Bidirectional EV Charging Stations for Grid Resilience
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Solution Overview
Problem
The electrical grid faces challenges in balancing demand and supply due to the intermittent nature of renewable energy sources and the difficulty in storing electricity, leading to reliability issues and disproportionate impacts on lower-income communities during events like blackouts and brownouts, which current EV charging systems cannot adequately address.
Innovation Solution
A system and method enabling bi-directional electricity usage from a distributed network of energy storage stations, utilizing machine learning optimization to strategically balance energy across regions, incorporating data from grid telemetry, traffic, and socio-economic factors to optimize energy storage and release, and providing energy reserves during peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electricity is generated as needed without storage, then the system simplicity is maintained, but the reliability of supply deteriorates during peak demand or failure events
Solution Approach 1:
The patent segments the energy storage function into distributed battery units deployed at multiple locations across the grid, including in EV charging stations. Each battery unit operates semi-independently, providing localized backup power and frequency regulation services, thereby improving supply reliability without requiring a single complex centralized storage system.
Solution Approach 2:
The patent makes EV charging stations multi-functional by enabling them to serve both as customer charging outlets and as distributed energy storage resources. The battery units in these stations can provide grid services including frequency regulation, backup power during outages, and peak demand support, thereby improving reliability while utilizing existing infrastructure.
2Adaptability or versatility
If EV charging systems only allow unidirectional power flow from grid to EV, then the charging system simplicity is maintained, but the grid balancing capability deteriorates during peak demand
Solution Approach 1:
The patent inverts the traditional unidirectional power flow by enabling bidirectional charging capability. EV batteries can discharge power back to the grid during peak demand periods through vehicle-to-grid (V2G) technology, allowing the EV fleet to act as a distributed energy resource that enhances grid balancing and provides peak shaving services.
Solution Approach 2:
The patent implements dynamic control of power flow direction based on real-time grid conditions. The charging station controller monitors grid demand and battery state of charge, dynamically switching between charging mode (grid to EV) and discharging mode (EV to grid), thereby adapting the system behavior to optimize grid support while managing complexity through intelligent control.
3Reliability
If energy storage is concentrated in single locations, then the system simplicity is maintained, but the resilience to localized failures deteriorates
Solution Approach 1:
The patent divides the energy storage function into numerous small distributed battery units located across multiple geographic sites, including EV charging stations and other strategic locations. This segmentation ensures that localized failures or disasters only affect specific areas while the broader grid remains operational, thereby enhancing overall grid resilience.
Solution Approach 2:
The patent places energy storage resources strategically at local levels where they can provide immediate backup power and frequency regulation services to local grid segments. Each distributed battery unit is sized and positioned to address specific local needs, providing tailored resilience solutions that match local grid characteristics and vulnerability profiles.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances grid resilience and risk mitigation by allowing energy storage stations to act as a distributed resource, improving energy equity and reducing the social and economic costs of grid failures, while supporting EV charging and grid balancing operations.
Implementation Method 1
convert the AC power to DC power using an AC-DC power converter
Implementation Method 2
convert the DC power in the high-voltage battery pack to AC power using an DC-AC power converter
Implementation Method 3
provide electrical isolation between the AC grid and DC connected internal components
Data Source
AI summary
A system and method for providing risk mitigation and resilience to the electrical grid system by allowing bi-directional electricity usage from a distributed network of energy storage stations to form a large, distributed resource for the grid. A machine learning optimization module ingests various forms of data—from grid telemetry to traffic data to trip-to-trip data and more—in order to make informed spatiotemporal decisions about strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, and grid fortification. Energy stores are then sent updated parameters as to the amount of energy to hold or release.