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The Analytics-Driven Digital Utility

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Executive Summary

Digitalization. Digital Transformation. Data 4.0. It doesn’t really matter what you call the latest trends in energy data analytics. What really matters to energy utilities is gaining the most value from the potential intelligence contained within their data streams. While much of the industry buzz is focused on data analytics, there needs to be a whole data ecosystem in place to support those analytics. As utilities are under increasing pressure to support electric vehicle (EV) charging infrastructure, having the proper data infrastructure and analytics is essential to avoid delays and high costs for installing EV charging stations. E.ON, a major European utility, has been able to implement a complete data ecosystem to provide timely analytics and insights to support municipal planners in Germany focused on the development of EV charging infrastructure.

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Data Analytics in Use

Deregulation, renewable energy, IoT, and Big Data systems have contributed to the transformation of the energy industry from an electron-driven business to a data-driven business. Utilities can now track and optimize operations by intelligently monitoring all their data—there and back, from generation to transmission, distribution, and destinations “behind the meter.” This feedback loop has a broad impact not only on the energy companies themselves, but also on their numerous stakeholders. Decision makers at energy companies, municipalities, equipment OEMs, as well as consumers, can now interact with their industries using data analytics.

What types of insights or trends from datasets can help a utility? Consider a typical North American energy utility with a few million customers. With a few decades of operational experience, they are likely sitting on vast troves of customer information: names, addresses, residence or business type, energy usage history (static as well as constantly updating, such as information from smart meters), credit card information, payment status, etc. Newer datasets include recent technological adoption trends: PV solar installations, an EV, an EV charger (and of what type), battery energy storage system, or more. While independently housed in different databases, these datasets don’t provide particularly useful information to the utility. But when aggregated, arranged, synthesized, analyzed, and visualized, this information becomes extremely useful.

All the types of data mentioned above can be grouped, re-grouped, filtered, and inspected with customizable viewpoints. Once the data has been rearranged into a new format that supports a specific logical data format (“schema”), different sorts of questions can be asked. Trends can be analyzed or aggregated: If x number of people on this street buy an EV in the next 2 years, will the transformer that feeds this street be able to sustain the new load? If not, what should the new specification of the new transformer be? When will these EVs charge? Where will they travel to? What amount of energy does each consume typically? Can multiple EV batteries be aggregated and viewed as a virtual source of power? Can that be tied together as a virtual power plant (VPP) and be used effectively? 

Thanks to advances in data science, analytics, machine learning, and AI-based modeling, these sorts of data inquiries are becoming commonplace and easier to address. This has led to a corresponding rise in businesses geared toward providing the answers through analytics. While some businesses enable easier data collection easier, or else easier data preparation and formatting, others offer solutions to make the data easily and securely shareable by multiple parties. Utilities, which deal with data streams from a wide variety of sources, usually have to allocate resources toward integrating these disparate solutions, or else rely on a systems integrator to do so. Looking at the challenge of supporting EV charging infrastructure, E.ON decided to take another path.

Sector Coupling: Electrified Transportation and Smart Energy

Let’s dive further into the topic of EVs and helping two sectors, electrified transportation and distributed energy, converge and synergize.

Carefully planned and phased grid updates to support electrification require a careful analysis of data from hundreds of individual data sources. Some of these sources include:

  • Existing grid infrastructure information
  • Parking location information
  • Battery types and energy content (charging needs)
  • Location infrastructure information
  • Parking infrastructure information (public or private garage, for instance), including ownership
  • Existing city infrastructure information (sewer, gas, water, communications cabling nearby, etc.)
  • Customer type and their resources (EV or energy storage, or heat pumps or all-electric appliances, etc.)
  • Regulatory and governmental data privacy information, such as restrictions on personally identifiable information
  • And many more

Installing EV chargers is mainly a data analytics exercise. Delays in interconnection studies–understanding and calculating the impact to the grid of adding new EV charging stations–can be very costly. Rising engineering expenses (soft costs) and protracted electrical interconnection processes are, in some cases, stopping EV charging installation projects. Potential EV charging station sites have been rejected or abandoned, often after significant costs have been incurred. For example, while the typical costs for the installation of a fast-charger ranges between $150K to $200K, the industry has seen cases where a project developer/owner has spent up to $1.5 million on a single fast-charger station because of utility interconnection issues.

