Preparing for a World with Electric Vehicles
As the number of electric cars increases, we are working on ways the grid can not only meet, but benefit from, this boost in power demand.
Integrating Electric Vehicles & the Grid
Vehicle-Grid Integration (VGI) research at Lawrence Berkeley National Laboratory (Berkeley Lab) consists of simulating, testing, and analyzing electric vehicles (EVs) as special loads for utility grids based on their charging flexibility and relatively high power levels. For example, VGI technologies can minimize grid costs and customer electricity bills through "smart charging" and mitigate distribution-level impacts and regional grid issues such as maintaining 60 Hz frequency.
VGI also enables EVs to function as bulk storage for utility grids by charging when the grid's renewable energy percentage is high. This is particularly valuable when supply exceeds demand, risking the supply's curtailment, as is happening increasingly in California and other high-renewable-electricity regions. Using an advanced vehicle-to-grid (V2G) system, EVs act as fully bi-directional storage devices, displacing the need for potentially large amounts of dedicated grid storage and offering microgrid integration opportunities. A microgrid consists of energy generation and energy storage that can power a building, campus, or community when not connected to the electric grid, for example, in the event of a disaster.
Berkeley Lab and University of California, Berkeley (UC Berkeley) scientists collaborate with researchers in other national labs, University of California campuses, and multiple public and private organizations on VGI research projects.
Data, Tools, and Facilities
Medium and Heavy-Duty Electric Vehicle Infrastructure – Load Operations and Deployment Modeling Tool
Supported by the California Energy Commission to inform actions to accelerate the decarbonization of medium and heavy-duty (MD/HD) vehicles in California, LBNL developed the HEVI-LOAD modeling tool to project the state-wide charging infrastructure needed to accommodate the growing number of MD/HD electric vehicles. HEVI-LOAD projects the number, type, and location of chargers, and the related electric grid supply requirements to support the new charging stations. Bin Wang, the head developer for HEVI-LOAD, is currently leading engagement with Western state utility stakeholders to expand the application of the HEVI-LOAD tool beyond California. HEVI-LOAD can be applied to other states seeking to make large scale investments in electric vehicle charging infrastructure.
The HEVI-LOAD tool presents data and analysis in simple formats, such as the Dashboard view in Figure 1 below. Using a web-based interface, users can view large- and small-scale results, such as the State- and County-Level Infrastructure Projections for total chargers, total electric vehicle population, and total energy requirements in 2030.
For information about licensing HEVI-LOAD, contact Bin Wang.
Figure 1: Sample Dashboard view of output from the HEVI-LOAD model showing estimates for parameters such as total EV chargers, EV vehicle market penetration and energy requirements.
DER-CAM is a powerful and comprehensive decision-support tool that primarily serves the purpose of finding optimal distributed energy resource (DER) investments in the context of either buildings or multi-energy microgrids. DER-CAM is publicly available and free to use.
DER-CAM was created by the Grid Integration Group (GIG) at Berkeley Lab. GIG is a leader in creating control and optimization solutions and demonstrating these solutions in real-world vehicle-grid and microgrid applications that reveal control, optimization, hardware, and software challenges that are not anticipated through simulation alone. Human behavior and system performance issues can often only be identified and addressed in the types of real-world tests and demonstrations performed by GIG.
Projects, Labs, and Groups
GIG works to make the evolving smart electric grid compatible with the requirements of electric system grid operators and electric utility companies while serving electricity customers' needs. The emergence of inexpensive sensing technology, the development of modern data-analytics methods, the widespread use of controllable loads and energy storage such as building HVAC systems and electric vehicles, all combine to offer a spectacular opportunity to reliably, resiliently, and securely de-carbonize the electricity grid.
