Impact of electric vehicles on the cost-competitiveness of generation and storage technologies in the electricity system

Abstract

The increasing levels of variable renewable electricity (VRE) generation-such as wind and solar power-will create important opportunities for the charging of electric vehicle (EV) batteries during low-cost hours with a lot of VRE generation and for the discharge of EV batteries back to the grid (i.e. vehicle-to-grid; V2G) during high-cost hours. This study investigates how different EV charging strategies influence the cost-competitiveness of generation and storage technologies other than EV batteries in the electricity system, using a regional electricity system investment and dispatch model. The charging requirements of the EVs, which are used as an input to the optimisation model, are derived from the yearly driving patterns of 426 vehicles measured with global positioning system. The study is carried out for four regions in Europe with different conditions for wind, solar and hydro power generation. The results show that optimised EV charging with V2G can: (i) reduce investments in peak power capacity in all the regions investigated; (ii) reduce the need for short-term and longterm storage technologies other than EV batteries (i.e. stationary batteries and hydrogen storage); and (iii) stimulate increased shares of solar and wind power generation, as compared to direct charging in some regions (mainly Hungary). This study also shows that EV battery capacities as low as 30 kWh, which are connected to the grid only at their home location, can to a large extent contribute with flexibility to the electricity system in the way mentioned. The present study also investigates the influences of different shares of the fleet participating in V2G, and shows that the additional benefits for the electricity system level off when approximately 24% of the vehicle fleet participates in V2G.

Assessment of lithium criticality in the global energy transition and addressing policy gaps in transportation

“…Three of the remaining four curves, which correspond to battery lifetime in second use (represented by the remaining two curves presenting variation in lifetime of secondary batteries), and vehicle-to-grid (V2G) integration, closely follow the trend corresponding to the BPS 3b LDV demand with some minor differences depending on the demand scenario. For instance, the integration of V2G technology 40 , 58 relieves the power sector. Hence, less second-life material is required and more Li can go directly to recycling because of BEV integration into the grid.…”

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

“…They increase the electric load, but may also provide temporal flexibility for integrating variable renewable energy sources and contribute to decarbonizing transportation 2 . Many model-based analyses investigate potential power sector interactions of future BEV fleets [3][4][5][6] and thus depend on a meaningful representation of electric vehicles' mobility patterns.…”

“…This can be interpreted as the vehicle making a short stop during a longer trip. Charging efficiency is set to 90% 6,9,15,23 .…”

Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

“…As such, GPS measured driving behavior can also reveal individual variations and how these variations might affect charging power demand. Several datasets of GPS measurements with conventional cars exist, but have mainly been used to study driving behavior (Dong et al, 2014), EV battery size requirements (Pearre et al, 2011;Björnsson and Karlsson, 2015) and impacts on investments in the electricity system (Taljegard et al, 2019). Pearre et al (2011) used a GPS measured database from Atlanta in the United States to estimate battery sizes needed to fulfill certain number of recorded trips.…”