In accordance with the United Kingdom's goal to reach net zero by 2050, electric vehicles (EVs) play a crucial role in transportation. However, if the electricity used to charge EVs is derived from fossil fuels, this does not necessarily imply a reduction of overall emissions nationally or globally. To achieve optimal EV charging, a deeper comprehension of the unpredictability of on-site renewable energy sources (ORES) energy output is required. In this paper, the predicted renewable energy generated is used as the actual value for the reinforcement learning algorithm simulation environment. Such a model can represent the relationship between the power generation and the wind speed as well as solar irradiation, which are characterized by significant uncertainties. The uncertainty analysis shows that the wind speed at Newcastle upon Tyne can be modelled as a Weibull distribution with parameters A = 19.98 and B = 1.91. As for energy demand, this paper integrates information from an Oslo (Norway) car parking garage-based set of EV charging stations with EVs' demand statistics. The charging habits of EV users range from 800 min to 1,000 min of parking time, and from 5 kWh to 20 kWh in terms of charging energy. The maximum connection frequency for EV charging is 20 min. In addition, this paper develops methods for stochastic EV charging and parking space occupancy employing actual data. Based on the aforesaid renewable energy generation and the EV charging status, it is possible to develop a decision algorithm to optimal renewable energy efficiency.
|Title of host publication||Proceedings of the 3rd International Symposium on New Energy and Electrical Technology|
|Editors||Wenping Cao, Cungang Hu, Xiangping Chen|
|Place of Publication||Singapore|
|Number of pages||13|
|ISBN (Print)||9789819905522, 9789819905553|
|Publication status||Published - 10 Mar 2023|
|Name||Lecture Notes in Electrical Engineering|