Impact of plug-in hybrid vehicles charging on distribution networks

This thesis is dedicated to study how the charging behaviours of plug-in hybrid vehicles affect the local distribution network. This study focuses on two issues: the power loss and charging cost optimization. The multi-objective particle swarm optimization technique is applied to achieve the optimal charging schedule, resulting in acceptable additional power loss ratio and charging cost. The power loss on electric lines is correlated to the load demand. However,due to the complexity of the distribution network including the transformers and unbalances of loads,it is necessary to understand the power loss-load demand model. The loss-load modelling is based on the distribution network structure and power flow analysis. The two classic distribution networks (IEEE 13-Node and IEEE 34-Node) are employed for power flow analysis.As the consequence of power flow analysis,a new power loss-load demand model is presented. In this thesis, the additional power loss ratio (APLR) is analysed to present the plug-in hybrid electric vehicle (PHEV) impact of power losses on distribution network. To study the charging cost impacts of PHEV,the least square error method is employed to curve fit the data of Australia electricity market and the electricity price-load and further charging cost-load equations are derived. Particle swarm optimization method is used in the optimization and Multi-Objective optimization is conducted to achieve the optimal charging schedule for PHEV to cause less APLR at acceptable charging costs. All the methodologies and algorithms are verified by simulations. The power losses and charging cost impacts and optimizations are simulated by DigSilent Power Factory and MATLAB