Digital Twin Power Grid Settings for Changing Objectives
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Solution Overview
Problem
Existing methods for determining system settings in industrial electric power systems, such as microgrids and distribution grids, struggle to account for changes in system states and objectives over time, leading to suboptimal performance and stability issues.
Innovation Solution
The integration of a digital twin with continuous system simulation and multi-objective optimization enables the online determination of system settings, allowing for the exploration of candidate settings and adaptation to changing objectives and constraints without disrupting system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If offline simulation is used to determine system settings, then system stability is improved through extensive simulation, but the system cannot adequately reflect changes in asset conditions and system objectives over time
Solution Approach 1:
The patent transitions from static offline simulation to dynamic online simulation that continuously adapts to changing system conditions. The simulation system is made dynamic by implementing continuous monitoring of asset conditions and system objectives, allowing the simulation models to update and reflect current system states rather than relying on historical snapshots.
Solution Approach 2:
The patent implements a feedback mechanism where simulation results are continuously compared with actual system performance, and the simulation models are adjusted based on this feedback. This closed-loop approach allows the system to learn from actual operations and improve its predictive accuracy over time, resolving the contradiction between stability and adaptability.
2Device complexity
If digital twin interface requires user input for determining optimum operation parameters, then system complexity is reduced, but the system cannot adequately reflect changing system objectives over longer time horizons
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine optimum operation parameters through continuous online simulation and multi-objective optimization. The system autonomously monitors changing objectives and adjusts parameters without requiring user input, while maintaining the ability to reflect changing system objectives over time through continuous data processing and optimization algorithms.
3Use of energy by moving object
If offline analysis of data snapshots is used for system adaptation, then computational resources are conserved, but asset conditions and system utilization cannot be adequately reflected
Solution Approach 1:
The patent implements periodic action by performing simulations at continuous intervals rather than relying on periodic offline snapshots. This allows the system to capture changing asset conditions and system utilization patterns while managing computational resources through efficient scheduling of simulation updates based on significant change detection or predetermined time intervals.
Data Source
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AI summary
To determine system settings for an industrial system (10), digital twin data of a digital twin (41) of the industrial system (10) is retrieved. System simulations of the industrial system (10) are performed based on the digital twin data to explore candidate system settings for the industrial system (10) prior to application of one of the candidate system settings to the industrial system (10). 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 (10).