Grid Action Selection via Causal Knowledge Optimization
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Solution Overview
Problem
Current data aggregation and analysis schemes for utility grids are inadequate for real-time, automated optimization due to insufficient causal knowledge and reliance on correlations, which hinders the ability to identify relationships between grid actions and their temporally and spatially distant effects, requiring human intervention and lacking perfect experimental controls.
Innovation Solution
Generating and exploiting causal knowledge of grid operational decisions by calculating experimental units based on temporal and spatial uncertainty, selecting control states in a randomized manner, collecting data on their impact, and updating a knowledge database to improve grid effectiveness metrics and drive desirable conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data aggregation and analysis schemes are used, then grid intelligence can be produced, but the knowledge is based only on correlations and lacks causal understanding, requiring human interpretation and precluding real-time optimization
Solution Approach 1:
The system performs preliminary actions by conducting randomized experiments and actively learning causal relationships between grid controls and outcomes in advance. This builds a causal knowledge base that enables subsequent real-time automated optimization decisions without requiring human interpretation, thus resolving the contradiction between causal precision and automation extent
Solution Approach 2:
The system implements feedback loops where outcomes of control actions are continuously measured and used to update the causal knowledge model. This feedback mechanism allows the system to learn from actual grid responses and improve its causal understanding over time, enabling autonomous real-time optimization while maintaining high causal knowledge precision
2Loss of information
If current data aggregation schemes are used, then analysis can be performed, but temporal and spatial relationships between grid actions and their effects cannot be fully identified
Solution Approach 1:
The system adds temporal and spatial dimensions to the analysis by explicitly modeling how grid control actions propagate through the grid over time and space. This dimensional approach captures the full causal chain from control to outcome, preventing information loss about temporal and spatial relationships while enabling rapid intelligence generation
3Reliability
If big-data modeling approaches are used, then conclusions can be generated, but uncertainty from third variables and directionality problems requires expert human interpretation
Solution Approach 1:
The system performs self-service by autonomously conducting randomized experiments that actively learn causal relationships and automatically disambiguate third variable effects and directionality issues. This self-interpretation capability eliminates the need for expert human interpretation while maintaining high conclusion reliability, thus resolving the contradiction between reliability and complexity
Data Source
AI summary
Systems and methods for automatically selecting actions to take on a utility grid to simultaneously reduce uncertainty while selecting actions that improve one or more effectiveness metrics. Grid action effects are represented as confidence intervals, the overlap of which is used as a weight when selecting actions within a constrained search space of grid actions. The response of the utility grid to the grid actions may be measured and parsed by the temporal and spatial reach of the grid action, then used to update the confidence intervals for that particular selected grid action.