EVSE Power Reallocation at Charging Stations Near Grid Capacity
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Solution Overview
Problem
The increasing prevalence of electric vehicles (EVs) poses a strain on grid infrastructure, particularly during peak hours, leading to potential grid overload, voltage instability, and equipment damage.
Innovation Solution
A method and system that utilize a cloud controller and an edge controller to manage EV charging at electric vehicle charging stations. The system determines if the total power consumption exceeds a certain percentage of the maximum power capacity and recalculates and reallocates power to each EVSE to prevent overload, implementing dynamic load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EV charging demand increases to meet growing EV prevalence, then EV charging accessibility improves, but grid infrastructure strain increases leading to overload and instability
Solution Approach 1:
The system dynamically adjusts power allocation to EVSEs based on real-time grid conditions. The edge controller continuously monitors total power consumption and recalculates power amounts for each EVSE when thresholds are exceeded, enabling the charging infrastructure to adapt flexibly to varying demand while maintaining grid stability.
Solution Approach 2:
The system changes the power consumption parameter of EVSEs dynamically. When total power consumption reaches a threshold (X% of maximum power capacity), the edge controller recalculates and adjusts the power amount for each EVSE, effectively changing operational parameters to prevent grid overload while continuing to serve EV charging needs.
2Device complexity
If cloud controller manages all EV charging operations centrally, then charging coordination is simplified, but response time to grid conditions deteriorates
Solution Approach 1:
The control system is segmented into two levels: cloud controller for high-level charging coordination and edge controller for real-time power management. This segmentation allows the cloud to handle strategic decisions while the edge handles tactical responses, reducing communication latency and improving response time to grid conditions.
Solution Approach 2:
The edge controller acts as an intermediary between the cloud controller and EVSEs. It receives charging requests from the cloud and translates them into real-time power allocation decisions based on local grid conditions, enabling faster local responses while maintaining centralized coordination.
3Productivity
If power allocation to EVSEs is increased to meet peak demand, then EV charging speed improves, but grid overload risk increases
Solution Approach 1:
The system implements feedback control by continuously monitoring total power consumption and comparing it against threshold values (X% of maximum power capacity). When the threshold is exceeded, the edge controller receives feedback and automatically recalculates power allocation, adjusting EVSE power amounts to prevent grid overload while optimizing charging speed within available capacity.
Data Source
AI summary
A method for charging electric vehicle (EV) at an electric vehicle charging station equipped with a number of electric vehicle supply equipments (EVSEs) is provided. Charging at least one EV by the EVSEs is performed under control of a cloud controller. Whether a total power consumption corresponding to the EVSEs is equal or larger than X % of a maximum power capacity is determined. X is a real number. When the determination is yes, amount of power for each EVSE is recalculated by an edge controller and the recalculated amount of power is assigned to each EVSE by the edge controller for charging the at least one EV by the EVSEs under control of the edge controller. Charging information of each EVSE is sent to the cloud controller by the edge controller when performing charging the at least one EV by the EVSEs under control of the edge controller.