Smart Metering as a Regulatory and Technological Enabler for Flexibility in Distribution Networks: Incentives, Devices, and Protocols

Abstract

The digital transformation of low-voltage distribution networks demands a renewed perspective on both regulatory frameworks and metering technologies. This article explores the intersection between incentive structures and metering technologies, focusing on how smart metering can act as a strategic enabler for flexibility in electricity distribution. Starting with the Spanish regulatory evolution and European benchmarking, the shift from asset-based regulation and how it can be complemented with performance-oriented incentives to support advanced metering functionalities is analyzed. On the technical side, the capabilities of smart meters and the performance of communication protocols (such as PRIME, G3-PLC, and 6LoWPAN) highlighting their suitability for real-time observability and control are examined. The findings identify a way to enhance regulatory frameworks for fully harnessing the operational potential of smart metering systems. This article calls for a hybrid, context-aware approach that integrates regulatory evolution with metering structures innovation to unlock the full value of smart metering in the energy transition.

1. Introduction

The European Union (EU) Third Energy Package and the Clean Energy for All Europeans legislative framework [1,2] have established not only decarbonization and electrification targets, but also a new regulatory framework for electricity distribution as part of the internal electricity market directives. This paradigm promotes a clear separation of market activities, consumer-centric policies, and digitalization, positioning Distribution System Operators (DSOs) as key facilitators of flexibility and renewable energy resource (RES) integration at the local level through non-discriminatory market mechanisms. The role of DSOs is evolving from network operators to active participants in the energy market, responsible for ensuring system stability, reliability, and efficiency while accommodating a diverse range of distributed energy resources (DERs).

As the role and responsibilities of DSOs increase, the transformation of the distribution grid into a smart, digitally enabled infrastructure becomes mandatory. Smart grids (bidirectional structures that accommodate decentralized energy resources, communication, and flexible loads) are emerging in the energy sector, replacing vertically integrated, unidirectional power flow models with a decentralized, bi-directional architecture (Figure 1).

In this new environment, advanced metering infrastructure (AMI) is no longer used only for automated billing, but rather a strategic player for achieving real-time observability, system operation, and developing user-centric services.

However, this shift in the distribution network paradigm has significant challenges: DSOs must adopt their new roles [3] and responsibilities while having limited visibility of their own assets. As networks become more complex, integrating a growing number of prosumers, flexible loads such as electric vehicles (EV) and heat pumps (HP), and distributed generation such as photovoltaic (PV), the design and deployment of a robust AMI becomes needed. New actors and customer-oriented services will introduce new communication requirements, where interoperable and scalable solutions capable of managing data flows and control signals in near real-time will be demanded.

At the same time, regulatory frameworks must evolve to recognize the operational capabilities and flexibility that AMI systems enable, not only considering capital-, but also operational expenditures (CAPEX and OPEX respectively). This includes the need for regulatory incentives that align with the operational capabilities of AMI systems, ensuring incentives for grid digitalization.

Recent literature further reinforces the importance of this intersection between technology and regulation. Low-carbon and flexible operation in combination with carbon-trading mechanisms, carbon capture technologies, and power-based flexibility illustrates how multi-objective optimization frameworks can combine economic efficiency with regulatory and environmental targets [4]. On the other hand, the integration of renewable energy sources in communities and the management of their inherent uncertainties are critical challenges that can be addressed through advanced data-driven techniques [5], which emphasize the need for data-collection systems, thus highlighting the need for robust regulatory frameworks that support such innovations.

For integrating community batteries and other distribution networks’ flexible resources, a co-optimization framework that simultaneously accesses behind-the-meter and front-of-meter data streams through a hybrid metering architecture highlights how regulatory design can unlock multi-service provision and improve the economic feasibility of energy communities [6]. All these contributions highlight that smart metering should not be understood solely as a technological upgrade, but as a regulatory enabler that bridges system operation with market design.

The main objective of this paper is to explore the dual nature of smart metering: as a technological infrastructure and as a regulatory asset in enabling grid operational services at the distribution level. The research problem addressed in this work is the lack of integrated analyses that connect large-scale smart metering deployments with the regulatory and incentive frameworks that made them possible. While numerous studies investigate the technical performance of communication protocols or the economic implications of regulation, very few works explicitly examine how regulatory design choices can drive the adoption of a specific technology at national scale. This constitutes a clear research gap.

