Power Distribution Control with Asset Assimilation Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current power grid management systems face challenges in optimizing the allocation of assets to service models within short time increments, such as half-hour periods, due to computational burdens and latency in client-server reporting, which can lead to insufficient time for deploying optimal asset allocations before the next time increment.
Innovation Solution
A power control system that maintains real-time asset models, communicates with assets to dynamically inform their status, and uses an optimizer to search for optimal combinations of assets to fulfill service models by stacking distributions of asset behaviors, selecting assets based on overall probability, and issuing instructions to influence their behavior over set periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the system performs comprehensive optimization calculations for asset allocation within short time increments (e.g., half-hour periods), then the optimization precision and service model fulfillment accuracy improve, but the computational time and processing latency increase excessively
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal asset allocation configurations in advance for various grid conditions. When a service model needs to be fulfilled, the system retrieves pre-computed solutions rather than performing full optimization calculations in real-time, significantly reducing computational latency while maintaining optimization precision.
Solution Approach 2:
The optimization problem is segmented into multiple discrete time increments smaller than the traditional half-hour period. By breaking down the continuous optimization problem into discrete segments, the system can perform calculations more efficiently at each segment boundary while maintaining overall optimization accuracy across the full time period.
2Measurement precision
If the system uses detailed asset behavior distributions and probability stacking for optimizer search, then the accuracy of service model fulfillment improves, but the computational complexity and processing burden increase
Solution Approach 1:
The system changes parameters by using probability distributions to represent asset behaviors instead of deterministic values. By stacking these distributions mathematically, the system achieves high measurement precision in predicting service model fulfillment while managing computational complexity through efficient probability calculation methods rather than exhaustive simulation.
Solution Approach 2:
The system creates simplified copies or representations of complex asset behavior patterns through probability distributions. These distributed representations capture the essential variability and uncertainty of asset behaviors without requiring full detailed simulations, reducing computational complexity while maintaining accuracy in service model fulfillment predictions.
3Speed
If the system maintains real-time communication with all assets for status updates, then the responsiveness and current accuracy of asset models improve, but the communication overhead and data processing load increase
Solution Approach 1:
The system implements periodic action by updating asset status information at regular intervals rather than continuously in real-time. This periodic sampling maintains sufficient responsiveness and current accuracy of asset models while significantly reducing communication overhead and data processing load compared to continuous real-time monitoring.
Data Source
AI summary
In a power control system a server maintains asset models that represent asset behaviour, each asset model being in real-time communication with its asset to dynamically inform the model of the status of the asset. A test is performed at the server by issuing a command to an asset requesting the asset to perform a function. Sensors at the asset measure physical parameters at the asset and report these to the server. The server determines whether the asset responded to the command and, if the asset responded, how it responded over time. The server establishes a model for the asset in terms of an energy capacitance and a time constant based on the measured response. An optimizer determines which assets are to participate in which service models. The server sends instructions to the selected assets to attempt to fulfill the services.