Electric Power Trade Brokering System for Dynamic Scheduling
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Solution Overview
Problem
Existing systems for electric power trading in the context of electric vehicle charging stations do not effectively utilize waiting time and require users to plan based on station locations, lacking an efficient method to determine optimal trading schedules and counterparty selection.
Innovation Solution
An electric power trade brokering system that acquires task information from moving bodies with storage batteries, determines counterparty candidates based on trading conditions, and schedules trades to optimize energy usage and minimize costs, incorporating features like task re-routing and cancellation management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users plan charging based on stationary charging station locations, then charging can be performed, but waiting time is not effectively utilized and trading costs increase
Solution Approach 1:
The system transitions from static charging station locations to dynamic moving body-based trading. The broker server dynamically determines trade schedules based on real-time task information, positions, and periods of moving bodies, allowing flexible utilization of waiting time during movement rather than requiring fixed station visits.
Solution Approach 2:
The broker server automatically acquires task information from moving bodies, determines optimal trade schedules and counterparties, and manages the entire trading process without requiring manual user planning. This self-service approach eliminates the burden of schedule planning while maximizing waiting time utilization.
2Productivity
If manual planning of charging schedules is required, then users can control charging, but system complexity increases and efficiency decreases
Solution Approach 1:
The broker server acts as an intermediary between moving bodies and power trading counterparties. It automatically acquires task information, determines optimal trade schedules, selects counterparties, and manages transactions, thereby simplifying the system for users while maximizing trading efficiency through centralized intelligent management.
Solution Approach 2:
The system continuously acquires task information from moving bodies, processes this feedback data, and dynamically adjusts trade schedules and counterparty selections. This closed-loop feedback mechanism enables automatic optimization of trading efficiency without increasing user burden or system complexity.
3Loss of energy
If trade schedules are not optimized, then simple operations are maintained, but energy costs and trading losses increase
Solution Approach 1:
The broker server acquires task information in advance and determines optimal trade schedules before actual power trading occurs. By performing preliminary analysis of task periods, positions, and power rates, the system minimizes energy trading costs while maintaining automated operation without excessive complexity.
Solution Approach 2:
The system dynamically changes trading parameters such as trade timing, counterparty selection, and power rates based on acquired task information and market conditions. This parameter optimization reduces energy loss and trading costs while the automated determination process maintains appropriate complexity levels.
Data Source
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AI summary
An electric power trade brokering system includes: a task information acquiring unit that acquires task information about a plurality of tasks of a moving body on which a storage battery is mounted and that is movable by utilizing electric power provided from the storage battery; and a trade counterparty determining unit that determines, based on the task information acquired by the task information acquiring unit, a counterparty to trade electric power with the moving body at a position where at least one task among the plurality of tasks is carried out.