Smart management of the charging of electric vehicles

Abstract

The objective of this thesis was to investigate the management of electric vehicles (EVs) battery charging in distribution networks.\ud Real EVs charging event data were used to investigate their charging demand profiles in a geographical area. A model was developed to analyse their charging demand characteristics and calculate their potential medium term operating risk level for the distribution network of the corresponding geographical area. A case study with real charging and weather data from three counties in UK was presented to demonstrate the modelling framework.\ud The effectiveness of a charging control algorithm is dependent on the early knowledge of future EVs charging demand and local generation. To this end, two models were developed to provide this knowledge. The first model utilised data mining principles to forecast the day ahead EVs charging demand based on historical charging event data. The performance of four data mining methods in forecasting the charging demand of an EVs fleet was evaluated using real charging data from USA and France. The second model utilised a data fitting approach to produce stochastic generation forecast scenarios based only on the historical data. A case study was presented to evaluate the performance of the model based on real data from wind generators in UK.\ud An agent-based control algorithm was developed to manage the EVs battery charging, according to the vehicles’ owner preferences, distribution network technical constraints and local distributed generation. Three agent classes were considered, a EVs/DG aggregator and “Responsive” or “Unresponsive” EVs. The real-time operation of the control system was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens. A series of experiments demonstrated the adaptive behaviour of “Responsive” EVs agents and proved their ability to charge preferentially from renewable energy sources.

Original document

The different versions of the original document can be found in:

• http://orca.cf.ac.uk/95568/1/2016XydasEPhD.pdf