Abstract: A system and method for providing real-time distributed micro-grid optimization using price signals 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 optimal pricing signals as well as strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, grid fortification, and revenue generation. Energy stores are then sent updated price signals and updated parameters as to the amount of energy to hold or release.

Abstract: 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.

Abstract: A system and method for providing real-time distributed micro-grid optimization using price signals 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 optimal pricing signals as well as strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, grid fortification, and revenue generation. Energy stores are then sent updated price signals and updated parameters as to the amount of energy to hold or release.

Abstract: 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.

Abstract: 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.