Conceptualising the emerging digital energy landscape

By Emily Judson, Energy Policy Group, 24 January 2019

Digitalisation of energy is an evolving space, with the potential to effect significant and rapid changes in the energy system. These changes are already occurring in two spaces: firstly in the ability of digitalisation to improve existing energy infrastructures, and secondly in opportunities to unlock new areas of value via customised, data-driven services. To make the most of the opportunities presented by digitalisation, it will be essential for policymakers, and other actors, to understand its developing landscape. In particular, digitalisation is increasingly blurring the boundaries of the energy system through the development of new services and business models based on novel, cross-sector uses of data.

This extended blog raises fundamental questions around the scope of the energy system in a digitalised, data-driven world. These questions challenge industry, policymakers and regulators to reconsider their remits and relationships in order to maintain progress towards stated environmental, social and economic objectives of energy system transition. To explore this, the blog identifies three broad trends in energy digitalisation; a discussion of the conceptual frameworks needed to contextualise digitalisation; and implications for policymakers.

Three trends in energy digitalisation

Digitalisation has the potential to facilitate improvements and efficiency-savings to existing physical energy infrastructures and energy companies (transmission, distribution, supply) in the UK. For example, distribution automation enables automated collection and analysis of data from sensors in geographically-dispersed parts of the network, with control responses (either automated or manual) then executed by the distribution authority. This can support distribution companies to optimise grid capacity, isolate faults more quickly, or to pre-empt and repair damaged infrastructure through predictive maintenance. Beyond the distribution level, National Grid are also looking to employ drone and artificial intelligence (AI) technologies to facilitate infrastructure inspection and predictive maintenance. This kind of digitalisation has the potential to support the efficiency and resilience of a more decentralised grid, potentially also passing on lower operating costs to consumers.

However, the potential for digital change goes beyond facilitating improvements to existing physical and business infrastructures. Digital innovation also creates opportunities for the development of entirely new products, services and business models. For example, the proliferation of ‘Internet of Things’ (IoT) devices in the home could provide the opportunity for energy companies to integrate their customer service centres with voice assistants such as Apple’s Siri or Amazon’s Alexa. Increased numbers of IoT appliances also potentially open the door for companies to offer home automation services enabling households to shift energy-intensive processes to times of day when energy is cheaper; both benefitting the consumer and relieving grid constraints.

This second aspect of digitalisation is more unpredictable than digital augmentation of existing infrastructure. While still in the early stages, there are indications that it is blurring the conceptual boundaries of energy systems. Three trends can be identified that demonstrate conceptual blurring in this second digital space:

  1. Digital technologies can be used across multiple functions and geographical scales within energy systems.

It cannot be universally assumed that the primary driver for companies in the digital energy space is to address energy-specific problems. For instance, digitalisation can also be driven by incentives to apply existing technologies to a new sector in ways that generate profit or serve the public interest. Patterns of digitalisation are therefore not guaranteed to be demarcated by the functions or geographic scales associated with the traditional energy system. For example, blockchain technology was originally developed to function as a secure, trustless public ledger underpinning the cyptocurrency Bitcoin. Blockchains are now being developed in the energy sector as distributed trading and smart contract ledgers, as well as distributed ledgers underpinning energy-related cryptocurrency and token schemes.

Commercial example: The blockchain platform currently being developed by the Energy Web Foundation could be used to record energy trading transactions from the local peer-to-peer level through to regional distribution, national or even international markets.

  1. Digital technology businesses increasingly provide services across multiple energy vectors, utilities, and industries.

Building on the first trend, drivers to develop technology products and services can also lead companies to expand operations across boundaries between different energy vectors (e.g. electricity, heat, transport), utilities (e.g. electricity, gas, water, internet), and even broader industries (e.g. energy, health, security).

Commercial example: Hive – a smart home devices and services company – began operations with the production of a smart thermostat. Since then, the company has expanded operations to include remote controlled lighting, water leak sensors, and a variety of home surveillance products. All products are linked via a wifi-enabled ‘home hub’ system.

  1. Services peripheral to the core of the traditional energy system model are stimulated to develop as the system digitalises.

Increased system complexity creates demand in peripheral areas such as cloud computing, cyber security, customer engagement and finance. As incumbent energy companies must adapt quickly to system digitalisation, they may be incentivised to outsource newly-required services rather than develop in-house capacity to meet all demands.

Commercial example: Amazon Web Services now provide services, such as cloud computing and customer engagement, for utility companies including EDF Energy.

Frameworks for a digital environment

Collectively, these trends demonstrate movement towards a far more densely interconnected energy system. Traditional tools used to conceptualise the system, largely based on geography and physical infrastructure, are ill-suited to capturing increased connectivity. This provides a strong impetus to look for other frameworks and models to understand the digital landscape.

The SGAM (Smart Grid Architecture Model) is an electricity-focussed framework designed to conceptualise the evolving structure of a digitalised smart grid. The framework was developed by the CEN-CENELEC-ETSI Smart Grid Coordination Group, as part of the European Commission Smart Grid Mandate M/490.

