Decentralized Power Grid Forecasting for Sub-Minute DER Control
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Solution Overview
Problem
Traditional centralized approaches to power grid operation are inadequate for modern power grids due to increased complexity and the erratic nature of Distributed Energy Resources (DERs), requiring sub-minute control and optimization that centralized methods cannot provide.
Innovation Solution
A decentralized system where each power grid component is equipped with a compute device to obtain measurements, forecast future conditions, and wirelessly communicate with other devices to negotiate behavior using machine learning techniques, allowing for localized optimization without relying on central control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a centralized approach is used to gather measurements and transmit them to a central control for processing, then the system structure is simple and easy to manage, but the latency increases and sub-minute control precision cannot be achieved
Solution Approach 1:
The centralized control system is segmented into distributed compute devices, each associated with specific power grid components. Each compute device independently performs forecasting and optimization for its associated components, eliminating the need to transmit all measurements to a central control and thereby reducing latency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The control architecture transitions from a single-dimensional centralized hierarchy to a multi-dimensional distributed network. Compute devices are distributed across multiple spatial and functional dimensions, enabling parallel processing of forecasting and optimization tasks, which reduces overall system latency while distributing complexity across multiple manageable nodes.
2Adaptability or versatility
If traditional centralized techniques are used, then the system is easy to implement and manage, but they are impossible to implement with the scale of today's transmission grid systems
Solution Approach 1:
The system is divided into independent compute devices that can be individually deployed and scaled. Each compute device handles a specific subset of power grid components, allowing the system to scale by simply adding more compute devices without redesigning the entire centralized architecture, thus improving scalability while maintaining ease of implementation through standardized modular units.
Solution Approach 2:
Each compute device autonomously performs forecasting and optimization for its associated power grid components without requiring centralized coordination. This self-service capability enables independent operation and scaling of individual devices, making the system highly adaptable to different grid scales while simplifying implementation through autonomous, plug-and-play functionality.
3Productivity
If centralized control processes all data and formulates optimization plans, then centralized coordination is maintained, but the latencies prevent sub-minute control and optimizations
Solution Approach 1:
The optimization task is segmented and distributed to multiple compute devices, each handling forecasting and optimization for specific power grid components. This parallel processing approach eliminates the sequential bottleneck of centralized control, achieving sub-minute control speed while managing coordination complexity through standardized communication protocols between independent devices.
Solution Approach 2:
Each compute device performs forecasting and optimization for its associated components with higher granularity and frequency than traditional centralized systems. This partial action approach, where each device independently optimizes its local subset, achieves overall faster control speed by accumulating many small optimizations rather than waiting for centralized processing of all data.
Data Source
AI summary
A method of operating a computer device includes obtaining measurements of one or more conditions of a power grid component associated with the computer device. The computer device forecasts a future state of the one or more conditions of the power grid component associated with the computer device. The computer device communicates with other computer devices associated with other power grid components to negotiate a behavior of the power grid component associated with the computer device using the forecast.