Bid-offer Condition Determination for Mobile Object Electricity Transactions
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Solution Overview
Problem
In the context of peer-to-peer (P2P) electricity transactions, mobile objects such as electric vehicles face challenges in determining optimal bid-offer conditions for selling and buying electricity across different markets, particularly in minimizing costs and maximizing profits while effectively utilizing renewable energy, without incurring wheeling charges associated with grid electricity transmission.
Innovation Solution
An apparatus for mobile objects that acquires information on sell and buy prices across various electricity transaction markets, optimizes charge-discharge operations to maximize profit or minimize loss, and determines bid-offer conditions for direct transactions, considering factors like climate information, date, and time, to select the most advantageous market for electricity transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If mobile objects participate in P2P electricity transactions through direct markets, then wheeling charges are reduced and transaction costs are minimized, but the complexity of determining optimal bid-offer conditions across multiple markets increases
Solution Approach 1:
The apparatus automatically determines optimal bid-offer conditions by autonomously acquiring market information, calculating profit/loss indices, and selecting transaction markets without requiring manual intervention. The system serves itself by using its own computational resources to make trading decisions based on real-time market data and stored climate information.
Solution Approach 2:
The system continuously monitors market prices, climate conditions, and transaction outcomes to refine its bid-offer condition determination. By using feedback from actual transaction results and market responses, the apparatus optimizes its decision-making algorithm to better predict profitable trading opportunities and minimize wheeling charges.
2Productivity
If the apparatus acquires and processes comprehensive market information to optimize charge-discharge operations, then transaction profits are maximized, but the computational resources and processing time required increase
Solution Approach 1:
The apparatus pre-processes and stores climate information and market data in advance of actual trading decisions. By having this information readily available in structured formats, the system can quickly query and process only the relevant data needed for current bid-offer determinations, avoiding the need to process entire historical datasets in real-time.
Solution Approach 2:
Instead of processing all available market information uniformly, the system identifies and focuses computational resources on specific local factors that most heavily influence profit/loss calculations for each particular transaction opportunity. This selective processing approach maximizes profitability while minimizing unnecessary computational overhead.
3Adaptability or versatility
If mobile objects utilize storage batteries for electricity transactions, then flexibility in charge-discharge operations is improved, but the complexity of optimizing charge-discharge schedules across multiple time periods increases
Solution Approach 1:
The optimization problem is segmented into discrete time periods and market segments, allowing the system to handle complex multi-period charge-discharge scheduling through systematic breakdown into manageable units. Each time period and market combination can be evaluated independently using the same algorithmic framework, reducing overall computational complexity.
Solution Approach 2:
The system dynamically adjusts optimization parameters such as discount factors, constraint thresholds, and objective function weights based on current market conditions and battery state. By changing these parameters adaptively rather than using fixed values, the system maintains optimal charge-discharge schedules while reducing the complexity of long-term planning through myopic optimization.
Data Source
AI summary
An apparatus, for a mobile object, that determines a bid-offer condition on an electricity transaction market: acquires information on sell-buy prices for an electricity amount presented by electricity demanders on direct transaction markets, where a contract is executed for electricity that the mobile object directly supplies to or procures from an electricity demander; determines, based on the sell-buy prices, an optimal condition that maximizes a profit from an electricity transaction for the mobile object; and determines, as the bid-offer condition, to place an offer or a bid on an electricity transaction market at a sell or buy price for a to-be-discharged or to-be-charged electricity amount that are determined for each time period in the optimal condition. The sell-buy prices for the electricity amount presented by the electricity demanders on the direct transaction markets are acquired through prediction, or notification from the individual electricity demanders.