Vehicle Energy Dispatch for Outage-Driven Home Power Support
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Solution Overview
Problem
Existing systems fail to efficiently manage and optimize energy distribution and utilization during power outages, particularly in locations where vehicles with energy storage capabilities can provide critical support to homes and infrastructure.
Innovation Solution
A vehicle system that predicts power outages and directs energy storage units to power priority devices at a location using AI and machine learning, optimizing routes and energy distribution among a network of vehicles with renewable energy sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vehicles with energy storage capabilities are used to provide energy during power outages, then energy availability at critical locations is improved, but battery depletion and energy resource management become problematic
Solution Approach 1:
The system performs preliminary actions by calculating the time until vehicle arrival and the time until device thresholds are exceeded before the power outage fully impacts devices. This allows the system to proactively manage energy distribution, ensuring vehicles charge energy storage units at optimal moments before critical depletion occurs, thereby improving energy availability while preventing excessive battery depletion.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring device status, battery levels, and vehicle arrival times. The processor uses this feedback to dynamically adjust energy distribution decisions, comparing the first time amount (vehicle arrival) with the second time amount (threshold exceedance) to optimize when energy transfer occurs, thus balancing energy availability with battery conservation.
2Productivity
If decentralized energy management using blockchain and smart contracts is implemented, then energy distribution efficiency is improved, but system complexity increases
Solution Approach 1:
The system applies self-service through autonomous vehicles that independently execute energy distribution decisions using onboard processors and smart contracts. Vehicles autonomously calculate arrival times, assess energy needs, and transfer power without centralized control, improving distribution efficiency while managing complexity through standardized autonomous protocols rather than complex centralized coordination.
Solution Approach 2:
The system achieves universality by using blockchain technology and smart contracts as a universal platform that enables multiple vehicles to participate in energy distribution with standardized rules. This multi-functional approach allows different vehicles with varying energy capacities to contribute to the same grid, improving overall efficiency while maintaining manageable system complexity through standardized protocols.
3Reliability
If real-time decision-making using AI and machine learning is implemented, then energy distribution optimization is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary computational actions by pre-calculating key parameters such as vehicle arrival times and device threshold exceedance times before real-time decision-making is required. This preliminary processing reduces the computational burden during critical moments, allowing AI and machine learning models to focus on optimization rather than basic calculations, thereby improving reliability without excessive processing delays.
Data Source
AI summary
An example operation includes one or more of determining, by a vehicle, a first amount of time until the vehicle arrives at a location experiencing a power outage, determining, by the vehicle, a second amount of time until a threshold associated with one or more devices in the location is exceeded, and the second amount of time is less than the first amount of time, and directing, by the vehicle, an energy storage unit associated with the location to power the one or more devices.