API and Database to Track Renewable Distributed Energy Resources for SA Power Networks

Summary

As part of a ‘proof-of-concept’ trial in conjunction with the federal government, SA Power Networks embarked upon their ‘Grid of the Future’ initiative to drive the enablement of distributed renewables. Chamonix was engaged to create a RESTful API backed by a database to assist in the registration and tracking of renewable distributed energy resources (such as solar and batteries) on the SAPN grid.

Challenge

One of the key challenges faced was implementing a fast and indexable data store for information being produced by the platform, as well as real-time data being ingested into the platform. With the rapid development of the platform, the requirement for such a data store became a high priority. Multiple proof-of-concepts (POCs) were quickly generated, implemented, and tested in a POC environment separate from the production platform. Analysis was performed over each tested solution to determine the most performant and cost-beneficial solution.

Solution

The Chamonix team undertook the analysis of multiple POCs in-house in an environment separate from the main solution. Each ‘mini-solution’ was put under rigorous testing, leading to the conclusion that a No-SQL database was the best fit for the project’s time-series and constraint-related data. This analysis included a cost-benefit as well as a ‘best-fit’ approach for the project’s evolving architecture. Quick thinking and thorough knowledge of possible solutions and testing practices were paramount to achieving the project goals.

The selected solution was developed and implemented across the DEV, QAS, and PRD environments. This approach ensured that the most performant and cost-effective solution was deployed, meeting the high-priority requirements of the project.

Business Benefits

• Financial: The implementation of a cost-effective No-SQL database solution reduced overall project costs while ensuring high performance.
• Process Improvement: The rigorous testing and analysis process led to the selection of the best-fit solution, improving the efficiency and effectiveness of the data management process.