What benefits can utilities achieve by deploying and leveraging distributed intelligence for the reliability of their grid networks? David Lukcic, director of AMI strategic solutions at US utility Tampa Electric Company, lists six advantages.
When utilities start to make decisions at the grid edge by leveraging data acquired from digital technologies and intelligent devices, distributed intelligence comes into play.
“It is the ability by grid devices to think and make decisions on their own and to be able to collaborate with neighbouring devices to address grid challenges,” said Lukcic during the webinar ‘Quantifying the Value & Benefits of Distributed Intelligence’ that was broadcasted during the Itron Utility Week for Asia Pacific Leaders event.
According to Lukcic, deploying an advanced metering infrastructure (AMI) is the core and foundation of distributed intelligence.
“In the utility sector, distributed intelligence enables safe operations and active management of the grid, as well as the expansion and optimisation of distributed energy resources.”
Why distributed intelligence is important
Lukcic highlights six reasons why distributed intelligence is important for utilities:
- Provides power and control to edge devices
By leveraging distributed intelligence, energy companies will have the power and more control over devices integrated with their grid. The control will enable them to access grid devices’ operations data more quickly and reliably. By accessing the data, utilities will be better equipped when making decisions regarding the operations of these devices in line with what they want to achieve by deploying these devices.
2. Resolves grid issues quicker
Having access to data regarding the operations of the grid will enable utilities to address grid challenges faster, before they become an issue for consumers. Distributed intelligence enables utilities to spend less time solving issues such as power outages or asset failures. They are also able to predict the lifespan of distributed assets, a development that would help them in the planning of asset maintenance initiatives.
3. Manages transactions and power flows in real time
Owing to the access to real-time grid data regarding energy demand and energy generation capabilities, utilities are able to leverage distributed intelligence to manage energy trading transactions and the flow of energy on their grid.
This data is vital for the development and implementation of demand response services such as Time of Use, whereby utilities charge higher prices during times when demand is high and lower prices during times when generation is high. This boosts grid stability and helps energy companies to simplify the integration of renewable energy resources on the network, at the same time ensuring they have enough capacity to meet baseload demand.
4. Predicts and manages energy needs across the entire network
Using machine learning, energy companies are able to predict the energy demand on the grid and align with the energy generation capability of a certain day. This will help ensure grid stability. For instance, on a day when cloud cover is forecasted to be high, weather data will be combined with consumer energy demand data to predict swings in energy generation and consumption.
5. Delivers consumer-based insights to improve safety and address usage
This can help in personalisation of services resulting in improved customer services. Visibility on how consumers are using their data and what they want will lower consumer churn especially in markets where supplier switching is supported by regulation to encourage competition amongst utilities to keep consumer energy bills low and services high.
6. Empowers an open and vibrant ecosystem of solution providers
Through distributed intelligence, utilities are able to take advantage of various technologies from multiple solution providers for the optimisation of the grid. For instance, a utility can use data from smart meters developed by a different solution provider that would have provided the utility with the grid communications network or intelligent substations. Distributed intelligence encourages the interoperability of various technologies and devices on the grid thereby enabling utilities to gather, distribute or deploy a wide range of solutions on their network.
Case study: Project rollout
Tampa Electric Company, a retailer to approximately 800,000 customers in Florida, started deploying their distributed intelligence programme in April 2020 through to December 2020. The project started with an AMI rollout with Itron.
The smart meter project included the installation of smart meters, head-end system, network, network head-end system and a distributed intelligence platform. The AMI was supported by a meter data management solution, a data warehouse and a customer relations platform.
Itron’s Lab in Raleigh was used in the first phase of analysis to evaluate the benefits of distributed intelligence apps and cloud analytics.
Texas-based software company Grid4C provided a data analytics tool for the pilot which was implemented in phases. The first phase (lab test) started with 3 households which were expanded to 100 households (field trials) and then 200 households by December 2020.
Three distributed intelligence apps were integrated with Itron’s smart meters including; Meter bypass and theft detection, residential neutral fault detection, and high impedance detection.
Comparing conventional analytics and distributed intelligence apps, Tampa saw significant improvements in regard to grid visibility with distributed intelligence.
An Itron Openway Riva smart meter was able to access up to 40 different electrical quantities every second whilst a conventional Centron meter provided 15 minutes to hourly intervals. The conventional meter again had to send data to the back office for processing before the utility could use that data. With distributed intelligence, Tampa Electric, Itron and third-party apps work with the data locally at the meter level in real-time to provide the utility with actionable insights without the data being sent somewhere else for processing.
Lessons learned and conclusions
Tampa Electric Company gained a lot of insight from the project rollout with Itron. For example, they learnt:
- That the predictions using data gathered from the distributed assets were correct.
- Access to real-time data gives actionable information.
- Distributed intelligence has the ability to tackle problems from an entirely different perspective.
- Managed to discover events that are otherwise undetectable by back-office analytics.
- Not all use cases are fit for distributed intelligence but when they are applied the value exceeds back-office results.
- Safety and customer impact issues can go undetected without distributed intelligence and can cost utilities lots of money to investigate false positives.