Community Energy Storage Strategy Using EV Battery Aging Models
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Solution Overview
Problem
The increasing integration of renewable energy sources and electric vehicles in power grids leads to volatility in energy supply and demand, causing instability in electricity prices and potential power outages, while existing V2G solutions neglect battery aging, resulting in inefficient energy management and premature battery degradation.
Innovation Solution
Implementing an edge computing device with a household optimization engine that receives community optimization strategies from cloud computing, incorporating battery aging models to optimize energy storage and charging/discharging schemes for electric vehicles, thereby minimizing energy costs and battery degradation while balancing supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If V2G solutions are implemented to balance supply and demand, then grid stability is improved, but battery degradation accelerates
Solution Approach 1:
The system dynamically adjusts charging/discharging parameters (rate, depth, timing) based on real-time battery state monitoring and grid conditions. By changing operational parameters adaptively, the system optimizes the balance between providing grid support services and minimizing battery degradation, thus resolving the contradiction between grid stability improvement and battery life preservation
Solution Approach 2:
The system implements continuous monitoring of battery state (charge level, temperature, health) and uses this feedback to adjust V2G operations. The feedback loop enables real-time optimization of charging/discharging cycles to prevent excessive degradation while maintaining grid stability, addressing both competing requirements simultaneously
2Object-affected harmful factors
If renewable energy share is increased to reduce emissions, then environmental impact is improved, but supply volatility increases
Solution Approach 1:
The system uses electric vehicle batteries as intermediary energy storage devices between renewable sources and the grid. These batteries absorb excess renewable energy when production exceeds demand and release energy when production falls short, thus mediating the volatility inherent in renewable energy systems while enabling higher renewable penetration and reducing emissions
Solution Approach 2:
The system performs preliminary charging of EV batteries during periods of high renewable generation (when supply exceeds demand) before renewable output decreases. This advance energy storage action ensures energy availability during low-generation periods, mitigating supply volatility while maximizing utilization of renewable energy
3Use of energy by moving object
If EV charging is increased to meet demand, then energy consumption is improved, but power demand peaks increase
Solution Approach 1:
The system implements periodic charging schedules for EVs that distribute charging loads across different time periods rather than concentrating them during peak demand hours. By using off-peak and mid-day charging periods, the system ensures EV energy needs are met while avoiding exacerbation of power demand peaks, thus resolving the contradiction between energy consumption fulfillment and peak power management
Data Source
AI summary
The disclosure relates to a device configured to optimize an energy storage strategy of a community comprising a plurality of households. Further, the disclosure relates to a cloud computing device configured to optimize an energy storage strategy of a community comprising a plurality of households. Further, the disclosure relates to an electric vehicle associated with a household in a community comprising a plurality of households, the electric vehicle comprising a battery aging model indicative of a battery aging status of a battery pack of said electric vehicle. Further, the disclosure relates to methods directed to optimize energy storage of households and communities.