Study of the impact of EV charging load based on a temporal-spatial model

Abstract

In the past few years, global warming has been a burning topic globally and many attempts have been made to lighten the situation. Upon discovering that transportation was one of the major contributors of greenhouse gases, which results in ozone depletion and hence global warming, many automobile companies and power service providers have come together to introduce a revolutionary technology, Electric Vehicles (EV). With the growing awareness of environmental health, the public are keen to look into EVs. As a result, companies are constantly engaged in Research and Development (R&D) and consumer education, to release better improved versions of EVs and increase charging stations to keep up with consumers’ demand. Observing the trends for demand of EVs, many EV service providers believe that there will be a continuous increase in the demand over the next few years. However, with the growing demand of EVs and limit charging stations, there will be a possibility of power congestion. Coupled with the unpredictable random behavior of current EV users, it can be rather challenging to predict the location and timing of power congestion. Hence, in this project, a time-spatial probabilistic model will be generated with the help of MATLAB, a software programming platform, to illustrate and identify the graphical distribution of load demand at certain charging points at different times of the day. Linking to real life application, this type of probabilistic model can be used by power service providers to take appropriate preventive measures to prevent power congestion during peak periods. This thesis will contain research information and theories relevant to the study of this project. Project progression will also be recorded within this thesis along with results obtained.