Social and technical differentiation in smart meter rollout: embedded scalar biases in automating Norwegian and Portuguese energy infrastructure

The study examines how automation within energy transitions exhibits uneven scalar effects, focusing on national smart meter rollouts as a foundational layer of automated, digitized energy infrastructure. It asks: what scalar biases characterize the automation of socio-technical energy infrastructure? Smart meters, while installed at the household scale, enable real-time data flows that structure future flexibility markets and automation, making them a critical lens on power and responsibility across scales. Comparing Norway (a near-universal, rapid rollout) and Portugal (a gradual, utility-led rollout), the paper situates the inquiry in energy geographies, where control tends to concentrate at higher scales while social impacts are borne by lower scales. The purpose is to specify who controls and who is made responsible for the social and technical aspects of automation, and why this matters for democratic, user-centric energy futures.

The paper locates its contribution within energy geographies, which analyze how spatial and hierarchical scales shape energy transitions and their socio-material infrastructures. Prior work highlights the concentration of power and technical decision-making in urban and national centers and the vulnerability of marginalised populations to transitions (Rutherford & Coutard, Broto & Baker). Increasing attention is paid to automation and digitalization—smart meters, data infrastructures, consumer access devices, and flexibility markets—and their implications for governance, equity, and demand response (Lund et al.; Hashem et al.; Hledik; Schick & Gad; Saele & Grande). Smart meters are framed as data infrastructures promising monitoring, efficiency, and new value streams (Benzi et al.; Erlinghagen et al.; Stojkoska & Trivodaliev) but also raising questions around benefit distribution, user agency, and socio-technical politics (Sareen & Rommetveit; Stephens et al.; Bulkeley et al.). The review underscores that although smart meters are deployed at households, their effects unfold across multiple scales, making them a prime site to study scalar biases in control and responsibility during automation. The paper adopts a matrix distinguishing control and responsibility across social and technical aspects, drawing on STS notions of centralized versus networked control (Hughes; Edwards; Silvast et al.) and political ecology perspectives on accountability and decentralization (Agrawal & Ribot; Larson & Ribot).

Comparative case study design selecting Norway and Portugal to maximize difference in data sources, system context, and rollout trajectories.

• Norway: Empirical data from an 18-month urban living lab (2017–2019) in Bergen within the PARENT project, with 46 purposively sampled households. A sub-meter monitor in each household simulated advanced smart meter functionality, generating real-time device-level consumption data. Participants accessed data via web and smartphone platforms; interventions included monthly newsletters, three rounds of focus groups, some interviews, pre/post surveys, and a hands-on workshop deconstructing the monitor. The dataset emphasizes household-scale behaviors, perceptions, and imaginaries during the national rollout that progressed from 18% (Jan 2017) to 97% coverage (Jan 2019).
• Portugal: Ethnographic fieldwork over 5 months (2017–2019) with 80 in-depth, semi-structured interviews (50–150 minutes), including over 20 experts discussing smart meters across 15 interviews; observations at sectoral meetings and site visits (e.g., EDP’s Inovgrid in Évora, >30,000 households). Interviewees spanned the DSO/supplier EDP, regulator, executive authority, consultants, researchers, cooperatives, journalists, investors, regional agencies, and municipalities. Focus at national/sub-national scales on regulatory design, concessions, rollout incentives, and the coupling with solar PV uptake. Background context:
• Norway: Predominantly hydropower-based electricity (95% hydro, 3% wind in 2018), high electrification of household end-uses (including heating), comparatively low energy poverty.
• Portugal: High and growing RES share (52% in 2018), mixed end-uses (gas for cooking, solar thermal for water heating), higher energy poverty with social tariff policies. Rollout trajectories:
• Norway: Mandatory rollout coordinated with >120 DSOs; near-universal coverage by Jan 2019; cost ~€300/meter; limited opt-out with penalties; small but visible resistance.
• Portugal: Utility-led pilots from 2009 (Inovgrid) scaling to >100,000 by 2015; accelerated deployment from 2017 (≈600,000 in 2017; ~25% by end-2018; >40%/≈2.5 million by Sept 2019). Regulator provided premium returns for efficiency investments but no national target; meters owned by DSO; users had data access rights.

