Analyzing the impacts of Plug-in EVs on the California power grid using quadratic programming and fixed-point iteration

This paper investigates the impacts of a large fleet of Plug-in Electric Vehicles (PEVs) charging/discharging behaviors on the electricity price dynamics of California power grid. Assuming that PEV owners charge/discharge their vehicles in a way which minimizes the total electricity cost, a quadratic programming framework is applied to optimize the PEVs' charging patterns. The charge requirement of all PEVs is estimated based on the projected PEV population in 2025 and estimated seasonal variations. Due to increase in the demand and electricity price because of the added PEV load, a fixed point iteration method is applied to find new equilibrium load and price trajectories. By comparing the real electricity price and that resulted from the fixed point iteration, we can observe the impact of the PEVs on the power grid. In order to establish a realistic estimation pertaining to the PEV energy consumption and charging requirements, we use data from the National Household Travel Survey (NHTS) to generate a set of driving profiles. Moreover, we use the California ISO's historical electricity price and load data to develop a simplified electricity price model, as well as the California historical VMT data to calculate seasonality factors. Simulation results indicate that the PEVs in large numbers can help shave the demand peak and fill the demand valley, thereby flattening the grid demand profile. Moreover, we observe that California power grid is capable of accommodating a large PEV fleet without requiring a major upgrade to the existing electricity infrastructure, provided that optimal charge scheduling and bidirectional V2G interaction are enabled for the PEVs.