Driven by climate change and fast depleting stock of fossil fuel, electrification of transport systems, both rail and road, has been promoted by many governments around the world. The resultant changes in the load demand in the transmission and distribution electricity networks, along with other motivations such as integrating distributed generation from renewable sources, improving energy efficiency through demand response (DR) and managing increasingly aged infrastructure, have led to the paradigm of Smart Grid being proposed as the next generation of the power grid. Electric vehicle (EV) as a load type requiring power for rechargi~g has a significant I impact on power systems, e.g. increases of peak demand, voltage drop, powerlosses and harmonic distortion, decrease of load factor, transformer overload and feeder congestion. The work presented in this thesis studies the aspect ofDR in Smart Grid which could help mitigate the impact of EV s on power demand and exploit the ability to manage EV s charging times for improving power system performance, i.e. flattening the system load profile. It aims to address the following issues: modelling and monitoring EV charging profiles to obtain load information; and developing a DR model for optimising power systems demand due to EV charging. Through comprehensive research, a model of the EV charging load is obtained by statistical analysis. A non-intrusive load monitoring (NILM) system, capable of monitoring and identifying the presence of traditional appliance and the EV charging loads through measurements at a single point in a household, i.e. the consumer unit has been developed. In the light of the outcomes of the load research, two novel DR programs based on multiple time-of-use (TOU) tariffs and real-time prices with penalties (RTPP) respectively are proposed to manage EV charging for the optimisation of power systems demand. Example studies are carried out to validate and evaluate the DR programs. Results show that the programs can help flatten the system load profile and the fluctuations in the profile decrease gradually with increasing penetration levels of EV s. Furthermore, by levelling off load requirements, the programs could allow generation companies to operate their plant more efficiently, reduce degradation of power plant due to inefficient operation, help to reduce utility costs, and hence reduce customer bills. I . The developed load model, NILM system and DR model in this thesis provide much improved tools in EV charging load prediction and management for power system planning and optimal operations, because the stochastic behaviour of the EV charging load and diversities among EV s that have not been considered in previous researches are carefully studied. The proposed DR programs give a valuable insight into strategies for the design and implementation of DR in the future Smart Grid, resulting in possible congestion due to concurrent responses be avoided. Index Terms - demand response, electric vehicle, electricity tariff, load model, load signature, load disaggregation, non-intrusive load monitoring, pattern recognition, quadratic programming, real-time prices.