• Twin scalar biases identified across both cases:
  1. Social aspects of automation are controlled at higher (national/utility) scales while households are responsibilised at the household scale.
  2. Technical aspects are both controlled and held responsible at higher scales, with little to no role for lower scales.
• Norway (household-scale lens):
  • Living lab revealed installation hurdles due to legacy infrastructure and usability issues with apps/algorithms (e.g., device disaggregation problems).
  • Engagement waned for many users; trust concerns about data use; perception of limited influence on rollout modalities and future energy use.
  • Despite informed, tech-savvy participants (some prosumers/EV owners), a strong sense of disenfranchisement persisted.
  • National rollout reached ~97% (2.9 million meters) by Jan 2019; the Elhub data exchange platform (operational Feb 2020) enables supply-side efficiency/flexibility, with uncertain user benefit distribution.
• Portugal (national-scale lens):
  • Smart meters framed as technical network upgrades; public perception of potential cost increases; DSO investment contingent on regulatory recovery mechanisms.
  • Regulatory constraints (e.g., at transformer scale) sought to ensure neutrality in the context of expiring municipal distribution concessions; debates focused on valuation/control of distribution infrastructure and flexibility needs, with minimal consideration of social aspects.
  • EDP advanced value creation via IoT-enabled offerings (e.g., EDP re:dy box), branding smart meters as centrally delivered technical innovation.
• Across both cases:
  • Control over social and technical aspects is concentrated at national/utility scales.
  • Responsibility for social aspects is shifted to households (behavior change, acceptance), while technical responsibility remains centralized.
  • This configuration constrains user agency and narrows social imaginaries of what smart meter-driven smart grids could enable.

The findings address the research question by demonstrating consistent scalar biases in the automation of energy infrastructure despite contrasting national contexts and rollout pathways. Smart meters, though installed at households, are governed as expert-led technical systems, with control over specifications, data infrastructures, and interface design retained at higher scales. Households are responsibilised for behavioral adaptation and acceptance without corresponding control or influence over technical decisions or governance processes. This separation embeds social and technical differentiation: technical aspects risk being dehumanized and insulated from user input, while social aspects are stripped of technical leverage and relegated to individual responsibility. The significance lies in how such biases shape future smart grid trajectories, potentially privileging supply-side efficiency and market logics over democratic, user-centric configurations, thereby limiting multi-scalar deliberation, equity, and the transformative potential of digitalization in energy transitions.

The paper contributes an analytical framework distinguishing control and responsibility across social and technical dimensions to reveal scalar biases in smart meter rollouts. Empirically, it shows that both Norway’s mandatory, rapid rollout and Portugal’s utility-led, incremental rollout embed similar biases: higher scales control both social and technical aspects, households are responsibilised for social aspects, and technical responsibility remains centralized. This socio-technical differentiation constrains user agency and narrows the social imaginaries of smart-grid futures. The study argues for processes that open space for deliberation and reflexive, user-centric governance across scales to counteract these biases. Potential future directions include: designing participatory mechanisms for household and municipal input into meter specifications and data governance; evaluating regulatory models (e.g., concession design, data platform governance) for their distribution of control/responsibility; and assessing how emerging flexibility/IoT markets can equitably share benefits with users.

• Asymmetric, qualitative datasets across cases: Norway emphasizes household-scale insights from a purposively sampled, relatively tech-savvy living lab (n=46), which may not represent broader populations; Portugal emphasizes national/sub-national expert interviews and observations, with limited household-level data.
• The Norwegian living lab used sub-meter monitors to simulate advanced smart meter functions; device disaggregation and app usability issues may differ from actual meter deployments.
• Lack of uniform official reporting in Portugal during the study period limited precision on rollout figures and constrained longitudinal comparability.
• Findings are context-specific and interpretive; while transferable, they are not statistically generalizable.