Distributed energy introduces a level of complexity most utilities are ill-equipped to handle in a cost-effective manner. However, the Internet of Things and Data-as-a-Service (DaaS) platforms may enable these businesses to actually thrive in a next-generation energy economy.
In Part One of this series, we’ll discuss how the energy sector is evolving and the manner in which utilities will use DaaS platforms to operate competitively.
Defining the New Energy Economy
Researchers from the Massachusetts Institute of Technology Energy Initiative (MITEI) conducted a study titled “Utility of the Future” that analyzed the impact of distributed generation, flexible demand, energy storage and sophisticated power control devices on utility operations. MITEI envisioned a utility that would draw on centralized as well as variable distributed energy resources in a cost-effective manner.
Attaining that vision involves overcoming dozens of obstacles, most of which have to do with how most utilities operate. MITEI researchers asserted that, in response to asset digitization and distributed energy resources, utilities must:
- Implement prices associated with the costs of injecting and withdrawing electricity to the customer.
- Track the value of power services across particular locations during specific times.
- Support real-time electricity procurement and delivery transactions on the wholesale market.
Enabling these capabilities involves gathering comprehensive information about the infrastructure. For example, if 40 houses with solar panels produce more energy then each residence can consume, that excess power goes to the grid. However, the utility infrastructure must automatically re-route that power to customers with high demand.
This level of automation isn’t commonplace in the energy sector. Never mind a machine learning-based power grid that can route distributed power to customers with high demand. Ensuring the substations, transformers and other assets across the infrastructure can operate effectively is challenging enough.
Where Data as a Service Fits In
The rise of low-cost, sophisticated information and communications technologies provides utilities with the means of analyzing operational data in real time. The MITEI researchers concluded that hundreds of thousands of smart devices will collect and exchange data with each other.
Navigant Reseach’s findings corroborated the university’s findings. Across the globe, power companies are expected to invest $9.5 billion in smart grid and smart city networking solutions by 2025.
Where’s that data coming from? Substations, transformers, photovoltaic panels, windmills, electric vehicles, smart appliances, smart meters – the list goes on. Smart grid solutions will likely collect much of the data those assets produce.
But what about pricing information? That’d probably be in a customer relationship management solution. In addition, power plant data may come from supplier-side systems. In a nutshell, all of the data power companies require may reside in completely different applications, which introduces an aggregation challenge.
That’s where DaaS platforms come into play. These platforms connect to and aggregate information from heterogeneous applications. Business analysts, foremen and other personnel across the utility can then log into DaaS platforms and pull data from various sources to:
- Chart substation failure trends.
- Calculate the average electricity demand for a certain location on a monthly basis.
- Determine the mean cost of delivering power to a particular house.
The options don’t stop there. DaaS technology also supports what’s called pre-emptive (better known as predictive) analytics.
Applying Predictive Analytics to Grid Operations
The Institute of Electrical and Electronics Engineers discovered that the average small to mid-sized business loses $10,000 during an eight-hour outage. Preventing power outages may not be practical, but diverting manpower to where it’s needed most can reduce the time it takes for a utility to get service up and running again.
In order to allocate resources (in this case, lineman and repair crew) to their most valued uses, utilities must be able to anticipate which assets will fail during a particular storm. Achieving this capability entails using applied predictive analytics.
The Weather Company noted that pre-emptive analytics consistently enables power companies to determine which asset, whether it be a substation, transformer or something else, will fail based on the nature of a storm. In this example, a DaaS platform could help service professionals determine:
- How ice, wind speed and other elements impact asset durability.
- Which equipment will be most vulnerable to the storm.
- Where high-risk assets lie.
- Any previous performance issues associated with at-risk equipment.
- Which crews are most capable of fixing failures after they occur.
This is just the tip of the iceberg. In Part Two, we’ll provide more examples of how applying predictive analytics with DaaS platforms enables grids to operate in an optimal fashion.