EV Micro Grid Identification via Traffic Simulation
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Solution Overview
Problem
Accurately estimating the spatio-temporal availability of energy from electric vehicles (EVs) for forming micro grids in city-scale traffic scenarios is challenging due to the difficulty in obtaining trip attributes and residual battery charge data for all vehicles.
Innovation Solution
A processor-implemented method that partitions a region into cells, determines base loads, uses a calibrated traffic micro-simulator to extract trip information, maps vehicle data to cells, models battery state of charge, configures V2G policy variables, and computes V2G power supply to identify potential micro grids based on comparison with base loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trip attributes and battery charge data are collected for all vehicles in city-scale traffic, then accuracy of micro grid prediction is improved, but data acquisition complexity and cost increase significantly
Solution Approach 1:
The patent segments the city-scale traffic into multiple zones and further divides vehicles into different types (e.g., commuter vehicles, delivery vehicles, taxis) with distinct travel patterns. This segmentation allows the system to apply different estimation models to different segments, reducing overall complexity while maintaining prediction accuracy for each segment.
Solution Approach 2:
The patent uses calibrated traffic micro-simulators that create virtual copies of real traffic scenarios. These simulated vehicles replicate the behavior and energy consumption patterns of actual vehicles, allowing the system to estimate micro grid potential without requiring direct data collection from every physical vehicle.
2Measurement precision
If detailed trip attributes are obtained for all vehicles, then spatio-temporal energy availability prediction is improved, but time and computational resources required increase
Solution Approach 1:
The patent performs preliminary calibration of traffic micro-simulators using historical traffic data and vehicle characteristics before actual prediction. This pre-calibration establishes validated models that can quickly estimate energy availability without requiring real-time detailed tracking of every vehicle trip attribute.
Solution Approach 2:
The patent collects and processes only the most critical trip attributes (such as origin-destination pairs, trip duration, and vehicle type) rather than all possible vehicle parameters. This partial action approach provides sufficient accuracy for micro grid identification while significantly reducing data processing time and computational burden.
3Power
If V2G power supply from multiple vehicles is aggregated, then potential micro grid capacity increases, but system coordination and control complexity increases
Solution Approach 1:
The patent merges individual vehicle V2G capabilities at the zone level by aggregating power supply potential from multiple vehicles within identified micro grid zones. This merging approach allows the system to evaluate collective V2G capacity without managing each vehicle individually, reducing coordination complexity while maximizing available power.
Solution Approach 2:
The patent introduces a zone-level aggregation layer that acts as an intermediary between individual vehicles and the central control system. This intermediary consolidates V2G potential from multiple vehicles before presenting it to the micro grid identification algorithm, simplifying the coordination architecture and reducing communication overhead.
Data Source
AI summary
Vehicle-to-Grid (V2G) technologies are being adopted to reduce peak demand and to take over as energy sources during grid instability. It is necessary to estimate attributes of electric vehicle trips and residual battery charge in order to correctly predict spatio-temporal availability of energy from EVs to form a micro grid. However, it may not be feasible to get the required attributes for all vehicles in a city-scale traffic scenario. Embodiments of the present disclosure and system address the problem of accurately estimating the local energy reserve that is available from parked EVs during a given time of the day. In addition, the system also determines which neighborhoods have the potential to form micro grids using the parked EVs during a given time period. This will help grid operator(s) to plan and design smart grids which can create EV-powered micro grids in neighborhoods during periods of peak demand or during disruptions.