Smart Agent Reinforcement Learning for Grid Congestion
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Solution Overview
Problem
Current smart grid technologies lack efficient methods for customers to control and optimize energy trading, leading to grid congestion and curtailed renewable production due to variability in energy consumption and pricing.
Innovation Solution
A computer-implemented method using a smart agent system with a reinforced learning neural network to calculate local electricity prices and select optimal trading strategies, facilitated by a local matching engine for peer-to-peer energy trading, allowing customers to customize trading behaviors and adjust energy requirements based on market prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed trading agents use optimization algorithms to trade energy locally, then energy trading efficiency is improved, but device complexity increases due to multiple algorithms and coordination requirements
Solution Approach 1:
The trading agent enables customers to self-customize trading behaviors by configuring parameters such as price thresholds, time windows, and energy requirements. The reinforcement learning model automatically learns optimal trading strategies without requiring complex manual configuration, allowing the system to serve itself by adapting to customer preferences while maintaining simplicity in deployment.
Solution Approach 2:
The system allows dynamic adjustment of trading parameters including price thresholds, time windows, and energy requirements. These parameters can be modified based on market conditions and customer preferences, enabling flexible optimization of trading efficiency without requiring fundamental changes to the agent's core architecture.
2Adaptability or versatility
If smart agents enable customizable trading strategies, then adaptability to market conditions is improved, but ease of operation deteriorates due to configuration complexity
Solution Approach 1:
The reinforcement learning model automatically adapts to market conditions and optimizes trading strategies without requiring users to manually configure complex parameters. The system learns from market data and autonomously adjusts its behavior, enabling high adaptability while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The trading agent dynamically adjusts its strategies based on real-time market conditions and learned patterns. The reinforcement learning model continuously updates its policy to optimize trading decisions, allowing the system to adapt to changing market environments without requiring manual reconfiguration by users.
3Measurement precision
If reinforcement learning models are used for price calculation, then measurement precision of local prices is improved, but device complexity increases due to neural network requirements
Solution Approach 1:
The reinforcement learning model automatically learns to calculate accurate local prices by processing market data and identifying patterns. The neural network self-trains on historical and real-time data to improve price prediction accuracy without requiring manual calibration or complex configuration, enabling precise measurements while managing computational complexity through automated learning.
4Adaptability or versatility
If multiple smart appliances consume energy at different times, then adaptability to price variations is improved, but grid congestion increases leading to renewable curtailment
Solution Approach 1:
The trading agent receives feedback from the smart grid regarding grid conditions, congestion levels, and renewable generation status. This feedback is used to adjust trading strategies and energy consumption timing, encouraging load shifting away from peak periods and reducing grid congestion while maintaining adaptability to price variations through automated decision-making.
Solution Approach 2:
The trading agent acts as an intermediary between smart appliances and the smart grid, coordinating energy consumption and trading decisions. By managing the interaction between multiple appliances and the grid, the agent optimizes energy usage patterns to reduce congestion while allowing appliances to benefit from price variations, thus mediating between flexibility and grid stability.
Data Source
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.