Customizable Energy Trading Agents With Transparent AI Pricing
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Solution Overview
Problem
Customers of a smart grid have limited control over trading agents that manage energy transactions, and smart appliances consume energy differently based on availability and price, complicating efficient energy trading.
Innovation Solution
A smart agent system with customizable trading profiles uses machine learning to optimize energy trading based on customer preferences, facilitated by a centralized information clearinghouse and reinforced learning neural networks to adjust trading behavior dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed trading agents are implemented to trade energy on behalf of customers, then energy trading efficiency is improved, but customer control over trading decisions deteriorates
Solution Approach 1:
The patent introduces a centralized information clearinghouse as an intermediary between distributed trading agents and customers. This clearinghouse collects, standardizes, and distributes market data to agents while maintaining customer preferences and constraints, thereby enabling automated trading without sacrificing customer control.
Solution Approach 2:
The system segments trading decisions into two layers: strategic decisions (trading goals, constraints, preferences) remain with customers, while tactical decisions (execution timing, price negotiation) are delegated to AI-powered trading agents. This segmentation allows customers to retain control over important decisions while benefiting from automated execution efficiency.
2Loss of energy
If smart appliances consume energy based on availability and price variations, then energy cost optimization is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements dynamic energy consumption strategies where smart appliances automatically adjust their operation schedules based on real-time price signals and availability from the grid. The system dynamically optimizes when appliances run by considering forecasted prices, customer preferences, and grid conditions, reducing energy costs without requiring complex manual configuration.
Solution Approach 2:
Smart appliances are equipped with embedded intelligence that enables them to autonomously make consumption decisions based on received pricing signals and customer-defined preferences. The appliances self-manage their operation schedules without requiring complex external control systems, thereby reducing overall system complexity while achieving cost optimization.
3Productivity
If multiple optimization algorithms are used in trading agents, then trading performance is improved, but transparency and interpretability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where trading agents provide explanations to customers about their decisions, the factors considered, and the expected outcomes. This feedback loop maintains transparency by allowing customers to understand and verify agent behavior, while the agents continue to use sophisticated optimization algorithms for high-performance trading.
Solution Approach 2:
The system allows dynamic adjustment of algorithm parameters and trading strategies based on market conditions and customer preferences. By making parameters configurable and observable, the system maintains transparency while preserving the ability to use complex algorithms for optimal trading performance.
Data Source
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AI summary
A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.