Time-Varying Virtual Energy Storage for Microgrid Trading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current peer-to-peer energy trading methods in microgrid systems face challenges due to high energy storage costs, limited consideration of time-varying virtual energy storage, and inadequate analysis of specific energy characteristics and trading processes, leading to suboptimal economic benefits and renewable energy utilization for prosumers.
Innovation Solution
A real-time peer-to-peer energy trading method that integrates time-varying virtual energy storage modeling, predicts environmental information, and uses a distributed transaction decision optimization based on continuous double auction to maximize prosumer income, considering multi-transaction preference levels and optimizing transaction prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional virtual energy storage system is used without considering time-varying characteristics, then the system complexity is reduced, but the measurement precision of building energy flexibility is insufficient
Solution Approach 1:
The patent transforms the static virtual energy storage model into a dynamic one by incorporating time-varying heat dissipation power characteristics. The model updates heat dissipation power based on real-time indoor-outdoor temperature differences, allowing the virtual energy storage capacity to dynamically reflect actual building thermal conditions throughout different time periods.
Solution Approach 2:
The patent changes the parameter representation from fixed heat energy quantification to time-varying heat dissipation power. By introducing time as a variable parameter and calculating heat dissipation power at different time points based on temperature differences, the model achieves more accurate energy flexibility quantification without excessive complexity.
2Productivity
If prosumers trade with limited other prosumers based on single transaction preference, then the trading decision complexity is reduced, but the productivity of energy transactions is insufficient
Solution Approach 1:
The patent segments prosumers into multiple preference levels based on their trading preferences. Each prosumer can be matched with others at the same or adjacent preference levels, creating a structured trading framework that expands transaction opportunities while maintaining manageable decision complexity through hierarchical organization.
Solution Approach 2:
The patent creates a universal trading framework that accommodates multiple transaction preference levels and enables prosumers to engage in transactions with multiple different prosumers. The system universally applies the preference level matching mechanism across all prosumers, maximizing market participation and transaction volume without requiring complex individualized decision-making for each prosumer pair.
Data Source
AI summary
The invention pertains to optimal scheduling and trading technology for microgrid systems, specifically focusing on a real-time peer-to-peer energy trading method involving time-varying virtual energy storage. This method predicts environmental information within a specified time domain and incorporates historical transaction data into virtual energy storage modeling and real-time energy trading. During the supply and demand energy extraction phase, it quantitatively extracts supply and demand energy and the marginal cost for trading using an autonomous energy management model for prosumers. In the transaction price optimization phase, it optimizes the transaction price based on historical data to maximize prosumers' income. The distributed transaction decision optimization method, utilizing a continuous double auction, enhances transaction matching decisions to maximize prosumers' income and accommodate multi-transaction preference levels. This approach leverages the complementary potential of energy resources to balance supply and demand within the system.