Open APIs for Virtual Power Plant (VPP) Integration


the problem

Over the past year, there were many notable power system and market events that required large amounts of contingency Frequency Control Ancillary Services (FCAS) or energy-only responses. A large energy market company needed to analyse these data to draw insights on how the VPPs are interacting with the power system.

VPPs need to integrate with the client’s system using VPP Demonstrations APIs and send required large volume of data real-time for analysis.

the approach

intelia worked with the client to formalise, design and develop these API specifications into a scalable, cloud-based platform. This involved standing up a scrum team made up of a scrum master, three integration engineers and two automation testers supported by a solution architect.

This team would be the first to deliver a solution into production using an Agile approach for this client.

the solution

The client embarked on the establishment of a new application programming interface (API) management platform with the launch of the VPP Demonstrations. This required establishing patterns for the multi-cloud integration for the first time.

intelia, a key development partner for this client has also developed its systems to receive operational data from VPPs that provides visibility of the distribution connected distributed energy resource (DER) to the business. This helps the client learn how to integrate VPPs into the market at scale, which then inform appropriate regulatory and operational changes.

the outcome

The Telemetry Data Dashboard enabled the VPP team to review and monitor the flow of device data into the client . The VPP team had a requirement to analyse the completeness and regularity of the data against the expected amount of data, and to drill down to device level to diagnose exactly what data may be missing and ultimately, why. The dashboard was designed to allow the VPP team to view metrics across selected time periods to establish trends.

This dashboard exceeded the client expectations and additional positive feedback was received from Tesla suggesting that they would also implement a similar tool to qualify their data. The dashboard and DQ view allowed the client analysts to identify where data had been provided in incorrect units, which would have been otherwise unnoticed, and allowed actionable communication with VPPs to correct specific data errors with precision.