Integration of plug-in electric vehicles into power grid
Nima Harsamizadeh Tehrani
Date of Issue2014
School of Electrical and Electronic Engineering
Plug-in Electric Vehicles (PEVs) are increasingly being seen as a sustainable mode of transport by countries worldwide. However, PEVs will add significant new load to the existing power distribution system and it will be a challenge to meet the new demand. On the other hand, the energy storage capability and charging flexibility of PEVs create an opportunity for being as ancillary service providers to improve performance of grid. In this study, stochastic modeling has been presented to estimate the system-wide PEV charging load within domestic grids. US National Household Travel Survey (NHTS) data set has been utilized in several ways to probabilistically quantify the PEVs status. The results indicate that the PEVs can contribute to increase the load demand at certain hours, although the charging demand is very limited most of the time. The most suitable methodology for the uncertain nature of the problem in modelling is the use of Monte Carlo simulation. Due to the existence of complex interdependencies between the system inputs, the problem definition leads to a multivariate uncertainty analysis problem. From the significant levels of vehicles' state of charge (SOC) observed, it can be foreseen that PEVs should be able to provide a notable amount of reserve capability. The operation of a distributed charging infrastructure is characterized. Then, the charging and discharging pattern is optimized according to day-ahead price variations to maximize the objective function that is the social benefit of the participation in the electricity market. Furthermore, Vehicle-to-Grid (V2G) provides the potential for the system operator to call on the PEV as a distributed energy resource and offers financial benefits to vehicle owner when supplying ancillary services. Using a stochastic model, a two-settlement market and fleet aggregator behavior has presented and showed that the problem of determining optimal contract offering for an aggregator can be solved using convex programming and coherent approaches to risk in optimization under uncertainty. The study provides decision-making tools for aggregator that allow making informed decisions within a short and medium term planning horizon while explicitly considering uncertain prices and demands. The main contribution is the formulation of the short-term trading problem of an aggregator as a stochastic programming problem with recourse, which, in addition, includes the modelling of the risk aversion using the Conditional Value-at-Risk (CVaR).
DRNTU::Engineering::Electrical and electronic engineering::Electric power