After a long time of not updating this news section, there are many exciting updates to share:
The new DOE vehicle-grid integration project (#2 above) has the following central objectives:
Within this project, the vehicle-grid simulations will be applied towards several targeted case studies to assess VGI feasility:
Finally, the VGI simulation tools will be applied towards developing strategies to aggregate and control large collections of vehicles by:
The project will last three years.
New V2G-Sim study demonstrates that EV charging loads can be reduced by over 75% during a demand response event without adversely affecting driver mobility needs
A new peer reviewed study published in the Society of Automotive Engineers 2015 World Congress quantifies the flexibility for electric vehicles (EVs) to respond to demand response (DR) events. Within this study, a smart charging controller was developed and integrated into V2G-Sim to explore the flexibility for EVs to respond to DR signals. The smart charging controller considers the individual mobility needs of each driver/vehicle to decide whether the vehicle can respond to demand response events.
Using the new controller algorithm, different DR events were simulated for a collection of over 3,000 vehicles to explore the magnitude of EV charging loads that could be reduced without adversely affecting the mobility needs of drivers. DR events were simulated to occur at a variety of different times during the day, and with duration spanning 1 hour, 2 hours, or 4 hours. It was found that EVs can reduce over 75% of their charging loads during a DR event without adversely affecting the mobility needs of any driver. Further, the study explored how the results may change if the travel itineraries for individual drivers were highly uncertainty. Even with substantial levels of uncertainty in travel needs, it was found that over 65% of EV charging loads could be removed during a DR event.
V2G-Sim to be applied in Berkeley Lab project to forecast the impact of PEVs on California's energy future
The California Energy Commission recently announced an award to Berkeley Lab for an analysis project that seeks to build a healthier and more robust future for California, by identifying low carbon energy scenarios for California through to the year 2050. V2G-Sim will be used alongside other simulation tools within the project. In particular, V2G-Sim will be applied to provide temporally and spatially resolved grid charging demand profiles from growing numbers of plug-in electric vehicles throughout California. The project was formulated as part of a proposal from Berkeley Lab in response to CEC PON-14-309.
V2G-Sim provides foundation for new vehicle-grid integration research award from California Energy Commission
The California Energy Commission recently announced an award to Berkeley Lab for a pilot project to demonstrate smart charging of plug-in electric vehicles at Alameda County's publicly accessible charging locations. The project, which was proposed in response to a proposal submitted for CEC PON-14-301, will leverage V2G-Sim to forecast the flexibility for vehicles to deliver smart charging services to building facilities and the grid without compromising the mobility needs of drivers.
V2G-Sim and MyGreenCar were featured in recent invited presentations for 1) the California vehicle-grid integration multi-agency research exchange, 2) DOE's Grid Integration Technical Team, 3) the California Energy Commission, 4) US EPA and DOE, and more...
At a recent launch event at the Los Angeles Air Force Base, the vehicle-to-grid integration project led by Berkeley Lab with funding from DOD and CEC was announced. 42 plug-in vehicles are being integrated to the grid to offer ancillary services in the CAISO wholesale market.
A press release is available here.
V2G-Sim and MyGreenCar were the featured topics at a presentation by Dr. Samveg Saxena at the BERC Energy Summit on Thursday October 16. The presentation, entitled "Charging Ahead on Clean Transportation by Eliminating EV Range Anxiety and Predicting Personalized Fuel Economy" was attended by an audience of nearly 150 people including students, faculty, researchers, entrepreneurs, and energy enthusiasts. The presentation described how today's EVs meet the vast majority of the needs of US drivers, and how V2G-Sim and its end-use application (called MyGreenCar) can be used to eliminate EV range anxiety and provide personalized fuel economy estimates for any driver in any vehicle (analogous to a personalized fuel economy label).
V2G-Sim predicts both the most likely charging load, but also provides an uncertainty estimate on grid loads arising from individual drivers taking unexpected trips, or taking longer time to complete an individual trip. In order for users to calibrate input statistics for a given region or fleet, automated methods have been built into V2G-Sim to derive input statistics from a deterministic data source. For instance, a user can derive the input statistics from the San Francisco Bay Area segment of the National Household Travel Survey and then conduct scenario analysis on grid impacts by systematically changing input statistics between each V2G-Sim run.
A new case study has been added which demonstrates how V2G-Sim can be used to spatially resolve how PEVs will interact with the electricity grid. V2G-Sim models vehicle-grid interactions on a second-by-second timescale at the individual vehicle level. With information on where each vehicle is located, vehicle-grid interactions can be spatially resolved at any scale. The case study illustrates two cases, one where vehicle charging is resolved by location type within a given area, and another where vehicle charging is resolved state by state on the national level. With this methodology, V2G-Sim can also enable spatial resolution on the distribution system scale, for example to resolve how EV charging loads will impact individual transformers within a utility company's service territory.
Vehicle batteries are known to lose capacity with time and cycling, and prior research has commonly assumed that EV batteries will provide useful life within EVs down to 70-80% remaining energy storage capacity. Around this level of remaining capacity, it is assumed that EV batteries must be retired because they will no longer meet the range and mobility needs of drivers. Many studies have proposed that after retirement from their vehicle lifetime, vehicle batteries can enter a second life where they serve as stationary energy storage for the electricity grid.
A new V2G-Sim case study quantitatively examines the assumption of having to retire EV batteries from vehicles once they reach 70-80% remaining capacity. Driving patterns from drivers across the United States are simulated in V2G-Sim within vehicles that have experienced different levels of battery degradation. Battery SOC profiles from these simulated vehicles are examined to identify if drivers run out of charge during their trips, and to determine how much range drivers would have if they needed to make an unexpected trip.
The results from this case study show that EV batteries continue to meet the needs of a vast majority of drivers well beyond degradation levels of 70-80% remaining capacity. These results suggest that battery degradation may be an over-emphasized concern in terms of meeting the mobility needs of drivers. EV batteries may well continue to serve the needs of drivers for at least as long as the lifetime of the entire vehicle. These results also challenge the assumptions of a majority of battery second life economic analysis literature because it is shown that batteries entering their second life for grid storage may have much less than 70-80% remaining capacity.
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