Contingency-Based Load Shedding via Generator Capability Modeling
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Solution Overview
Problem
Power systems face challenges in balancing electrical generation with fluctuating load demands, leading to disruptions when demand exceeds supply, and existing technologies lack effective methods for prioritizing load shedding and estimating generator capabilities to maintain system stability.
Innovation Solution
The implementation of contingency-based load shedding schemes using priority lists, contingency detection algorithms, and generator capability models to monitor and manage power system state, topology, and load distribution, allowing for proactive load shedding and capacity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If load shedding is implemented to maintain system stability when demand exceeds supply, then system reliability is improved, but loss of information about load prioritization and generator capabilities worsens due to lack of effective methods for prioritizing and estimating
Solution Approach 1:
The system performs preliminary actions by pre-establishing priority lists for different types of loads and pre-storing generator capability information before load shedding is needed. This allows the control system to quickly determine which loads to shed and by how much, rather than making these decisions in real-time during a crisis, thus preventing information loss about prioritization criteria
Solution Approach 2:
The control system continuously monitors system conditions and uses feedback loops to maintain accurate information about generator capabilities and load priorities. This feedback mechanism ensures that the control system has up-to-date information about system state, generator status, and load characteristics, preventing information degradation over time
2Measurement precision
If contingency detection algorithms and generator capability models are implemented to accurately estimate generator capacity and prioritize load shedding, then measurement precision is improved, but device complexity worsens due to multiple monitoring and management components
Solution Approach 1:
The control system is designed with multi-functionality, serving as a universal platform that performs contingency detection, generator capability estimation, load prioritization, and load shedding control all through a single integrated system. This reduces overall device complexity compared to having separate dedicated systems for each function
Solution Approach 2:
The system uses parameter changes in generator capability models to estimate generator capacity under different operating conditions. By modeling how generator capabilities change with various parameters (temperature, humidity, load conditions), the system achieves precise measurements without requiring complex physical measurement devices for each condition
3Reliability
If priority lists and contingency detection algorithms are used to proactively manage load shedding, then system stability is improved, but ease of operation worsens due to complex monitoring and decision-making processes
Solution Approach 1:
The control system is designed to be self-service by automatically detecting contingencies, determining load priorities, calculating required shedding amounts, and executing load shedding decisions without requiring manual intervention. The system serves itself by continuously monitoring its own state and making autonomous control decisions, thus improving ease of operation despite the complexity of the underlying processes
Solution Approach 2:
The system performs preliminary actions by pre-establishing priority lists and contingency response strategies before actual load shedding events occur. This preparation work is done automatically in advance, so when a contingency occurs, the system can quickly execute pre-planned actions rather than requiring complex real-time human decision-making, thereby improving ease of operation
Data Source
AI summary
Disclosed herein are a variety of systems and methods for management of an electric power generation and distribution system. According to various embodiments, a system consistent with the present disclosure may be configured to analyze a data set comprising a plurality of generator performance characteristics of a generator at a plurality of operating conditions. The performance characteristics may be used to produce a generator capability model. The generator capability model may comprise a mathematical representation approximating the generator performance characteristics at the plurality of operating conditions. The system may further produce an estimated generator capacity at a modeled condition that is distinct from the generator performance characteristics of the data set and is based upon the generator capability model and may implement a control action based on the estimated generator capacity at the modeled condition.