The group is conducting the first-ever controlled study of the impact of providing vehicle-to-grid and vehicle-to-building services on vehicle batteries. The study will use new batteries in a fleet of Nissan LEAFs specially modified with bi-directional charging capability currently operating at the Los Angeles Air Force Base. A number of them will serve as control batteries that provide mobility only. The project will, in parallel, develop a scalable second-life battery energy storage solution for fleet applications.
The Microgrids and Vehicle-Grid Integration team studies customer adoption patterns of microgrid technologies and controllers to enable vehicle-grid integration. A microgrid consists of energy generation and energy storage that can power a building, campus, or community when not connected to the electric grid, for example, in the event of a disaster. Microgrids have obvious benefits in powering critical resources such as hospitals in the event of planned or unplanned grid outages. Renewable sources of generation such as photovoltaic have benefits over diesel generators. They don't emit greenhouse gases (GHGs) and other pollutants, and they don't require transport of fuel that may be restricted in a disaster event. Renewable microgrids can operate year-round to reduce energy costs and emissions and to provide emergency power resources.
GIG has created the Distributed Energy Resources Customer Adoption Model (DER-CAM) platform to optimally design, plan, and operate microgrids and has real-world experience developing optimization algorithms for microgrid control. GIG is a leader in creating control and optimization solutions and demonstrating these solutions in real-world vehicle-grid and microgrid applications that reveal control, optimization, hardware, and software challenges that are not anticipated through simulation alone. Human behavior and system performance issues can often only be identified and addressed in the types of real-world tests and demonstrations performed by GIG.
The GEMINI Project is a collaborative effort between the National Renewable Energy Laboratory (NREL) and Berkeley Lab. The project examines the extreme fast charging (XFC) impacts of future EV fleets on local distribution grids in the San Francisco Bay Area, culminating with a region-wide analysis. XFC is defined in the GEMINI project as individual charge points of 250+ kilowatts and charging depots with 1+ megawatts of coincident load at the site level.
The project integrated two complex modeling frameworks:
- The Behavior Energy Autonomy and Mobility (BEAM) model at Berkeley Lab
- The Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) and OpenDSS distribution-level grid modeling capabilities at NREL
It involves developing a complex network of interactive logic controllers and subsequent analysis in this now combined modeling framework, to respond to grid operational conditions, prices, and other signals and to manage the charging of future fleets of personally-owned and autonomous personal and ride-hail EVs in a large urban region.
The project is led by Matteo Muratori at NREL with Co-PIs Bryan Palmintier (NREL), Timothy Lipman (Transportation Sustainability Research Center (TSRC)/Berkeley Lab), and Haitam Laarabi (Berkeley Lab), along with a team of NREL and UC Berkeley researchers.
A slide presentation about GEMINI-XFC outlines the project.
Berkeley Lab has been involved in several collaborative efforts to simulate the impacts of future adoption of shared automated electric vehicles (SAEVs).
RISE (Routing and Infrastructure for Shared Electric vehicles) is an agent-based model developed by researchers at UC Berkeley and Berkeley Lab that simulates the activity of each vehicle in a fleet of electric vehicles seeking to serve passenger trip demand (e.g. bus or taxi service, ride-hailing, or SAEVs), along with charging and fleet-rebalancing activity. Given inputs for road network and trip demand, the model determines requirements for battery range, fleet size, and charging infrastructure, along with outputs for distance traveled both with and without passengers, wait times, energy consumption, and charger utilization.
GEM (the Grid-integrated Electric Mobility model), developed at Berkeley Lab in collaboration with University of California, Davis, UC Berkeley, Marain Inc, and Emerging Futures LLC, is a national scale simulation framework to analyze the impact of connecting high penetrations of electric vehicles to the U.S. grid at different levels of sharing and automation. GEM combines individual vehicle trips, parameterized agent-based model outputs from RISE, a cost model for vehicles and charging behavior, and a national-level U.S. electricity production cost model that includes both EV and non-EV loads. GEM co-optimizes the allocation of SAEV vehicles and charging infrastructure along with charge scheduling and economic dispatch of grid generators to find the minimal cost combination of vehicles, chargers, and operations to satisfy a given demand for trips. The result includes optimized EV fleets and associated charging infrastructure spanning both urban and rural areas, which serve the mobility needs of the entire U.S. population under various assumptions about automation, sharing, and charging strategies. We use GEM to explore how these parameters affect hourly electric load patterns, peak power demand, EV battery capacity, fleet size requirements, charger power levels and quantity, total cost of fleet ownership, renewables curtailment, and GHG emissions, in a future personal transportation system composed of any combination of privately-owned EVs and centrally-managed SAEVs.
