EV Charging Aggregation Optimization via Quadratic Programming
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Solution Overview
Problem
Existing methods for charging electric vehicles in aggregation fail to efficiently manage charging power to minimize impact on the power grid and optimize charging costs, leading to inefficiencies and potential strain on the grid during peak times.
Innovation Solution
A method and system that utilize a master controller and sub-controllers to obtain and adjust charge power curves based on coordinating information and charging cost curves, employing quadratic programming to optimize charge power distribution among electric vehicles, ensuring efficient charging that converges quickly and minimizes grid impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If charging power is increased during peak times to meet vehicle charging demands, then charging speed and user satisfaction are improved, but grid strain and power system stability deteriorate
Solution Approach 1:
The patent implements periodic action by adjusting charging power based on time-of-day patterns and grid conditions. The system uses time-based pricing signals and coordinated charging schedules to shift charging loads away from peak periods, creating periodic charging patterns that satisfy user needs while protecting grid stability during high-demand times.
Solution Approach 2:
The patent applies preliminary action through advance charging scheduling and reservation systems. Users can pre-book charging slots during off-peak periods, and the system proactively manages charging queues to ensure vehicles are charged before peak demand occurs, eliminating the need for high-power charging during critical grid periods.
2Object-affected harmful factors
If charging power is decreased during peak times to reduce grid impact, then grid stability is improved, but charging efficiency and user satisfaction deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through real-time monitoring of grid conditions, charging station status, and vehicle battery levels. The system continuously adjusts charging power based on feedback signals from the grid operator and charging management platform, optimizing the balance between grid stability and charging efficiency through dynamic response to changing conditions.
Solution Approach 2:
The patent applies dynamics by making charging power flexible and adaptive rather than fixed. The system dynamically adjusts charging rates based on real-time grid conditions, vehicle urgency levels, and pricing signals, allowing charging efficiency to be optimized when grid conditions permit while automatically reducing power when stability concerns arise.
3Adaptability or versatility
If centralized control is used to optimize aggregate charging, then grid coordination is improved, but computational complexity and system response time deteriorate
Solution Approach 1:
The patent implements segmentation by dividing the charging management system into hierarchical layers: a centralized coordination layer for aggregate optimization and local control layers at individual charging stations for real-time execution. This segmentation allows grid coordination benefits to be achieved while distributing computational tasks to reduce overall system complexity and improve response time.
Solution Approach 2:
The patent introduces an intermediary charging management platform that acts as a mediator between the grid operator and individual charging stations. This intermediary handles the computational complexity of aggregate optimization and translates it into simplified control signals for local execution, reducing the computational burden on both the centralized system and individual charging points while maintaining effective grid coordination.
4Loss of energy
If iterative optimization is performed to achieve ideal charge power curves, then charging cost optimization is improved, but calculation time and computational resources deteriorate
Solution Approach 1:
The patent applies partial action by implementing iterative optimization with early termination criteria. The system performs optimization iterations to improve charging cost efficiency but automatically stops when a predetermined performance threshold is achieved or when computational time exceeds a set limit, providing sufficient cost optimization without excessive calculation time that would degrade user experience.
Data Source
AI summary
Method and system for charging electric vehicles in an aggregation is provided. The method includes: obtaining a plurality of first charge power curves of a plurality of electric vehicles in the aggregation; obtaining a coordinating information of each of the plurality of electric vehicles from the plurality of first charge power curves; obtaining a first feedback charge power curve of each of the plurality of electric vehicles from the coordinating information and a charging cost curve of each of the plurality of electric vehicles; judging whether the first feedback charge power curve is same with the first charge power curve of each of the plurality of electric vehicles; if yes, charging each of the plurality of electric vehicles in accordance with the first charge power curve.