EVSE Energy Management Using Centralized Baselines and Local Updates
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Solution Overview
Problem
The increasing adoption of Electric Vehicles (EVs) poses challenges to the electric vehicle supply equipment (EVSE) network, including reduced power dispensing during peak hours, dynamic pricing, and potential power outages, due to the intermittent nature of renewable energy sources and grid limitations, necessitating advanced energy management strategies.
Innovation Solution
An AI-based energy management system integrating centralized and decentralized control strategies, energy storage systems, and distributed energy resources to optimize power flow, predict demand, and adjust charging/discharging schedules, ensuring reliable and cost-effective power dispensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy storage systems are integrated with renewable energy sources to increase reliability, then the reliability of the hybrid system is improved, but the device complexity increases
Solution Approach 1:
The patent combines energy storage systems with renewable energy sources (wind and photovoltaic) to create a hybrid system. This merging allows the system to store energy when generation exceeds demand and discharge when generation is insufficient, thereby improving reliability while managing the complexity through integrated design.
Solution Approach 2:
The energy storage system performs preliminary action by storing energy in advance when renewable generation is high and prices are low. This pre-stored energy is then available to meet demand during periods when renewable generation is insufficient, proactively ensuring system reliability before reliability issues arise.
2Productivity
If electric vehicle supply equipment dispenses power at higher wattage during peak hours, then the productivity is improved, but the loss of energy increases due to grid limitations
Solution Approach 1:
The system performs preliminary charging of electric vehicles during off-peak hours when energy is abundant and prices are low. By charging vehicles in advance rather than during peak demand periods, the system avoids energy losses associated with peak-hour power dispensing while still meeting customer needs.
Solution Approach 2:
The energy storage system enables continuous useful action by providing power to EVSE during peak hours from stored energy, maintaining productivity without drawing additional power from the grid during high-demand periods when energy losses would occur.
3Loss of energy
If dynamic or tiered pricing is implemented to discourage power dispense during peak hours, then the loss of energy is reduced, but the ease of operation decreases
Solution Approach 1:
The system implements self-service through automated dynamic pricing mechanisms that automatically adjust pricing based on demand conditions. The AI algorithms autonomously manage pricing strategies without requiring manual intervention, reducing energy losses through peak-hour pricing while maintaining ease of operation through automation.
Solution Approach 2:
The system uses feedback from grid conditions and demand patterns to dynamically adjust pricing in real-time. This feedback mechanism allows the system to automatically respond to changing conditions, reducing energy losses during peak hours while maintaining operational simplicity through automated decision-making.
4Productivity
If AI algorithms are used to optimize power flow and manage energy storage, then the productivity is improved, but the device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or manual energy management systems with AI-based computational algorithms. This substitution uses software intelligence to optimize power flow and manage energy storage, improving productivity through sophisticated decision-making while managing complexity through virtual rather than physical systems.
Solution Approach 2:
The AI algorithm performs multiple functions including power flow optimization, energy storage management, pricing strategy development, and demand prediction. This multi-functionality consolidates what would otherwise require multiple separate systems into a single intelligent platform, improving productivity while containing complexity through integration.
Data Source
AI summary
Techniques are provided for dividing control of energy flow at multiple Electric Vehicle (EV) stations using the combination of a centralized controller and a plurality of decentralized controllers. The centralized controller is configured to execute algorithms to generate centralized predictions, related to energy usage at stations, for a first period of time, and to generate one or more centralized baseline signals based on the centralized predictions. Each decentralized controller is configured to receive the centralized baseline signal(s), monitor interactions at a subset of stations during the first period of time, and update the centralized baseline signal(s) in real-time based on the interactions to produce locally-updated baseline signal(s). The locally-updated baseline signal(s) are communicated to the subset of stations, and energy flow is controlled at the subset of stations based on the locally-updated baseline signal(s).