Ripple Control in Networked Utilities With Event-Triggered Assistance
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Solution Overview
Problem
Utility systems like power and water networks face challenges in managing low-probability, high-impact disruptions, such as natural disasters, which existing management systems cannot account for, leading to potential system collapse and inefficiencies in resource allocation.
Innovation Solution
The implementation of a ripple-type control algorithm that allows controllable devices in networked systems to communicate assistance requests only when local resources are depleted, creating a ripple effect to address operational constraints, ensuring convergence to safe operational configurations without requiring knowledge of system model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing management systems are used to monitor and control networked systems, then system operation is maintained under normal conditions, but the systems cannot address low-probability, high-impact disruptions leading to potential system collapse
Solution Approach 1:
The control system dynamically adapts its behavior based on system conditions. During normal operation, devices operate independently. During disruptions, the system transitions to a coordinated response mode where devices communicate assistance requests and adjust operations based on ripple-type control signals, enabling the system to handle both normal and disruptive conditions effectively
Solution Approach 2:
The system changes operational parameters based on disruption detection. When output parameters fall below minimum thresholds, the system generates assistance requisition values and modifies input parameters through ripple-type control, transforming the system's response characteristics from static to adaptive based on real-time parameter monitoring
2Reliability
If centralized control systems are used to manage networked systems during disruptions, then system-wide coordination is achieved, but communication requirements and computing complexity increase significantly
Solution Approach 1:
The control system is segmented into independent controllable devices, each capable of autonomous decision-making based on local conditions. Instead of a single centralized controller, each device monitors its own output parameters and independently generates assistance requests when needed, distributing the control intelligence across the network and reducing overall system complexity
Solution Approach 2:
Each controllable device serves itself by monitoring its own output parameters and determining when assistance is needed. The system uses self-contained logic where devices automatically generate assistance requisition values based on their own state, eliminating the need for complex centralized monitoring and control algorithms
3Speed
If controllable devices communicate assistance requests continuously, then system response to disruptions is rapid, but communication bandwidth and energy consumption increase
Solution Approach 1:
Instead of continuous communication, the system uses event-triggered periodic communication. Devices monitor their output parameters continuously but only communicate assistance requests when output parameters fall below minimum thresholds, creating a periodic communication pattern that responds rapidly to disruptions while minimizing unnecessary communication energy consumption
Solution Approach 2:
The system extracts communication events only when necessary - specifically when output parameters violate minimum thresholds. By taking out only the essential assistance requests rather than continuously transmitting all operational data, the system achieves rapid response to disruptions while minimizing communication bandwidth and energy usage
Data Source
AI summary
Techniques for ripple-type control of networked physical systems such as power systems, water systems, and others are provided. As one example, a device includes at least one processor configured to determine, for a first controllable device in a system, based on a measurement of an output parameter and a minimum output parameter value, an output violation value for the first device. The processor is further configured to determine, based on a present input value for the first device, the output violation value, and an assistance requisition value corresponding to a second device, a target input value for the first device. The processor is further configured to cause the first controllable device to modify operation based on the target input value and a maximum input value for the first controllable device.