One of the most intimidating aspects of managing a multitude of member DERs is handling all the associated data. In this digital age, utilities already have more data than they’re able to leverage. Layer on telemetry from thousands to millions of devices on the grid, and it gets complicated quickly.
To achieve its vision of orchestrating local devices as a meaningful part of grid operations, HCE needed Camus’ help in figuring out how to deal with the data, collecting and integrating it into a unified data foundation.
HCE’s data collection systems included:
The cooperative also collected datasets from powerline sensors, DERs, and renewable energy systems.
Camus worked with HCE’s IT team to understand the cooperative’s network architecture and agree on an approach for the data transfer and collection. HCE set up data transfer infrastructure within its secured internal network.
Camus ordinarily installs and configures its data collection server during a site visit. But because of the COVID-19 pandemic, HCE completed these tasks with Camus staff guiding HCE remotely. Using its standard server configurations, HCE deployed Camus’s data collection server in a “demilitarized zone”—in other words, a network separate from HCE’s internal network.
Camus provided the data collection scripts to the HCE team, who moved them onto the server, where they worked as expected. The data transfer infrastructure pushed the data over a secure VPN outside HCE’s network boundary where Camus’s server collected them in the cloud. This approach allowed Camus access to necessary data while protecting HCE’s internal network from cyberattacks.
Camus’ systems never crossed the HCE network boundary. Throughout the transfer process, Camus always completed tasks on HCE’s server in a group setting with HCE and via remote control initiated by HCE using Webex. Every task happened with HCE’s consent, involvement, and direct supervision.
After the data transfer, the next big step was to integrate HCE’s datasets into the Camus platform. This was a critical part of the Camus-HCE collaboration. Camus’ objective was to deploy its platform as a unified, coherent view of the grid, including all the information needed for HCE’s day-to-day operational decisions.
The interface had to be simple and easy to navigate, while providing quick, actionable insights about the grid and DERs. At the same time, it had to enable easy access to additional data to inform deeper-dive analyses.
Camus started by integrating its standard datasets: SCADA, AMI, and GIS, followed by optional datasets chosen based on HCE’s resource mix:
Other datasets planned for integration or in progress at HCE include:
To best utilize the platform, HCE wanted to be sure that all of its team members – regardless of technical expertise - could access and analyze the data. By offering a simple user experience with robust analysis tools, it was easy for Camus to onboard a wide range of HCE staff members with varying goals, responsibilities, and skill sets.
Camus first provided select HCE power supply and engineering staff with unique login credentials to enable them to navigate the new platform in their web browsers. Then, Camus conducted a series of canary and acceptance tests on various aspects of the platform, ranging from the verification of specific data points to detailed tests of key interfaces and controls observed by HCE’s “super users.”
When these initial tests and verifications were complete, HCE extended platform access to additional users from member support, engineering, operations, and program administration.
Camus staff and HCE’s super users provided training to the new users on key aspects of the platform. Fortunately with the platform’s simple interface, anyone at HCE —whether it be a member support representative or an executive—can use the platform to gain a bird’s eye view of HCE’s grid and analyze data for more informed decisions. It’s as easy as using Google Maps or Google Search.
At the same time, the platform also provides access to powerful capabilities. It encapsulates millions of data points, so that resource schedulers, like David Manning, can conduct complex analyses like voltage heatmaps and orchestrate distributed assets in real-time.
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