Frequenz gRPC API to aggregate component data from microgrids
Project description
Frequenz Reporting API
Introduction
Frequenz gRPC API to aggregate component data from microgrids.
Supported Platforms
The following platforms are officially supported (tested):
- Python: 3.11
- Operating System: Ubuntu Linux 20.04
- Architectures: amd64, arm64
Overview
The Microgrid Reporting API serves as an interface for obtaining detailed insights into microgrid operations and metrics. Unlike general telemetry APIs, this API specializes in generating reports based on complex, user-defined aggregations of microgrid data. It provides both historical and real-time reporting capabilities.
Objective
The primary objective of the Microgrid Reporting API is to furnish a robust foundation for building data-driven applications that optimize microgrid performance, enable efficient power trading strategies, and facilitate intelligent decision-making across multiple operational scenarios. By aggregating and streamlining access to key metrics and data, this API not only aids in conducting in-depth performance analysis but also supports the development of algorithms and strategies for real-time and future power trading. This dual focus ensures that the API serves as a versatile tool for both operational and financial optimization within the microgrid ecosystem.
Key Features
- Real-time and Historical Reporting: Supports both real-time reporting through data streams and historical data retrieval, offering comprehensive analytical capabilities.
- Custom Aggregation: Support for user-defined aggregation formulas for microgrid component metrics like power, voltage, and more.
- Multiple Microgrid Support: Allows users to aggregate data from multiple microgrids in a single request, providing a holistic view of operations.
Scope and Limitations
The Microgrid Reporting API is designed to offer extensive reporting capabilities, allowing for both simple and complex data aggregations across multiple microgrids. It provides granular insights on a per-component basis as well as an overarching view of entire microgrid operations. The scope of the API is limited by the types of aggregation formulas it supports, potentially constraining its utility in highly specialized analytical scenarios.
Target Audience
The Microgrid Reporting API is tailored for a broad audience, including performance analysts, trading strategists, and cloud application developers. Whether the aim is to perform in-depth performance analysis, devise trading strategies based on microgrid data, or build applications that capitalize on real-time and historical data, this API serves as a comprehensive data source. By providing an array of key metrics and aggregation features, it accommodates various use-cases and empowers users to make well-informed decisions in different operational contexts.
Contributing
If you want to know how to build this project and contribute to it, please check out the Contributing Guide.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file frequenz_api_reporting-0.5.0.tar.gz
.
File metadata
- Download URL: frequenz_api_reporting-0.5.0.tar.gz
- Upload date:
- Size: 101.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c81ac870778b77c7282f6701b43b210f0608c773f068130d9ae362de7d4664d7 |
|
MD5 | 261ade308edf2d57a4c759c22df0cb7d |
|
BLAKE2b-256 | 0d63a2b94822b66d79408c13a7f9735ebcf2b131ce0289e0d2e66ba173a07eb2 |
File details
Details for the file frequenz_api_reporting-0.5.0-py3-none-any.whl
.
File metadata
- Download URL: frequenz_api_reporting-0.5.0-py3-none-any.whl
- Upload date:
- Size: 16.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.1.1 CPython/3.12.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 49d97819e20bd5c5937c38ba7ab8be1d5eacdd29de880e805cefd5d95ff599b5 |
|
MD5 | 46cfffc41717e84ae5910648f8e0eff9 |
|
BLAKE2b-256 | 57c878d593707853d8863fa7a682f4a800dd40b71fddf639907507231257c60d |