EV Charging Station Power Management via Statistical Occupancy Modeling
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Solution Overview
Problem
Current methods for managing power in electric vehicle charging stations are imprecise and unsuitable for handling uncertainties related to occupancy levels and variations throughout the day, making it difficult for the electricity network to anticipate and manage power consumption effectively.
Innovation Solution
A method that involves determining a statistical occupancy model for the charging station, generating occupancy scenarios, creating power profiles for each scenario, distributing power among vehicles based on connection duration, and selecting an optimum profile to ensure customer satisfaction and minimize prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power management methods are used for charging stations, then the system is simple to operate, but the power consumption forecasting precision deteriorates due to inability to handle occupancy uncertainties
Solution Approach 1:
The patent segments the power management approach by dividing the time period into multiple time intervals and creating multiple power profiles. Each profile corresponds to different occupancy scenarios, allowing precise forecasting for each segment while maintaining overall system manageability through modular scenario-based analysis.
Solution Approach 2:
The patent applies preliminary action by determining a statistical occupancy model and generating multiple occupancy scenarios in advance. Power profiles are predetermined for each scenario, enabling the system to prepare multiple forecasting pathways before actual occupancy patterns are known, thus improving precision without requiring real-time complexity.
2Measurement precision
If multiple power profiles are created for different occupancy scenarios, then the power consumption forecasting precision is improved, but the computational complexity increases
Solution Approach 1:
The patent utilizes parameter changes by varying the occupancy rate parameter across different scenarios (e.g., minimum, maximum, and intermediate occupancy rates). This allows the system to explore multiple forecasting possibilities by changing a single key parameter while keeping the overall methodology consistent, thereby improving accuracy without proportionally increasing complexity.
Solution Approach 2:
The patent creates simplified copies of power profiles for different occupancy scenarios based on a base statistical model. Instead of developing entirely new complex models for each scenario, the system copies and adapts the fundamental occupancy model with different parameter values, reducing computational complexity while maintaining prediction accuracy.
3Ease of operation
If power is distributed equally among connected vehicles, then the power management is simple to implement, but the customer satisfaction rate deteriorates due to inability to consider connection duration variations
Solution Approach 1:
The patent applies dynamics by making the power distribution strategy adaptive rather than static. The system dynamically selects from multiple predetermined power profiles based on actual occupancy scenarios, allowing the power distribution to adjust automatically to different connection duration patterns and vehicle needs, thereby improving customer satisfaction while maintaining operational simplicity.
Solution Approach 2:
The patent incorporates feedback mechanisms by comparing actual occupancy patterns against predicted scenarios and selecting the most appropriate power profile accordingly. The system uses satisfaction rate thresholds as feedback to validate scenario predictions, continuously improving power distribution effectiveness while keeping implementation straightforward through rule-based selection.
Data Source
AI summary
A method for managing power in a charging station for charging electric vehicles, the charging station including several charging points, the method is used to predict accurately a consumption of the charging station, with satisfaction of clients. The method includes determining a statistic model of occupation of the charging station, determining some scenarios of occupation of the charging station, take into account the statistic model, determining, for each power profile among many power profiles, some scenarios which are valid and some other scenarios which are non-valid, taking in account client satisfaction rate, selecting an optimum power profile among said many power profiles for which a number of non-valid scenarios does not exceed a predefined threshold.