Digital Twin Simulation for Adaptive Industrial System Settings
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Solution Overview
Problem
Existing methods for simulating electric power systems, such as microgrids and distribution grids, are limited by their offline nature, which fails to adequately reflect changing asset conditions, utilization, and system objectives over time, leading to suboptimal system settings.
Innovation Solution
A digital twin-based approach combined with continuous system simulation and multi-objective optimization, allowing for the online determination of system settings that adapt to changing objectives and constraints, using active learning to explore and identify optimal solutions without disrupting real-world system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline simulation is used to determine system settings, then simulation accuracy is improved, but system adaptability deteriorates because the simulation cannot reflect changing asset conditions and system objectives over time
Solution Approach 1:
The patent implements a digital twin that continuously synchronizes with the real-world industrial system, enabling the simulation model to dynamically update its parameters and reflect current system states. This transforms the static offline simulation into a dynamic online simulation that adapts to changing conditions while maintaining accuracy through continuous data synchronization from sensors and system operations.
2Adaptability or versatility
If continuous online simulation is performed to improve system adaptability, then computational resources and time are increased
Solution Approach 1:
The patent performs preliminary simulations during the design and commissioning phases to establish baseline system behavior and pre-compute optimization strategies. This allows the online digital twin to focus computational resources on adjusting for deviations from the pre-analyzed scenarios, significantly reducing the computational burden during real-time operation while maintaining adaptability.
3Adaptability or versatility
If digital twin is used for online determination of system settings, then system adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the industrial system that replicates its behavior, structure, and parameters. This digital copy can be manipulated and optimized without affecting the physical system, allowing complex simulations and what-if analyses to be performed in the virtual environment. The digital twin serves as an intermediary that absorbs the complexity of continuous simulation while providing simplified recommendations for actual system control.
Data Source
AI summary
To determine system settings for an industrial system, digital twin data of a digital twin of the industrial system is retrieved. System simulations of the industrial system are performed based on the digital twin data to explore candidate system settings for the industrial system prior to application of one of the candidate system settings to the industrial system. At least one optimization objective or at least one constraint used in the system simulations is changed while the system simulations are being performed on an ongoing basis. The results of the system simulations are used to identify one of the candidate system settings for application to the industrial system.