Mobile Object Bid-Offer Condition Determination for P2P Electricity Transactions
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Solution Overview
Problem
In P2P electricity transactions involving mobile objects like electric vehicles, existing systems face challenges in efficiently determining bid-offer conditions that maximize profit and minimize costs for both the mobile object and electricity demander, particularly during peak electricity demand periods, leading to suboptimal transactions and increased costs for large-scale demanders.
Innovation Solution
An apparatus that determines bid-offer conditions for electricity transactions by predicting sell and buy prices, optimizing charge-discharge operations, and adjusting upper limits of electricity amounts based on demand forecasts, allowing mobile objects to strategically participate in direct transactions and reduce reliance on grid electricity during peak hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile objects participate in P2P electricity transactions without optimized bid-offer determination, then transaction simplicity is maintained, but transaction efficiency and profit maximization deteriorate
Solution Approach 1:
The mobile object autonomously determines its own bid-offer conditions using an onboard determination apparatus that calculates optimal prices and quantities based on storage battery state, electricity prices, and demand predictions, eliminating the need for complex centralized negotiation while maximizing transaction efficiency
Solution Approach 2:
The system dynamically adjusts bid-offer parameters (price, quantity, timing) based on real-time conditions including storage battery charge/discharge state, electricity spot prices, and predicted demand, enabling adaptive optimization of transactions without fixed complex protocols
2Use of energy by moving object
If mobile objects charge/discharge without optimization, then operational simplicity is maintained, but energy utilization efficiency deteriorates
Solution Approach 1:
The determination apparatus continuously monitors the storage battery's state of charge, discharge power capabilities, and electricity price signals, using this feedback to dynamically adjust charge-discharge decisions and bid-offer conditions, maximizing energy utilization without requiring complex manual control
Solution Approach 2:
The system performs preliminary optimization calculations to determine optimal charge-discharge timing and quantities before transactions occur, using predicted electricity prices and demand forecasts to prepare optimal strategies in advance, improving energy efficiency without real-time complex control
3Loss of information
If bid-offer conditions are determined without demand prediction, then decision simplicity is maintained, but transaction profitability deteriorates
Solution Approach 1:
The determination apparatus performs preliminary prediction of electricity demand and pricing trends before transactions, using this advance information to set optimal bid-offer conditions that maximize profitability while the complexity of prediction algorithms remains contained in the calculation phase rather than real-time negotiation
Data Source
AI summary
An apparatus, for a mobile object, that determines a bid-offer condition on an electricity transaction market: determines an optimal condition making the largest gain for the mobile object from an electricity transaction, on which a contract is executed for electricity that the mobile object directly supplies to an electricity demander, based on an upper limit of an offer electricity amount of the mobile object and a predicted value of a buy price of the electricity demander for an electricity amount, the upper limit determined based on a predicted value of an electricity amount demanded by the electricity demander in each time period and an electricity amount transferable from the mobile object; and determines, as the bid-offer condition, to place an offer on an electricity transaction market at a sell price for a to-be-discharged electricity amount that are determined for each time period in the optimal condition.