Automatic Bidding System for Power Trading
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Solution Overview
Problem
Existing power trading systems fail to adequately address user preferences beyond low-cost electric power purchases, particularly in terms of renewable energy ratios and state-of-charge for vehicle batteries, due to a lack of automated bidding solutions that consider user-specific conditions.
Innovation Solution
An automatic bidding system and method that utilizes a computer to acquire user preferences and adjust bidding parameters for power trading, including setting target state-of-charge values for vehicle batteries, to facilitate purchases under preferred conditions such as low unit prices and high renewable energy ratios, using a user terminal to input and analyze user information for optimized bidding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic bidding is implemented to simplify power trading for users, then ease of operation is improved, but the system requires complex algorithms and parameter settings that increase device complexity
Solution Approach 1:
The bidding unit automatically executes power trading bids without requiring user intervention in the bidding process itself. The system self-adjusts bidding parameters based on pre-set user preferences and real-time power market conditions, enabling users to benefit from automated trading while the system handles the computational complexity internally
Solution Approach 2:
User preferences for power trading (such as desired power price ranges, renewable energy ratios, and charging time windows) are collected and stored in advance through the information collection unit. This preliminary configuration allows the bidding unit to automatically execute trades without requiring users to manually set complex parameters each time, simplifying operation while accommodating system complexity through pre-computed preferences
2Adaptability or versatility
If bidding parameters are dynamically adjusted to meet diverse user preferences, then adaptability is improved, but the difficulty of detecting and measuring user preferences increases
Solution Approach 1:
The information collection unit continuously acquires and updates user preference information through feedback mechanisms. Users can modify their preferences regarding power price, renewable energy ratios, and charging constraints, and the bidding unit adjusts its bidding parameters in response to this feedback, enabling the system to adapt to changing user needs while maintaining a manageable information collection process through structured feedback loops
Solution Approach 2:
The information collection unit is designed to handle multiple types of user preferences and requirements through a unified interface. It collects diverse information including price sensitivity, renewable energy preferences, charging time constraints, and power quantity requirements, consolidating these into a comprehensive user profile that the bidding unit can utilize for automated decision-making across different trading scenarios
3Adaptability or versatility
If the system collects detailed user information to personalize bidding, then adaptability is improved, but loss of information privacy increases
Solution Approach 1:
The system extracts and processes only the essential preference information needed for automated bidding (such as price ranges, renewable energy ratios, and charging constraints) while excluding unnecessary personal data. This selective extraction approach enables personalized bidding customization without requiring the collection and storage of excessive user information, thereby reducing privacy risks while maintaining adaptability
4Adaptability or versatility
If the bidding unit optimizes for multiple user preferences simultaneously, then adaptability is improved, but the complexity of the bidding algorithm increases
Solution Approach 1:
The bidding algorithm is segmented into separate functional modules that handle different user preferences independently. The bidding unit processes price optimization, renewable energy ratio optimization, and charging constraint satisfaction as distinct computational tasks, then integrates their results. This modular segmentation reduces the complexity of the overall algorithm by breaking down multi-preference optimization into manageable sub-problems that can be solved using established optimization techniques
Data Source
AI summary
An automatic bidding system includes a vehicle agent (a computer). A vehicle agent includes a bidding agent (a bidding unit) that places an automatic bid for power trading related to electric power of a user in accordance with an automatic bidding algorithm, and an information collection agent (an information collection unit) that acquires user information indicating a preference of the user. The bidding agent is configured to set a parameter of the automatic bidding algorithm using the user information.