Delays in the grid interconnection approval process can add weeks, months, or more than a year to a project schedule, incurring thousands of dollars in soft costs. Much of the soft cost increases and delays are attributed to challenges assessing the available capacity to add new electrical loads to the distribution network at a prospective site or during the process of obtaining a utility interconnection.

One of the major causes of these delays is a lack of easy and quick access to electric grid information, which can significantly impair the siting and construction of new EV-charging stations. Challenges include:

  • Grid data is extremely complex and difficult to process, synthesize, and maintain.
  • Digital grid data sources are not only disparate, they are stored in different physical locations and consist of various data types. Not all grid data is digital. Therefore, analog sources of grid data require manual, time-consuming conversion.
  • Balancing access to grid data and privacy laws is tricky and requires extensive expertise, data-handling experience, and risk mitigation know-how.

An Optimized Data Ecosystem—Providing Real Value

E.ON solved its challenges around obtaining accurate grid information by using an optimized grid data analytics ecosystem. The core of this ecosystem is a single, rights-managed, secure solution that combines geographic information, grid information, and grid infrastructure characteristics in one view. With this solution, planners can instantaneously conduct a feasibility analysis of a new grid interconnection request, including:

  1. Performance impact to the grid at transformer location
  2. Equipment upgrade costs
  3. Anticipated utilization rates, and more.

These tools have reduced interconnection planning time by 90% - 95% and eliminated the upfront soft cost associated with grid interconnection requests.

Deployed in Germany since 2017 and managed by DigiKoo, a subsidiary of E.ON, the solution allows municipality planners, EV infrastructure stakeholders, and even the average citizen, to site EV charging stations. This can be done by simply entering an address or uploading a photo of a desired location to initiate crowdsourcing. The solution provides grid transparency and gives stakeholders efficient, yet highly secure visual and graphical displays. This allows planners to quickly assess the feasibility of deploying EV charging stations without swelling soft costs and lengthy engineering time. The following figures illustrate the process and the data analytics involved:


Figure 1: Share grid and city infrastructure, parking, existing and planned EV charger locations


Figure 2: Analytics developers create cost/performance algorithms from shared data (from Figure 1)

The solution relies on a secure, trusted data rights management platform which governs the use and access to grid data. This platform ensures compliance with all relevant data usage policies, whether imposed by government or corporate entities. Using data virtualization technology (which avoids expensive, time-consuming data reformatting processes), it creates datasets by querying the data where it resides, in any location–whether on-premises or in the cloud. The results are containerized and secured, and then made available for collaboration and co-creation of value-added services by internal or external partners. This ensures that the algorithm creator/owner doesn’t lose control over their intellectual property, and the data owner never loses control over who accesses what piece of data. Each owner is granted complete and very fine-grained control over role and rule-based access to their IP and data, respectively.


Modern utilities depend on data to improve workflows, run more efficiently, and discover new opportunities. Fortunately, new data analytics solutions are becoming increasingly commonplace, which leverage intelligent automation and decentralized, neutral solutions to make energy data management easier and seamless. The energy utility industry has been one of the earliest adopters of intelligent analytical solutions and automation. As seen in the E.ON use case, a data platform that facilitates secure multi-organization collaboration, cross-cloud data sharing, and interoperability can provide real value to grid management companies.

Shamik Mehta's picture
Thank Shamik for the Post!
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Matt Chester's picture
Matt Chester on Dec 2, 2020

 Fortunately, new data analytics solutions are becoming increasingly commonplace, which leverage intelligent automation and decentralized, neutral solutions to make energy data management easier and seamless.

Are these new solutions typically being built on top of existing data operations, or do they require entirely new build out? 

Shamik Mehta's picture
Shamik Mehta on Dec 2, 2020

I see both, Matt. Custom solutions built from the ground up to solve very specific problems, including "new, final products" assembled by systems integrator firms, are common. But with the increased move to cloud-based platforms with their built-in DevOps advantages, I see more of the former proliferating: solutions being deployed on top of existing DataOps infrastructures and systems. 

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