Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Distribution grid impacts of electric vehicles: A California case study.

ABSTRACT: California has adopted a substantial number of electric vehicles over the last decade but there are many challenges associated with the electrification of vehicles, including how they interact with the electricity grid. We employ real-world feeder circuit level data in California from PG&E to measure the capacity of local feeders. We model the adoption of electric vehicles down to the census block and take advantage of real-world vehicle charging data to simulate the future loading on circuits throughout Northern California. In our highest adoption scenario of 6 million electric vehicles in California, we find that across PG&E's service territory, 443 circuits will require upgrades (nearly 20% of all circuits) and merely 88 of these feeders have planned upgrades in the future. The costs of these feeders are an essential part of a utility's planning process, and this work speaks to the importance of electric vehicles on local distribution networks.

SUBMITTER: Jenn A

PROVIDER: S-EPMC8749456 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Distribution grid impacts of electric vehicles: A California case study.

Jenn Alan A Highleyman Jake J

iScience 20211228 1

California has adopted a substantial number of electric vehicles over the last decade but there are many challenges associated with the electrification of vehicles, including how they interact with the electricity grid. We employ real-world feeder circuit level data in California from PG&E to measure the capacity of local feeders. We model the adoption of electric vehicles down to the census block and take advantage of real-world vehicle charging data to simulate the future loading on circuits t ...[more]

PMID: 35036872

Similar Datasets

Assessing Short-Term Supply Disruption Impacts within Life Cycle Sustainability Assessment─A Case Study of Electric Vehicles.

Project description:In this article, the recently published SPOTTER approach, which allows for identifying potential supply disruption impacts along the entire supply chain within life cycle sustainability assessment in the short term (i.e., < 5 years), is applied to a case study addressing the cobalt and aluminum supply chains of electric vehicles (EVs) used in Switzerland. Existing studies within the field assessing supply disruption impacts for EVs and other technologies focus on impacts related to raw material supply and thus neglect impacts along full supply chains. The present study identifies hotspots and overall impacts along the full supply chains by analyzing six supply disruption events (i.e., geopolitical instability, child labor restrictions, trade barriers, price volatility, limited recyclability, and economic resource depletion) for two impact categories (i.e., cost variability and limited availability). Identified hotspots suggest that supply chains are potentially disrupted mainly through events occurring in Asian, African, or other developing countries and affecting the Western economies. The highest risks are indicated in relation to the supply of EVs, EV wiring, traction batteries, cobalt powder, and cobalt ore. Suitable measures to mitigate these supply risks are suggested showing that some of the suggestions could not have been made based on the results of existing studies.

| S-EPMC10702435 | biostudies-literature

Ensuring greenhouse gas reductions from electric vehicles compared to hybrid gasoline vehicles requires a cleaner U.S. electricity grid.

Project description:Emissions from electric vehicles depend on when they are charged and which power plants meet the electricity demand. We introduce a new metric, the critical emissions factors (CEFs), as the emissions intensity of electricity that needs to be achieved when charging to ensure electric vehicles achieve lifecycle greenhouse gas emissions parity with some of the most efficient gasoline hybrid vehicles across the United States. We use a consequential framework, consider 2018 as our reference year, and account for the effects of temperature and drive cycle on vehicle efficiency to account for regional climate and use conditions. We find that the Nissan Leaf and Chevy Bolt battery electric vehicles reduce lifecycle emissions relative to Toyota Prius and Honda Accord gasoline hybrids in most of the United States. However, in rural counties of the Midwest and the South, power grid marginal emissions reductions of up to 208 gCO2/kWh are still needed for these electric vehicles to have lower lifecycle emissions than gasoline hybrids. Except for the Northeast and Florida, the longer-range Tesla Model S battery-electric luxury sedan has higher emissions than the hybrids across the U.S., and the emissions intensity of the grid would need to decrease by up to 342 gCO2/kWh in some locations for it to achieve carbon parity with hybrid gasoline vehicles. Finally, we conclude that coal retirements and stricter standards on fossil fuel generators are more effective in the medium term at reducing consequential electric vehicle emissions than expansion of renewable capacity.

| S-EPMC10796910 | biostudies-literature

Managing grid impacts from increased electric vehicle adoption in African cities.

