Behind-the-Meter BESS Detection from Flat Net Power Intervals
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Solution Overview
Problem
Current methods struggle to accurately determine the location, charge/discharge sessions, and size of behind-the-meter battery energy storage systems (BESS) in power distribution systems using only net power measurements from smart meters, which do not separately register consumption and generation components.
Innovation Solution
An unsupervised methodology using net power measurements to identify BESS by detecting flat intervals in time series data, determining charge/discharge sessions, and evaluating size based on eligible days of full utilization, without requiring separate measurements of local generation and consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If joint time series disaggregation of PV generation and BESS is performed, then identification accuracy of BESS parameters is improved, but system complexity and data requirements increase
Solution Approach 1:
The patent segments the identification process into two distinct stages: first detecting the presence and location of BESS using flat interval patterns in net power data, then separately estimating BESS size using eligible days and charge/discharge intervals. This segmentation avoids the complexity of joint disaggregation while maintaining identification accuracy.
Solution Approach 2:
The patent extracts the BESS detection function from the joint disaggregation process by identifying flat intervals in net power measurements as a standalone indicator of BESS charge/discharge activity. This extraction allows BESS identification to be performed independently without requiring separate measurements of PV generation and consumption.
2Measurement precision
If joint time series disaggregation of PV generation and BESS is performed, then identification accuracy of BESS parameters is improved, but data requirements and measurement infrastructure increase
Solution Approach 1:
The patent extracts the BESS detection function from the joint disaggregation process by identifying flat intervals in net power measurements as a standalone indicator of BESS charge/discharge activity. This extraction allows BESS identification to be performed independently without requiring separate measurements of PV generation and consumption.
Solution Approach 2:
The patent enables the BESS identification system to serve itself using only the net power measurement data already collected by smart meters. The flat interval detection method uses inherent patterns in the net power data to identify BESS activity without requiring additional measurement infrastructure or external data inputs.
3Loss of information
If detailed disaggregation of net power into consumption, PV generation and BESS charge/discharge is performed, then component-level visibility is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts the BESS detection function from the joint disaggregation process by identifying flat intervals in net power measurements as a standalone indicator of BESS charge/discharge activity. This extraction allows BESS identification to be performed independently without requiring separate measurements of PV generation and consumption.
Solution Approach 2:
The patent segments the identification process into two distinct stages: first detecting the presence and location of BESS using flat interval patterns in net power data, then separately estimating BESS size using eligible days and charge/discharge intervals. This segmentation avoids the complexity of joint disaggregation while maintaining identification accuracy.
4Reliability
If flat interval detection with tolerance band and minimum continuous period is applied, then detection reliability is improved, but false positive rate may increase
Solution Approach 1:
The patent adjusts the detection parameters (tolerance band width and minimum continuous period duration) to optimize the balance between detection reliability and false positive rate. By carefully selecting these parameters based on typical BESS operating patterns, the system achieves reliable detection while minimizing false alarms.
Solution Approach 2:
The system uses the detected flat intervals to identify eligible days, then uses charge/discharge interval analysis on those eligible days to confirm BESS presence and estimate size. This feedback mechanism allows the system to verify detections and reduce false positives by cross-validating results across multiple analysis steps.
Data Source
AI summary
A computer-implemented method for identifying behind-the-meter battery energy storage systems (BESS) in a distribution system includes measuring net power over a number of days via a meters at different locations in the distribution system. Using a time series of the measurement, a behind-the-meter BESS is detected by detecting flat intervals where the measured net power is substantially constant for a minimum continuous period, indicating BESS charge/discharge activity. If a BESS is detected, the time series data is used to detect first, second and third states that respective define net consumption, net generation and flat intervals indicating BESS charge/discharge activity. One or more eligible days are then determined that include a state change from the third state to the first state and a state change from the third state to the second. Intervals of BESS charge/discharge activity in the eligible days are extracted to evaluate a size of the detected BESS.