SGAM – Smart Grid Architecture Model
Source: CEN-CENELEC-ETSI Smart Grid Coordination Group, SGAM User Manual – Applying, testing & refining the Smart Grid Architecture Model (SGAM) Version 3.0, November 2014, p15

Rather than taking physical components of the electricity system as its backbone, the SGAM condenses these into a ‘component layer’. Four additional layers are then built on top: communication layer, information layer, function layer, and business layer. All model layers are ‘interoperable’; allowing for inter-layer exchange and use of information. The model is designed to be flexible for use at different levels of abstraction, and for different purposes. Examples of the types of information that can be mapped onto each of the SGAM model layers, as given by the SGAM User Manual, can be found in the figure below.

Exemplary categorisation of different abstraction levels per SGAM layer (SGAM analysis pattern)
Source: CEN-CENELEC-ETSI Smart Grid Coordination Group, SGAM User Manual – Applying, testing & refining the Smart Grid Architecture Model (SGAM) Version 3.0, November 2014, p16

The SGAM provides an improved tool to model a complex and evolving electricity system in granular detail, as it enables users to move away from the geographically-bounded models demarcated by generation, distribution and transmission. This is necessary to build up more granular pictures of the evolving landscape, including new actors, information flows and business models.

Referring back to the first section of this blog, the SGAM is a helpful tool to support modelling and analysis of the first and third broad trends. By loosening ties to geographically-bounded infrastructure, the SGAM can be used to show how digital technologies, such as blockchain, could operate across non-conventional functions (e.g. energy trading, smart contract records) and geographies (e.g. microgrid, local peer-to-peer, distribution systems) within the electricity system. Inclusion of different layers within the SGAM also supports modelling and analysis of peripheral services that may not be tied to physical electricity infrastructure. For example, it can be used to demonstrate how peripheral services, such as cloud computing or cyber security, interact with a digitalised electricity system via the information, communication or business layers of a smart grid.

However, it should be noted that the SGAM is a tool designed to conceptualise the electricity system in isolation. While system-specific models are useful, they are also limited in that they omit connections that operate across energy vectors, utilities and broader industries. Modelling in system siloes particularly restricts a comprehensive mapping of the data landscape that is intrinsic to energy digitalisation.

Data has characteristics of a non-rivalrous good that can be used multiple times, and for different purposes, with low marginal costs [1]. As well as having multiple uses, the total volume and diversity of energy data is also growing. These factors provide a substantial opportunity for actors, both ‘inside’ and ‘outside’ the energy system, to extract value from that data. For example, detailed information about household energy consumption could be used by demand-response programmes, but also to profile households for cross-selling or up-selling other home products and services, such as smart appliances. In the broader economy, data is increasingly viewed as an asset in its own right [2] and a key component in a company’s business model. Given the value and use-flexibility of data relating to energy, there may even be potential for the commercial value of energy data to overtake the value of the energy services that originally drove the industry.

To reiterate, frameworks that conceptualise digitalisation only within the traditional bounds of the energy system have a blind spot to broader data flows that can be highly influential in the digital landscape. Understanding the bigger picture therefore requires a new approach. This approach will need to cross-reference system or sector-specific models with complementary frameworks that situate energy data in broader technological and business context. To keep pace with commercial developments, modelling energy data flows and impact is an area requiring further research [3].

Policy implications

The content of this blog raises fundamental questions around the scope of the energy system in a digitalised, data-driven world. Correspondingly, policymakers and regulators will need to reconsider the scope of traditional energy policy and regulation in order to serve a new, highly interconnected system. For example, it could be useful to explore whether Ofgem could and should form relationships with new technology-focused Government bodies such as the Office for Artificial Intelligence [4], or whether a new regulatory body (or bodies) is required. The Energy Data Taskforce could be a useful platform for considering these kinds of questions, balancing the need to address energy sector-specific dimensions of digitalisation while also supporting the Taskforce’s commitment to: “Link the energy sector with other emerging data-enabled sectors (e.g. open banking) to enable the development of cross-sectoral products and services (e.g. automatic payment of blockchain-enabled energy bills) resulting from data interoperability.” [5]

Looking to the future, careful, ongoing and reflexive analysis of the evolving digitalised energy landscape will be central to policymakers’ ability to thoroughly evaluate whether energy system digitalisation is contributing towards or undermining stated environmental, social and economic objectives. This will be vital for effective navigation through energy system change.

 


Emily Judson joined the Energy Policy Group as a PhD researcher in September 2018. Her research explores relationships between trends of energy digitalisation and democratisation, focussing on the democratisation of energy data and algorithms. Her university profile can be found here.

[1] This term is used noting that rivalry can be conceptualised as a sliding scale. While data may not be considered a ‘pure’ non-rivalrous good in all circumstances, it is situated on the non-rivalrous side of this spectrum.

[2] For example see: The Economist, 6 May 2017, “The world’s most valuable resource is no longer oil but data”. https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data or “Data’s Identity in Today’s Economy”, MIT Technology Review, 7 April 2016. https://www.technologyreview.com/s/601207/datas-identity-in-todays-economy/

[3] The Open Data Institute’s work on mapping data ecosystems may be relevant – available via https://theodi.org/article/mapping-data-ecosystems/. Crawford and Joler’s “Anatomy of an AI System” (2018), accessible via https://anatomyof.ai/, also provides an example of a more extensive mapping project; producing a visual resource depicting “The Amazon Echo as an anatomical map of human labor, data and planetary resources”.

[4] https://www.gov.uk/government/publications/artificial-intelligence-sector-deal/ai-sector-deal

[5] Energy Data Taskforce Terms of Reference, Annex, page 4. Available via: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/748566/energy-data-taskforce-terms-of-reference.pdf

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