The objective of this paper can be described as: (i) to analyze the regulatory and incentive framework that enabled the mass deployment of metering protocols in the EU, especially PRIME PLC in Spain, (ii) to compare PRIME with alternative communication protocols by in-depth reviewing existing simulation evidence on latency, reliability, and scalability, and (iii) to propose recommendations for future regulatory and technological strategies aimed at enabling flexibility and resilience in distribution networks.

The paper is structured as follows: Section 2 analyzes the economic and institutional logic of distribution network remuneration, with a focus on the Spanish case and comparing it with other remuneration models in the EU. Section 3 reviews the functional architecture of smart meters and compares wired, PLC-based communication protocols with wireless protocols. Section 4 explores the interaction between regulation and technology, proposing remuneration strategies to align incentives with system capabilities and grid utilization rates, which can only be achieved with grid digitalization. Finally, Section 5 provides the main conclusions and future research.

2. Exploitation Models and Incentive Structures

Historically, the Spanish remuneration framework for distribution networks, established under Ley 54/1997 [7] and later refined by Ley 24/2013 [8], has provided long-term investment stability by guaranteeing returns on the Regulated Asset Base (RAB). This approach successfully guaranteed the development of a resilient and high-quality distribution infrastructure, capable of supporting a liberalized electricity market and maintaining security of supply under demanding conditions. The framework was designed to promote investment recovery and anticipate growing electricity demand, enabling DSOs to meet challenging requirements for service continuity and technical robustness. As a result, the distribution grid in Spain today stands out for its capacity to withstand peak events and extreme scenarios, like the fast recovery witnessed during the 2024 Spanish Floods in Valencia or even the more challenging 2025 Iberian Peninsula Blackout, where the Spanish grid demonstrated better resiliency and recovery times compared to the Portuguese grid.

This incentive framework led DSOs to adopt investment strategies oriented toward ensuring security of supply under all operating conditions, including infrequent but critical peak events. As a result, the Spanish distribution network has achieved outstanding technical readiness. The robustness of the grid has become a key enabler of future innovation. Today, this remanent capacity in network capacity, designed as a safety margin, opens the opportunity for regulating flexible connections and dynamic access to the grid during off-peak hours.

Flexibility can be planned either as a temporary solution until network reinforcement is done, or as a definitive solution in areas where operating conditions make it technically and economically viable. As DSOs are responsible for the deployment and operation of advanced metering systems, the regulatory framework must now evolve to value how these systems can support secure and efficient access to available capacity, particularly through flexibility-oriented planning.

2.1. Evolution of the Spanish Regulatory Framework

The liberalization of the Spanish electricity system began in 1997, when a clear separation between regulated and liberalized activities was established. Transmission and distribution were classified as regulated natural monopolies, where:

• There is a market structure where a single company is the provider of the service.
• High infrastructure costs and impracticality of duplicating networks.
• The regulator (CNMC) oversees the remuneration mechanisms.
• Grid investment still follows a reliability over efficiency logic.

To ensure investment stability and security of supply in this liberalized context, Article 16 [7] introduced a remuneration framework based on the RAB. This model guarantees cost recovery and a fixed return on capital for investments considered as necessary to meet electricity demand. By linking remuneration to capital deployment (e.g., substations, transformers, and lines), the regulation promoted grid expansion and technical robustness, which are essential conditions for achieving the service quality levels that have characterized the Spanish distribution network over the past two decades. This model was implemented via successive directives [9,10], which defined a reference network approach for determining the allowed annual remuneration. The formula, presented in Equation (1) links annual remuneration to inflation adjustments and demand evolution, providing a predictable and transparent mechanism for revenue allocation across regulatory periods [11].

$$D_{in}=D_{in-1}·\left(1+{\displaystyle \frac{\mathrm{IPC}-1}{100}}\right)·\left(1+\Delta D·F_e\right)$$

(1)

where:

