Meter-Targeted Load Shedding for Demand Variation Control
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Solution Overview
Problem
Existing load shedding management methods in distribution networks fail to effectively respond to variations in demand, leading to potential network collapses due to overconsumption.
Innovation Solution
A load shedding management method that involves forecasting overall consumption, determining load shedding periods, estimating load shedding gains for meters with high consumption, selecting meters for load shedding based on these gains, and sending load shedding commands to achieve a target gain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional load shedding methods are applied in distribution networks, then network collapse is avoided, but demand variations cannot be effectively responded to
Solution Approach 1:
The system performs preliminary forecasting of overall consumption and identifies potential load shedding periods before demand crises occur. By analyzing consumption patterns and predicting future demand, the system proactively schedules load shedding actions in advance, allowing the network to adapt to demand variations while maintaining stability.
Solution Approach 2:
The system continuously monitors actual consumption data and compares it with forecasted values. This feedback mechanism allows the system to adjust load shedding strategies in real-time, improving its ability to respond to demand variations while maintaining network reliability through data-driven decision making.
2Reliability
If load shedding is applied to all meters uniformly, then network stability is maintained, but efficiency is reduced due to unnecessary shedding on low consumption meters
Solution Approach 1:
The system estimates load shedding gains for individual meters based on their specific consumption patterns and characteristics. Instead of applying uniform load shedding, it identifies and targets specific meters that will provide the most effective load reduction, optimizing the local application of load shedding to each meter's needs and consumption profile.
Solution Approach 2:
The system dynamically adjusts load shedding parameters for different meters based on their consumption patterns, time of day, and historical data. By changing the parameters of which meters are shed and when, the system achieves network stability while maximizing efficiency by avoiding unnecessary shedding on low consumption meters.
3Measurement precision
If load shedding gain is estimated for all meters, then optimal selection is achieved, but computational complexity increases
Solution Approach 1:
The system estimates load shedding gains for only the necessary number of meters required to achieve the target gain, rather than calculating for all meters in the network. This partial action approach maintains measurement precision for selection while reducing computational complexity by limiting the scope of calculations to what is strictly necessary.
Data Source
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AI summary
Load shedding is implemented in a distribution network of a resource in order to avoid overconsumption. The network comprises at least one network head and a plurality of meters configured to measure consumption of said resource by a customer and to transmit to the network head customer information representative of the customer's consumption over a determined time range. An overall forecast consumption is determined, from the customer information transmitted by the meters, for all the meters for the determined time range. One or more load shedding periods are determined, by comparing the overall forecast consumption with a predefined overall threshold. For each, the meters to be load shedding are selected according to the expected load shedding gains for the meters considered. And a load shedding command is sent to them which concerns one or more future occurrences of the determined time range.