Project description:Electric vehicles are pivotal for global climate solutions, particularly in emerging markets like Africa. Despite the continent's clean energy potential, electric vehicle adoption faces unique challenges due to inefficiencies and reliability issues of distribution power grids. Here, we analyze the impacts of expanding electric vehicle fleets-private, commercial, and paratransit-on Nairobi's power grid. We simulate traffic patterns, charging behaviors, and transformer utilization using local mobility data. Our results show that while electric commercial and paratransit fleets may improve power system efficiency, widespread private EV adoption could significantly strain the grid, increasing peak loads and transformer aging. Smart charging strategies could mitigate these issues, reducing potential transformer replacement costs by up to 40%. Our study highlights the importance of tailored demand management and infrastructure planning to support EV growth in African cities, providing critical insights for policymakers, utilities, and transport planners to facilitate sustainable electric mobility transitions.

| S-EPMC11484782 | biostudies-literature

Energy, Emissions, and Cost Impacts of Charging Price Strategies for Electric Vehicles.

Project description:While utilizing price signals to affect charging behaviors has been identified as a promising strategy to manage charging loads, few studies discuss their impacts comprehensively. We investigate how different charging price strategies can affect the spatial and temporal distribution of charging activities at the individual level and the required charging infrastructure system. We utilize an integrated optimization platform for electric vehicle (EV) charging management and infrastructure placement in home and nonhome locations in San Diego, CA, that include charging price strategies, infrastructure costs, and mobility demand patterns. We evaluate three pricing scenarios and demonstrate that the time-of-use pricing scheme results in the highest emissions and the real-time one the lowest, which are 20.2% higher and 0.7% lower than the annual emissions under the flat rate scenario, which is about 8,787 MtCO2e. Our results show that the charging load profile is the result of various determinants including the dynamic electricity price, price elasticity of charging demand, travel and dwelling constraints, carbon price, as well as exclusive home and shared nonhome charging patterns. The effectiveness of changing charging behavior through internalizing climate damage to obtain environmental benefits depends largely on charging price strategies, implying that policymakers should consider charging price strategies in conjunction with carbon pricing rather than independently.

| S-EPMC9070358 | biostudies-literature

Extended DEMATEL method with intuitionistic fuzzy information: A case of electric vehicles.

Project description:The Decision-Making Trial and Laboratory (DEMATEL) methodology excels in the analysis of interdependent factors within complex systems, with correlation data typically presented in crisp values. Nevertheless, the judgments made by decision-makers often possess a degree of fuzziness and uncertainty, rendering the sole reliance on precise values inadequate for representing real-world scenarios. To address this issue, our study extends the DEMATEL approach to more effectively and efficiently handle intuitionistic fuzzy information, which denotes the factor correlation information from decision-makers in the form of intuitionistic fuzzy terms. The paper aggregates the intuitionistic fuzzy correlation information from each decision-maker, employing operators designed for managing intuitionistic fuzzy numbers. The significance and categorization of factors are determined through intuitionistic fuzzy matrix operations. Additionally, a causal and effect diagram is constructed to elucidate the distinct roles of these factors. Finally, this study illustrates the applicability of our proposed method with a real-world case in the context of electric vehicles (EVs). The study's results identify four cause factors and six effect factors within EV battery technology. The identification and categorization of these factors will assist EV companies in implementing targeted measures to foster the advancement of the battery technology.

| S-EPMC11658640 | biostudies-literature

Impacts of shared mobility on vehicle lifetimes and on the carbon footprint of electric vehicles.