• $D_{in}=$ the actual year remuneration, in €
• $D_{in-1}=$ the previous year annual remuneration, in €
• $\mathrm{IPC}=$ the consumer price index,
• $\Delta D=$ the variation in demand, in MWh (typical range: 1–5% at LV feeder level);
• $F_e=$ an elasticity adjustment factor that establishes the relationship between demand growth and actual cost increases for the DSOs, in €/MWh

This framework ensured financial predictability while enabling DSOs to align investment planning with long-term system growth, ensuring infrastructure stability and reliability. At the same time, this model allowed:

• Design for resilience and peak readiness: While this design philosophy results in available headroom during normal operating conditions, it also guarantees high reliability and rapid recovery.
• Gradual integration of digital assets: While the regulatory framework has historically centered on tangible infrastructure, it has progressively expanded to recognize the role of digitalization in improving grid performance. Investments in remote control systems and digital supervision are today fully capitalizable under the RAB model.

Further evolution may be needed to reflect the value of data-driven grid-management services, including those built on software, analytics, or real-time observability. These have resulted in a distribution system that is robust, resilient, and capable of absorbing demand growth and new usage patterns. They also create an opportunity: the availability of unused capacity through flexibility services, especially in off-peak periods. This shift would enable more efficient asset use while maintaining the system’s strong quality-of-service standards.

This model aligns with what has been defined as the bridge analogy: Just as toll bridges are financed through fixed charges that ensure their availability regardless of actual traffic volume, distribution networks are funded through stable, regulated tariffs that guarantee their robustness and accessibility at all times. This approach has successfully enabled the construction and maintenance of a highly resilient infrastructure, ensuring that capacity is available when most needed, such as during extreme weather events or coincident demand peaks. These fixed-term charges, included in the electricity bill, support the long-term sustainability and strength of the network. To better understand this example, the bridge analogy presented in

Table 1. Bridge analogy with a distribution network structure.

Element	Bridge Model	Distribution Network
Infrastructure	The bridge itself (CAPEX)	Substations, transformers, cables, smart meters
System Operator	Toll bridge company	Distribution System Operator
Access Pricing	Bridge usage toll	Regulated access fees (peajes de acceso)
Regulation	Toll cap set by public authority	CNMC regulates remuneration and access rules
Usage Goal	Crossing the bridge	Delivering electricity to end-users with quality
Market Design	One bridge per river crossing	Regulated monopoly: One DSO per geographical area

helps clarifying this concept.

While this design guarantees security of supply and quality of service (QoS), it also creates an opportunity: the presence of remaining capacity can now be leveraged through regulated flexible connections. By allowing consumers or aggregators to make use of this available capacity during off-peak hours, the grid can evolve toward more dynamic and efficient utilization, without compromising its robustness and resilience.

2.1.1. Regulatory Shift Toward Performance-Based Metrics

As the energy sector evolved, the Spanish regulatory framework also adapted to address new system needs and emerging opportunities, specially from renewable generation. Key changes came in 2013, when updated legislation [8] introduced new mechanisms to enhance the alignment between capital investment and operational performance. While the RAB-based model was preserved, recognizing the value of long-term, stable infrastructure investments, additional elements were introduced to support digitalization, efficiency, and service quality.

A significant milestone in reshaping distribution remuneration was introduced through the Article 14, which formally established the principle of reasonable remuneration for regulated activities.

This principle ensures that DSOs are compensated in a way that reflects the performance of efficient, well-managed companies in low-risk sectors, while preserving the financial predictability necessary for infrastructure development. This shift was an evolution from the unique focus on capital investment, introducing a more comprehensive approach that considers operational efficiency and service quality as part of the remuneration framework.

2.1.2. Performance KPIs

Performance-based components were introduced into the remuneration scheme, aiming to incentivize operational efficiency and service quality. Among these were KPIs such as SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) (In Spanish, those are defined as TIEPI (Tiempo de Interrupción Equivalente de la Potencia Instalada) and NIEPI (Número de Interrupciones Equivalente de la Potencia Instalada.)), which measure the reliability of supply from the end-user perspective. These KPIs allow for performance-based remuneration adjustments: DSOs that exceed quality thresholds may incur penalties, while those demonstrating improvements are rewarded. Additionally, specific targets for reducing technical and non-technical energy losses have been implemented, particularly in medium- and low-voltage networks.

