Solar Power Forecasting With Bellwether Meter Clustering
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Solution Overview
Problem
Forecasting solar power generation is challenging, especially on cloudy days, due to unpredictable weather conditions and the need for third-party weather services, which introduces latency and costs for electric utilities.
Innovation Solution
A solar power estimation and forecasting system (SPEFS) uses bellwether meters in clusters of solar power systems to generate real-time estimates and forecasts, eliminating the need for third-party weather services by leveraging real-time data from these meters to predict cloud movement and adjust forecasts accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party weather services are used to forecast solar power generation, then forecast coverage is improved, but latency and costs increase
Solution Approach 1:
The patent extracts the forecasting function from external third-party weather services and implements it locally using on-site sensors and edge computing devices. This allows the system to generate forecasts independently without relying on external services, thereby eliminating latency and reducing costs while maintaining forecast coverage.
Solution Approach 2:
The system enables solar power plants to self-generate weather forecasts using their own sensors (pyranometers, anemometers, etc.) and local computing resources. By serving its own forecasting needs rather than relying on external providers, the system eliminates dependency on third-party services and reduces both time delays and operational costs.
2Measurement precision
If extensive deployment of solar source meters is implemented, then measurement accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent segments the monitoring system into strategic clusters, deploying bellwether meters at representative locations within each cluster rather than uniformly across all solar power plants. This segmentation approach maintains measurement accuracy for the entire system while significantly reducing the total number of meters needed, thereby lowering complexity and cost.
Solution Approach 2:
The system merges multiple measurement functions into the bellwether meters, which simultaneously monitor solar irradiance, weather conditions, and power generation output. By combining these functions into single integrated measurement points, the system achieves comprehensive data collection without the complexity of multiple separate metering systems.
3Productivity
If bellwether meters are used in clusters, then forecasting speed is improved, but device deployment requirements change
Solution Approach 1:
The patent divides the solar power system landscape into geographic or operational clusters, with each cluster represented by a single bellwether meter. This segmentation enables parallel processing of forecasts across multiple clusters, significantly improving overall forecasting speed while reducing the total number of meters required compared to comprehensive deployment.
Solution Approach 2:
The system uses bellwether meters as representative copies that model the behavior of multiple solar power plants within their clusters. By creating these representative copies rather than instrumenting every individual plant, the system achieves fast forecasting across the entire portfolio with a limited number of physical meters.
Data Source
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AI summary
A computer system is configured to group solar power systems that provide electric power to an electricity distribution system into clusters. The computer system identifies a solar source meter in each of the clusters that is representative of the respective one of the clusters as a bellwether meter. Each of the bellwether meters monitors a power output of one of the solar power systems in one of the clusters. The computer system receives solar power generation data from the bellwether meters. The computer system generates a solar power generation forecast for each of the clusters of the solar power systems using the solar power generation data from the bellwether meters in respective ones of the clusters.