Manhattan Taxi Study
Researchers have evaluated the potential impact of a fleet of self-driving electric vehicles operating in the Manhattan borough of New York City, finding that such a fleet could do the same job as present-day taxis but with lower costs, less pollution, and reduced energy use. The study found that a fleet of such taxis drawing power from the current New York City power grid would reduce greenhouse gas emissions by 73% and energy consumption by 58% compared to a fleet of automated conventional gas-powered vehicles.
(See news article on the study: "Fleet of Automated Electric Taxis Could Deliver Environmental and Energy Benefits.")
This project aims to develop efficient numerical solutions to solve the tightly coupled grid-transportation co-optimization problem, where electric vehicles serve as the mobile storages to bridge the two systems. The problem being investigated is at unprecedented high fidelity and scalability by considering diverse travel and energy-demand patterns of multiple vehicle types, real-world transportation systems, and electric grid networks. We are leveraging the parallel simulation and high-performance computing (HPC) techniques at Berkeley Lab to compute the optimal solutions that will specifically provide guidance on 1) reliable and cost-effective planning of charging infrastructures, 2) time- and cost-aware operations of EV fleets and chargers and 3) energy, environmental and system resilience impact analysis.
Collaborations and Partnerships
TSRC was formed to study the economic, social, environmental, and technological aspects of sustainable transportation. It is housed at the Institute of Transportation Studies at the University of California, Berkeley.
TSRC does extensive research on the intersection of transportation and other key infrastructure systems. This includes utility electrical grids and intelligent transportation systems (ITS), such as ecodriving, real-time traveler and refueling information, the future of ITS (20 to 40 years), and augmented speed enforcement.
The National Renewable Energy Laboratory (NREL) is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
NREL is transforming energy through research, development, commercialization, and deployment of renewable energy and energy-efficiency technologies.
Emerging Futures, LLC was founded in 2016 as a research and analysis consultancy and is based on the U.S. West Coast. Its interests currently focus on two broad areas: climate change mitigation and the commercial expansion of humanity into space.
Drawing on the more than 20 years' combined experience of team members in research, analysis, simulations, and policy design, Emerging Futures works with researchers, government agencies, nonprofits, and companies to identify large-scale, net-zero emissions solutions for building a sustainable future. Climate change mitigation research areas include low-carbon transportation, hydrogen and low-carbon fuels, renewable and low-carbon electricity, life-cycle assessment, and non-energy climate strategies, policy and integrated assessment.
Marain Inc. is a mobility software company based in Palo Alto, California, with its origins in Stanford's Autonomous Systems Lab. The company develops software to provide planning, analysis, simulation, and in-the-loop real-time electricity optimization capabilities for operators of electric automated mobility on-demand (EAMoD) fleets. Delivered electricity service will become a dominant, volatile, complex, and location-specific cost for operators of these fleets. Marain uses state-of-the-art predictive models to help fleet operators minimize these costs by making decisions at multiple timescales. Marain also helps other stakeholders — such as charging infrastructure providers, utilities, and cities — explore load growth, usage patterns, pricing strategies, and policy design. Marain's team includes pioneers in the field of EAMoD control and optimization from Stanford University, the Massachusetts Institute of Technology (MIT), Harvard University, the Technical University of Munich, and the Swiss Federal Institute of Technology in Zurich (ETH Zurich).