Energy Allocation Estimation with Partial AMI Metering
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Solution Overview
Problem
Utility providers face challenges in estimating the allocation of energy delivered at distribution metering points due to incomplete consumer metering information, making it difficult to account for contributions from consumers and dissipation in the distribution infrastructure.
Innovation Solution
The system employs estimators based on population parameters derived from sample statistics, classifying consumers into consumption classes and using sampling distributions to estimate energy allocation, even with incomplete metering data, by calculating per-unit apparent power and power factors, and comparing these estimates with substation measurements to determine dissipation metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete consumer metering information is available, then energy allocation estimation accuracy is improved, but infrastructure cost and complexity increase
Solution Approach 1:
The patent applies partial action by implementing metering only for a sample subset of consumers rather than complete coverage. The system collects metering data from a representative sample of consumers and uses statistical inference to estimate energy allocation for the entire population, thereby reducing infrastructure complexity while maintaining acceptable estimation accuracy.
Solution Approach 2:
The patent uses sampling distributions as statistical copies of the full population parameters. By creating sampling distributions from the sample data and using these as proxies for the complete consumer base, the system can estimate energy allocation without requiring actual metering data from every consumer, thus reducing infrastructure requirements.
2Device complexity
If sampling distributions are used to estimate population parameters, then infrastructure cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms by using bootstrapping and resampling techniques to continuously refine the sampling distributions. The system iteratively improves the estimates by incorporating new sample data and adjusting the statistical models, thereby enhancing measurement precision while maintaining the reduced infrastructure complexity advantage.
Data Source
AI summary
This disclosure is directed to a system and method of allocating energy provided by a power source. The system includes a computing device that receives, from meters, observations of energy delivered by the power source to sites. The device classifies the sites into consumption classes, where a first consumption class has complete coverage and a second consumption class has incomplete coverage. The device determines a metric for a characteristic of energy for the first class. The device determines a first demand for the first class based on the metric for the characteristic of energy for the first class. The device generates a sampling distribution of the metric for the characteristic for the second class and determines a second demand for the second class based on the sampling distribution. The device determines a dissipation metric based on the first and second demands.