Many factors contribute to the power failures in an electricity network such as faults at power stations or damage to transmission lines, short or circuit breaker operations etc. However, a particular set of factors have motivated the digital energy ecosystem we have been developing. These include the inadequate visibility over the power grid, the failure to identify the emergency conditions, and the failure to communicate that status to neighbouring systems. We’re attempting to contribute to the best set of eyes and the appropriate voice for emergent energy systems, such as those driven by the injection of clean energy into the market.
This blog introduces the DIPS (Distributed and Intelligent Power Systems) platform. DIPS is one outcome of the development of the Monash Microgrid, as part of the Net Zero Initiative. It provides a lens into the Clayton campus of Monash University, whereby distributed energy resources (DERs), outside of performing their operational role in decarbonising Monash’s operations, appear as instruments in a research laboratory. The campus is then a research environment for performing experiments on “smarts” coordinating DERs.
For example, DIPS has focused on Phasor Measurement Units (PMUs) as eyes into the power quality conditions of the campus microgrid. PMUs measure the frequency of electric signals at a specific location on a power line at the microsecond scale, aggregating measurements into reports at around one hundred times a second. This information provides critical insight into the microgrid’s stability and conditions. If performed in real-time, we can optimise and automate changes to the grid to keep its frequency and phase in sync.
However, this increased fidelity comes with a challenge. Substantially more measurements mean substantially more data generated – to the point where the networking, data management, and data storage are all also an experiment in design and operations (see our next blog on DIPS ML use case).
We formed a cross-disciplinary team involving the Monash eResearch Centre (MeRC), building services (Buildings and Properties Division), the Net Zero Initiative and power-systems engineers (Dr. Reza Razzaghi, Senior Lecturer, Department of ECSE, Faculty of Engineering and his research team) to address this challenge. The eResearch contributions to this endeavour, were to:
- Enable decentralisation of PMU data (ingestion, processing, streaming and storage), and
- Employ best practice systems engineering, IoT and big data technologies to enable actionable insight at the edge (near the PMUs themselves).
Small GPU accelerated ‘edge devices’ are placed by the sensors for onsite processing, allowing for data processing at the edge rather than sending such a large volume to the cloud. The distributed approach means that the devices can send information to each other and make decisions in real-time. Our pilot aims to employ machine learning to forecast and estimate grid conditions to improve response time and quality while reducing the unnecessary load on the power network.
The prototype of the DIPS framework, shown in Figure 1, entails the following:
- Real-time data ingestion, streaming and processing of high accuracy sensors leveraging open-source software.
- Data availability at the edge/cloud for analytical purposes.
- Actionable Insights (Forecasting, estimation and event detection ability) (see our next blog on DIPS ML use case).
- Persistent data storage for long-term data retrieval, archival and backup purposes.
- Data privacy, authentication and authorisation (this feature TBC).
- Client APIs (RESTful) for publishing data outside the Monash network.
Figure 1: High-level architecture of the DIPS framework
A digital twin of the DIPS ecosystem connected to a simulated PMU and the insight example has been made accessible to collaborators on the Nectar Research Cloud. Presently we are testing the same ecosystem with an actual PMU and data from multiple other DERs. Additionally we plan to make our campus PMU data available. Watch this space!
Dr Steve Quenette, Deputy Director, Monash eResearch Centre
Dr Ayesha Sadiq, Research Software Specialist, Monash eResearch Centre
Dr. Reza Razzaghi, Senior Lecturer, Department of Electrical and Computer Systems Engineering
Sharnelle Lai, Marketing Officer, Monash eResearch Centre