EV Charging Demand Prediction for Grid Load Balancing
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Solution Overview
Problem
Charging electric vehicles takes longer than refueling internal combustion engines, leading to longer idle times and potential overloading of local power supply networks due to insufficient charging infrastructure and power capacity.
Innovation Solution
A method to predict charging power demand by recording movement and energy data of electric vehicles in a data cloud, enabling spatial and temporal distribution analysis to adjust energy supply and infrastructure to meet demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If charging power is increased to reduce charging time, then charging speed is improved, but the power supply network may be overloaded
Solution Approach 1:
The system performs preliminary analysis of charging demand by collecting and processing movement data, battery state data, and historical charging data to predict future charging power requirements. This advance planning allows the power supply network to prepare and adjust capacity before peak demand occurs, enabling high charging speeds without overloading the network.
Solution Approach 2:
The charging power is made dynamic rather than static. The system continuously monitors and adjusts charging power based on real-time network capacity, predicted demand patterns, and individual vehicle requirements. This dynamic adjustment allows the system to optimize charging speed while adapting to changing network conditions and avoiding overload.
2Loss of time
If more charging stations are built to reduce waiting time, then charging accessibility is improved, but infrastructure cost increases
Solution Approach 1:
The system uses historical charging data and movement patterns to predict future charging demand at various locations. This preliminary analysis identifies high-demand areas where charging infrastructure expansion is most needed, allowing planners to prioritize investments in locations that will have the greatest impact on reducing waiting times while avoiding unnecessary infrastructure in low-demand areas.
Solution Approach 2:
The system applies different charging strategies to different locations based on local demand characteristics. High-demand urban areas receive priority infrastructure investment and higher power capacity, while rural or low-demand areas receive appropriate but scaled-back infrastructure. This localized approach optimizes the balance between reducing waiting times and controlling infrastructure costs.
3Power
If charging power is increased to meet demand, then energy supply capacity is improved, but energy consumption by other consumers must be reduced
Solution Approach 1:
The system implements periodic or time-based charging strategies where charging power is increased during off-peak hours when other consumers have lower energy demands, and reduced during peak hours when other consumers need more energy. This temporal distribution allows the system to meet overall charging demand without requiring simultaneous high power delivery that would force reductions in other consumer energy usage.
Data Source
AI summary
The embodiment relates to a method for predicting a charging power demand for electric vehicles (202). In the method, movement data (204) describing current and/or expected movements of the electric vehicles (202) and energy demand data (205) on planned and/or expected required charges of electric energy for one electric vehicle (202) at a time are recorded in a data cloud (201). The movement data (204) and the energy demand data (205) are used to determine an anticipated spatial and temporal distribution of a charging power demand for supplying the electric vehicles (202) with electrical energy.