Predictive Load Shedding Using Real-Time Grid Contingency Data
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Solution Overview
Problem
Conventional automated load shedding schemes in power systems rely on fixed frequency settings and lack real-time data, leading to conservative or insufficient load shedding, and are not tailored to actual power system conditions, making them vulnerable to unforeseen contingencies.
Innovation Solution
A proactive load shedding system that utilizes real-time power system operating data to predict the need for and the optimal type of responsive load shedding action based on actual operating conditions, using a power control system that generates predictive models and responses to potential contingencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated load shedding schemes utilize fixed frequency settings, then the system operation is simple, but the load shedding accuracy is insufficient and cannot adapt to actual power system conditions
Solution Approach 1:
The patent implements dynamic load shedding by transitioning from fixed frequency settings to real-time frequency monitoring with adaptive response thresholds. The system continuously tracks system frequency and dynamically adjusts load shedding actions based on actual frequency deviations, ensuring accurate and timely responses to varying power system conditions.
Solution Approach 2:
The patent employs feedback mechanisms by monitoring real-time system frequency and using this information to trigger appropriate load shedding actions. The system continuously measures frequency, compares it against target values, and automatically initiates corrective load shedding when deviations exceed predefined thresholds, creating a closed-loop control system that adapts to actual conditions.
2Reliability
If proactive load shedding uses real-time power system operating data, then the response accuracy to contingencies is improved, but the data processing requirements and system complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring load shedding priorities, thresholds, and response strategies before contingencies occur. The system pre-establishes the hierarchy of loads to be shed and the conditions triggering each shedding action, enabling rapid automated response when frequency deviations occur without requiring complex real-time decision-making algorithms.
Solution Approach 2:
The patent utilizes parameter changes by monitoring real-time frequency deviations and adjusting load shedding actions based on the magnitude and duration of frequency excursions. The system modifies operational parameters such as shedding thresholds, response times, and load selection criteria dynamically based on the severity of the frequency deviation, optimizing reliability while managing complexity through parameter-based control.
3Reliability
If load shedding is delayed until frequency drops significantly, then the response timing is simpler to detect, but the system stability is compromised and total system failure risk increases
Solution Approach 1:
The patent applies preliminary anti-action by implementing proactive load shedding that anticipates and counteracts frequency deviations before they lead to system instability. The system monitors frequency in real-time and initiates load shedding actions at early stages of deviation, preventing the development of severe frequency excursions and eliminating the need for delayed reactive responses.
Solution Approach 2:
The patent employs continuous feedback monitoring of system frequency with automated trigger mechanisms that initiate load shedding when frequency deviations exceed predefined thresholds. This real-time feedback system enables early detection and immediate corrective action, maintaining system stability by responding to frequency changes as they occur rather than waiting for significant drops.
Data Source
AI summary
A power control system utilizing real-time power system operating data to effectuate predictive load shedding so as to accurately predict the need for and the optimal type of responsive action to a contingency-before the contingency actually occurs.