EV Charging Optimization via Receding Horizon Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The rapid development of electric vehicles poses challenges to power systems, including decreased power quality, increased system losses, and potential instability, especially when charging occurs disorderly, peaking during system peaks and troughs.
Innovation Solution
A method and device that optimize electric vehicle charging by predicting charging behavior and establishing a real-time receding-horizon optimization model to adjust charging power, increasing it during system troughs and decreasing it during peaks, ensuring efficient and stable power system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electric vehicles are charged without optimization, then charging simplicity is maintained, but power quality deteriorates and system stability is endangered
Solution Approach 1:
The system performs preliminary forecasting of electric vehicle charging behavior, including charging time, charging power, and vehicle arrival/departure patterns. This advance prediction enables the optimization model to pre-plan charging schedules that avoid power system peaks, thereby improving stability before the actual charging occurs.
Solution Approach 2:
The charging optimization model dynamically adjusts charging power based on real-time power system state and predicted vehicle behavior. The system continuously updates charging schedules using receding horizon optimization, making the charging process adaptive rather than static, which resolves the contradiction between maintaining simplicity and ensuring stability.
2Productivity
If charging power is increased during system peaks, then charging speed is improved, but power system stability is endangered
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring power system state and comparing it with predicted charging demands. The receding horizon optimization model uses this feedback to adjust charging schedules in real-time, ensuring that charging power is increased during system troughs and decreased during peaks, thereby achieving both fast charging and system stability.
Solution Approach 2:
The optimization model changes charging power parameters dynamically based on power system conditions and predicted vehicle behavior. By adjusting charging power levels according to system state and forecasted demands, the system achieves high charging productivity during favorable conditions while maintaining stability during critical periods.
3Ease of operation
If charging is concentrated during peak periods, then user convenience is improved, but system losses increase
Solution Approach 1:
The system predicts vehicle arrival times and charging requirements in advance, allowing it to schedule charging during system troughs before vehicles actually arrive at charging stations. This preliminary planning reduces system losses while maintaining user convenience by ensuring vehicles are charged and ready when needed.
Solution Approach 2:
The receding horizon optimization implements periodic updates to charging schedules, adjusting charging power in a periodic manner based on system state and predicted behavior. This creates a rhythm of charging activity that aligns with power system cycles, concentrating charging during troughs and reducing it during peaks, thereby minimizing energy losses.
Data Source
AI summary
A method and a device for charging an electric vehicle in a power system are provided. The method includes: obtaining a first electric vehicle connected to the power system, and obtaining a rated charging power and a first charging requirement; determining a first charging period corresponding to the first electric vehicle; determining a forecast period, and obtaining a second electric vehicle to be connected to the power system; revising the first charging period to obtain a second charging period, and obtaining a second charging requirement and a maximum charging power; establishing a charging model, establishing a first constraint of the charging model, and establishing a second constraint of the charging model; and solving the charging model under the first constraint and the second constraint to obtain an optimal charging power so as to charge each first electric vehicle under the optimal charging power.