Smart coordination approach for power management and loss minimization in distribution networks with PEV penetration based on real time pricing

Abstract

The impact of Plug in Electric Vehicles (PEV) will be most significantly felt by the electric power distribution networks, and specifically by distribution transformers that exist on each neighborhood block and cul-de-sac as customers charge their PEVs. That impact is unlikely to be positive. Since PEV adoption is initially expected to cluster in neighborhoods where demand for PEVs is strongest, the new load may overload transformers, sap much-needed distribution capacity and also increase distribution network losses. Hence, the national goal of putting one million PEVs on the road by 2015 could easily impose a severe burden on the distribution network. Whether PEVs will help or hinder electricity provision will depend on how frequently and at what times the customers charge their vehicles. This behavior will be driven in part by the rate structures that are offered by utilities, as well as the price responsiveness of PEV owners to those rate structures. In this chapter, we propose a method to optimally charge the PEVs in order to minimize the system distribution network losses and to maximize energy transferred to PEVs. A novel short term prediction unit consisting of a receding time horizon method is proposed to forecast the PEV load and a multi objective bacterial foraging algorithm is used as an optimization tool. Also it is interesting to study the manner in which distribution network losses vary with PEV charging behavior. Hence the purpose of this chapter is to demonstrate a power management strategy using smart coordination approach to (a) design a charging and discharging infrastructure for the PEVs that maximizes energy delivered to PEV batteries and (b) reduce the distribution network losses to avoid overloading of the grid.