Load Curve Smoothing via Random Time-Shift Desynchronization
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Solution Overview
Problem
Reducing sampling period for monitoring electricity consumption leads to significant oscillations in the overall load curve, making it difficult to accurately estimate energy usage and potentially causing electrical line deterioration or congestion.
Innovation Solution
A method and system that time-shift the operation of electrical loads by applying random shifts to their operation or stored load curves, based on the sampling period and regulation period, to desynchronize periodic loads and reduce oscillations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the sampling period is reduced to obtain more accurate load curve data, then measurement precision is improved, but the overall load curve becomes greatly affected by oscillations
Solution Approach 1:
The system applies preliminary time-shifting to elementary load curves before aggregation. By introducing random time shifts Δti to each elementary load curve based on its regulation period di and a random integer Ni, the system desynchronizes periodic loads in advance, preventing the formation of large oscillations in the aggregated load curve while maintaining measurement precision.
2Measurement precision
If the sampling period is reduced to capture temporal variations, then measurement precision is improved, but harmful oscillations are generated that may cause electrical line deterioration
Solution Approach 1:
The system converts the potentially harmful effect of high-frequency sampling into a benefit by using the detailed temporal information to apply intelligent time-shifting. The random shifts Δti = ni × Tech (where Tech is the sampling period) transform the fine-grained measurement data into a means for desynchronization, converting what would be harmful oscillations into a controlled smoothing effect that protects electrical lines.
3Productivity
If the sampling period is reduced to obtain real-time data, then productivity is improved, but the complexity of processing and managing oscillating data increases
Solution Approach 1:
The system changes the time parameter of each elementary load curve by applying random time shifts Δti before aggregation. This parameter transformation desynchronizes the periodic patterns in the data, converting oscillating load curves into smoothed aggregate curves that are easier to process and analyze in real-time, thereby reducing processing complexity while maintaining productivity.
Data Source
AI summary
A method is provided for smoothing of an overall load curve. The method includes aggregation of a plurality of elementary load curves. In one example, the method makes provision to determine (S1) a regulation period di associated with the load i; determine and store each elementary load curve by obtaining (S2) a plurality of measured consumption samples with a sampling period Tech that is a submultiple of each regulation period di. Starting from a reference time common to all of the loads, the method time-shifts (S3) the operation of each load i by a random shift Δti depending on the sampling period Tech, on the regulation period di determined for the load i and on a random integer value Ni associated with the load i.