As the energy and utilities industry continues to rapidly transform, the area of utility data monetization is becoming increasingly a critical topic.  Currently there is no universal data sharing model that has been endorsed by customers, regulators, utilities and those operating at the edge of the grid. Each stakeholder has their own particular needs and value associated with various sets of data, whether that be in a connected home, on a utility meter or across the broader grid. To that end, a common approach that benefits all parties is needed. What is clear however, is that a potentially large opportunity for utilities to maximize their data assets exists. Looking at similar industries and the scale of the opportunity to monetize data, we see that a market for ‘telco data as a service’ is potentially worth $79 billion by 2020 according to 451 Research. Indeed, as the market for monetizing data matures in this industry we are seeing  some telco’s develop B2B partnerships with firms like SAP to leverage aggregated customer data for targeted advertising, measurement and broader consumer insights. The key question for utilities in the short-to-medium term will be how they value, transact and ultimately monetize their data in the face of ongoing pressure from third parties and regulators. In this piece, we offer a high-level framework for utilities and some initial steps to unlock the Utility Data as a Service ‘UDaaS’ opportunity.

The Utility Data Monetization Framework

Utilities globally need a common data value assessment framework and one utility jurisdiction in particular that offers insight into this space is New York. Indeed in the DSIP Guidance Order, it states, “At the core of the new model is improved information – improved both in its granularity, temporal and spatial, and in its accessibility to consumers and market participants.” In our recent REV related piece on NY’s Utilities Joint Distributed System Implementation Plans, we highlighted the difference between two types of utility data -- basic and value added. While it is true that there are some nuances unique to the type of data being considered here, these two categories are broadly defined under the REV proceeding as:

  • Basic Data is data that will be available to the requestor at no charge beyond the costs that are already included in base rates and includes data that is readily available, in the public domain, and provided without additional analysis or processing.
  • Value-added Data is data that will be available for a fee determined through utility-specific fee structures. Value-added data goes beyond basic data as it is not routinely developed or shared, has been transformed or analyzed in a customized way (i.e., aggregated customer data), is delivered more frequently than basic data, is requested and provided on a more ad hoc basis; and/or is more granular than basic data.

In the figure below, we highlight how within this framework, basic and value added data have distinct characteristics and that different treatment across both the customer and the system domains is required. 

Utility Data Monetization Framework

With this framework in mind, we see that opportunities exists for utilities to monetize value added data, and that this is particularly true as utilities capture more grid data and as the IoT and connected home markets accelerate. 

Utility Data Growth - Monetization Opportunities and Threats

The volume of data captured by the Internet of Things (IoT) will exceed 1.6 zettabytes by 2020, according to a forecast from ABI Research, part of this trend is the shift from cloud computing toward edge computing. Indeed, it is the connected home and smart city verticals in the IoT market, combined with traditional DER opportunities (asset ownership) that may provide the largest opportunity for utilities. For example, in terms of data streaming, use cases include device monitoring and control at the meter premise, demand response, DER dispatch, and settlement and interfacing with on premise devices (e.g., building management systems) or offering energy management and related services. In terms of utility activity in this space, we pointed out last year that 93 percent of energy and utilities companies had increased the number of IoT projects they were involved in. To that, utilities are currently capturing and processing a host of valuable data.

That being said, over the next 5 years, the idea that technology firms may be collecting / utilizing more home energy data than utilities is a possibility. For example, Google’s $3.2 billion acquisition of Nest in 2014 was less about a device sales play (breaking even only if they sell to the majority of US homes by some estimates), but more about a data play. Nest provides Google with a strategic advantage in the IoT market through data, immediately creating new opportunities in the home. While this is a flagship example, there are plenty more across the industry, ranging from startups to established industry players that are gaining traction in the home and acquiring access to home energy data.

Adding to this trend of third party access to energy data has been the broader data sharing initiatives in the space over the past 5 years. For example, The Green Button Initiative, the energy data standardization effort that was officially launched in the US in January 2012 has enabled the launch of 235 applications by startups and established players using data from over 50 utilities and some 60 million homes and businesses. Similarly, in April 2016, the DOE launched Orange Button, with $4 million for projects to increase access to solar data aiming to increase solar market transparency and fair pricing by establishing data standards for the industry.

