharged e-bikes is less than the 5%. The Capital Expenditure (CapEx) and Operational Expenditure (OpEx) are estimated for the identified optimized charging station and compared against the grid-connected case and it arises that the off-grid system is slightly more profitable after 3 years, due to the savings in the energy costs.