Project description:Shared cars will likely have larger annual vehicle driving distances than individually owned cars. This may accelerate passenger car retirement. Here we develop a semi-empirical lifetime-driving intensity model using statistics on Swedish vehicle retirement. This semi-empirical model is integrated with a carbon footprint model, which considers future decarbonization pathways. In this work, we show that the carbon footprint depends on the cumulative driving distance, which depends on both driving intensity and calendar aging. Higher driving intensities generally result in lower carbon footprints due to increased cumulative driving distance over the vehicle's lifetime. Shared cars could decrease the carbon footprint by about 41% in 2050, if one shared vehicle replaces ten individually owned vehicles. However, potential empty travel by autonomous shared vehicles-the additional distance traveled to pick up passengers-may cause carbon footprints to increase. Hence, vehicle durability and empty travel should be considered when designing low-carbon car sharing systems.

| S-EPMC9613654 | biostudies-literature

Conventional, Hybrid, or Electric Vehicles: Which Technology for an Urban Distribution Centre?

Project description:Freight transport has an important impact on urban welfare. It is estimated to be responsible for 25% of CO2 emissions and up to 50% of particles matters generated by the transport sector in cities. Facing that problem, the European Commission set the objective of reaching free CO2 city logistics by 2030 in major urban areas. In order to achieve this goal, electric vehicles could be an important part of the solution. However, this technology still faces a number of barriers, in particular high purchase costs and limited driving range. This paper explores the possible integration of electric vehicles in urban logistics operations. In order to answer this research question, the authors have developed a fleet size and mix vehicle routing problem with time windows for electric vehicles. In particular, an energy consumption model is integrated in order to consider variable range of electric vehicles. Based on generated instances, the authors analyse different sets of vehicles in terms of vehicle class (quadricycles, small vans, large vans, and trucks) and vehicle technology (petrol, hybrid, diesel, and electric vehicles). Results show that a fleet with different technologies has the opportunity of reducing costs of the last mile.

| S-EPMC4509519 | biostudies-other

A spatiotemporal distribution prediction model for electric vehicles charging load in transportation power coupled network.

Project description:With increasing demand of electric vehicles (EVs), problems such as insufficient EV charging piles and unreasonable layout of EV charging stations are also becoming prominent. New challenges are introduced to the planning of urban EV charging infrastructures. To effectively plan the charging facilities, accurately predicting EV charging loads is essential. The present study proposes a spatio-temporal distribution prediction model for EV charging loads in transportation-power coupled network (TPCN). First, path planning is performed separately using the Dijkstra algorithm and refined origin-destination (OD) probability matrix based on the travel characteristics of various vehicle types. The charging selection model is then formulated considering multiple compelling factors, such as transportation conditions, ambient temperature, rest days and so on. Furthermore, the transportation-power coupled network is established based on the graph-theoretic analysis approach, and the spatial and temporal distribution characteristics of charging loads are predicted by Monte Carlo simulation. Finally, a case study is conducted in an actual urban region. The results show that EV charging load presents significant differences in different functional areas, different time periods and scenarios, and the proposed method can accurately predict the spatial-temporal distribution of charging load. This study represents a reliable approach for predicting charging demand in a certain region, and also provides powerful support for the rational planning of EV charging stations.

| S-EPMC11787364 | biostudies-literature

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

Project description:The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

| S-EPMC5411096 | biostudies-literature

Polar Grid Navigation Algorithm for Unmanned Underwater Vehicles.

Project description:To solve the unavailability of a traditional strapdown inertial navigation system (SINS) for unmanned underwater vehicles (UUVs) in the polar region, a polar grid navigation algorithm for UUVs is proposed in this paper. Precise navigation is the basis for UUVs to complete missions. The rapid convergence of Earth meridians and the serious polar environment make it difficult to establish the true heading of the UUV at a particular instant. Traditional SINS and traditional representation of position are not suitable in the polar region. Due to the restrictions of the complex underwater conditions in the polar region, a SINS based on the grid frame with the assistance of the OCTANS and the Doppler velocity log (DVL) is chosen for a UUV navigating in the polar region. Data fusion of the integrated navigation system is realized by a modified fuzzy adaptive Kalman filter (MFAKF). By neglecting the negative terms, and using T-S fuzzy logic in the adaptive regulation of the noise covariance, the proposed filter algorithm can improve navigation accuracy. Simulation and experimental results demonstrate that the polar grid navigation algorithm can effectively navigate a UUV sailing in the polar region.

| S-EPMC5539858 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data