Physics-Informed Graph Neural Control for Integrated Energy Systems
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Solution Overview
Problem
The integration of renewable energy into integrated energy systems poses challenges in managing the system effectively, particularly due to the uncertainty of renewable energy sources and the impact of aging infrastructure, which traditional optimization methods cannot address efficiently.
Innovation Solution
An optimization control method based on a physical-informed neural network is developed, which constructs a deep graph neural network model with physical-informed fusion. This model incorporates optimization targets and variable constraint conditions into its loss function, enabling real-time system control and addressing uncertainties in renewable energy and unexpected situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional optimization methods are used for integrated energy system control, then the system can operate with established control strategies, but the response time is too long to meet current control requirements
Solution Approach 1:
The patent pre-trains the neural network model offline using historical operation data and physical constraints, so that when real-time control is needed, the model has already learned the optimization patterns and can provide immediate control strategies without performing time-consuming real-time calculations
Solution Approach 2:
The patent replaces traditional mathematical optimization methods (which require iterative calculations) with a neural network-based intelligent model that can directly output control strategies, substituting computational mechanics with learned patterns from training data
2Adaptability or versatility
If renewable energy is deeply integrated into the integrated energy system, then carbon emissions are reduced, but the uncertainty of renewable energy creates new challenges for stable operation
Solution Approach 1:
The patent incorporates physical constraints and operational limits into the neural network training process, creating a feedback mechanism where the model learns from both historical data and physical laws, enabling it to adapt to renewable energy fluctuations while maintaining system reliability through constraint-based decision making
Solution Approach 2:
The patent transforms the control approach by changing from fixed deterministic optimization parameters to adaptive probabilistic parameters learned from training data, allowing the system to handle the uncertainty of renewable energy sources while maintaining stable operation through learned patterns
3Productivity
If a deep graph neural network model with physical-informed fusion is constructed, then real-time control strategies can be provided, but the model complexity increases
Solution Approach 1:
The patent segments the complex integrated energy system into a graph structure where nodes represent system components and edges represent energy flows, allowing the neural network to process complex relationships through modular graph convolutions rather than handling the entire system as a single complex model
Data Source
AI summary
The present disclosure discloses an optimization control method for an integrated energy system based on a physical-informed neural network, which comprises the following steps: S1, constructing an a solar-electricity-heat-gas integrated energy system optimization control model; S2, generating a node connection relation matrix based on the network topology structure of the integrated energy system; S3, constructing a deep graph neural network model with physical-informed fusion; S4, constructing a loss function of the deep graph neural network model with physical-informed fusion; and S5, training a physical-informed neural network model according to the historical operation data to be used for system optimization control. The present disclosure can effectively deal with the influence of uncertainty of renewable energy and unexpected situations on the energy system, thereby ensuring the safe and stable operation of the integrated energy system.