Outside of these examples and the data monetization opportunities in the home, we are seeing utilities gather significantly more valuable and granular data across the grid as sensors, communications networks and sophisticated processing algorithms are increasingly deployed. In this space, utilities may have significantly more opportunities to monetize their value-added data. In the figure below we highlight the accelerated growth in utility data both sides of the meter. In this sense, it is important for utilities to assess the value of data collected at various levels including feeders, substations, and at the system level and the value of this data to third parties. For example, DER providers can use this data as an input to their technical and business decisions, such as where to market services or locate resources to support grid needs, and how to best respond to non-wire alternatives solicitations. (For more information on how utilities can do more with data see our UtilAPP resources). With this in mind we are seeing technology companies increasingly advocate for broad access to utility data. 

Accelerated Growth in Utility Data

Third Parties Push for Access to Utility Data

In a paper written by several technology groups associated with clean energy in Dec 2016, they urged utilities to move well beyond data sharing efforts such as the Green Button, arguing that data transparency on its own is will not spur market animation. They suggest that various types of utility data must be available in a readily utilized set format. For these technology companies, they say they want access to grid planning data into three categories:

  1. Grid Needs and Planned Investments (Grid Need Type, Location, Scale of Deficiency, Planned Investment, Reserve Margin, Historical Data, Forecast Data and Expected Forecast Error)
  2. Hosting Capacity (Circuit Model, Loading, Equipment Ratings and Settings)
  3. Locational Value (Energy + Losses, Generation, Transmission & Distribution Capacity, Ancillary Services, Renewable Energy Compliance, Societal Benefits, Voltage and Power Quality, Conservation Voltage Reduction, Equipment Life Extension, Reliability and Resiliency, Market Price Suppression)

In sum, they argue for access to granular grid planning data and that regulators should consider ordering utilities to share their holistic grid data developed from their resource plans through a machine-readable standard data format in an easily accessible manner. What is clear here is that the framework that we outline “Basic and Value Added Data” and the access to the data that these technologies firms outline will need to be negotiated and reconciled. With this in mind, we need a regulatory path forward.

Utility Data Monetization - A Regulatory Path Forward

Again, looking to New York and in particular, the PSC’s REV Order Adopting a Ratemaking and Utility Revenue Model Policy Framework, May, 2016 we see that data falls under REV’s definition of Platform Service Revenues (PSRs) e.g. PSRs can be earned by utilities through their provision of Distributed System Platform (DSP) services. The idea being that increased PSRs would encourage utilities to support access to their systems by DER providers, and offset required base revenues derived from ratepayers. The ultimate purpose of the transition is to create “a business and regulatory model where utility profits are directly aligned with market activities that increase value to customers”. To that, they give the example of a competitive value-added service in the provision of data analysis. According to the PSC, in this example, there could be three types of services associated with data, with three different types of regulatory treatment.

  • First, in the context of the order and the DSIP, utilities will be required to make some level of data available to customers and to third parties, at no cost (aligned to the definition of basic data explained in our framework).
  • In cases where customers request information that is more detailed and/or more frequent than basic required data, utilities could supply this value-added data for a nominal fee. This second type of service – additional data – would derive directly from the monopoly function and be treated as a PSR. (aligned to the definition of value added data in our framework)
  • In the third case, utilities may perform analysis of customer-specific data, and provide recommendations based on that analysis, conditioned on utilities implementing tools to allow customers to easily share their usage data with third-party vendors including firms providing data analysis (again, aligned to the definition of value added data in our framework). This third type of service – analysis and recommendation – would be competitive.  

With this approach in mind as well as the framework outlined in this piece, an initial overview of the types of data and what could be monetized by utilities is emerging, the more difficult question however, lies in determining the ‘real’ value for that data and the fee-based structure that is needed for utilities to monetize utility data resources.

Looking Ahead - Fee-based structures for value-added data services

Overall, utilities across the country should begin to explore alternative means of utilizing fee-based structures for value-added data services. However, in order to begin assessing this and forming a broader view, utilities should first assess all of their data in a basic and value added framework to identify the overall opportunity. This may result in the utilities developing fees for data that had previously been provided at no additional charge. Similarly, as is the case in New York, utilities may also leave open the possibility that what may presently be characterized as value-added data may become part of basic data in the future. It will also serve utilities well to look to other industries where well defined models and B2B partnerships to process, transact and ultimately monetize data exists. Ultimately, these steps may well lead to a large and timely Utility Data as a Service (UDaaS) opportunity.