The most recent regulatory advancement has been achieved in 2019 with the introduction of the COMGES [12] incentive (From the original acronym in Spanish: Componente Gestionable de la Retribución.): a strategic component designed to reward grid-management capabilities beyond traditional metering infrastructure. The COMGES links a portion of the DSO remuneration to how effectively the smart metering systems are used for grid management, establishing indicators, such as:

• Use of metering data for outage localization.
• Availability and continuity of load curve data.
• Quality and resolution of power and voltage measurements.

These mechanisms represent a growing regulatory recognition of smart metering not only as a compliance tool, but as a key enabler of real-time observability.

2.2. Supporting Digitalization Through Capital Investment and Performance Incentives

The Spanish remuneration framework has historically relied on capital-based investment logic, ensuring long-term stability and transparency in the financing of essential infrastructure. Within this model, digitalization is increasingly supported through recognized CAPEX categories, particularly when associated with remote control and supervisory systems. The deployment of advanced metering infrastructure (AMI), distribution automation, and even real-time monitoring solutions is often capitalizable and fully integrated into the regulatory retribution scheme.

While some operational services (such as cloud-based analytics or third-party data processing) still fall outside of traditional asset-based frameworks, the framework has progressively introduced mechanisms to reflect their value.

Recent additions (e.g., COMGES mechanism) already reward the use of digital capabilities for operational improvement. The upcoming regulatory period (2026–2031) [13] offers a perfect opportunity in further aligning remuneration mechanisms with how digital infrastructure is used, particularly for unlocking flexibility at the distribution level.

In this context, the discussion is no longer between CAPEX or OPEX investments, but of expanding the regulatory framework to regulate dynamic and performance-based use of infrastructure. The recognition of flexible services and advanced observability as key components will reinforce the role of smart metering in supporting real-time, decentralized, and customer-centric network operation. To understand how these incentives are currently structured, it is important to analyse how regulated infrastructure investments are remunerated:

• A financial return, based on the Weighted Average Cost of Capital (WACC).
• Annual depreciation, calculated over the asset’s economic life.

For the 2020–2025 regulatory period, the CNMC has set the WACC at 5.58% [14] ensuring a stable and predictable environment for long-term infrastructure investments. At the same time, the evolving nature of network operation, driven by real-time data processing, and decentralized control, opens up new opportunities that go beyond traditional asset categories: While some OPEX such as software services and platform subscriptions are not directly integrated into the RAB, the regulatory framework has begun to explore performance-based instruments that can recognize their contribution to grid management.

Moving forward, the opportunity lies in further complementing the current CAPEX-based model with targeted mechanisms that incentivize flexible and intelligent grid operation, while maintaining the core strengths of financial predictability and infrastructure resilience.

2.2.1. The Metering Infrastructure as a Hybrid Asset

Smart meters and AMI (Figure 2) represent a strategic infrastructure asset within the current remuneration framework. While the physical meter is capitalized and integrated into the RAB, many of the associated digital services are also considered part of broader digitalization efforts and thus eligible for capital remuneration. Smart meters enable capabilities that need to be supported by telecommunications infrastructure and edge processing platforms, which are often capitalizable when deployed as part of distribution automation or telecontrol systems. Far from being limited to billing automation, AMI is already serving as the foundation for new regulatory instruments which link operational performance to economic incentives.

The next step lies in fully leveraging this infrastructure to enable flexibility activation, DER integration, and customer-centric services. This evolution does not require a departure from CAPEX-based logic, but rather its contextual expansion to value how infrastructure is used, not just what is deployed.

2.2.2. Incentives to Unlock Latent Flexibility

The Spanish regulatory framework has provided a stable foundation for building one of the most resilient and reliable distribution networks in Europe. Designed to ensure security of supply under all-weather conditions, the current CAPEX-based model has successfully supported widespread deployment of infrastructure, and smart metering systems.

As the energy transition progresses under the European Commission Third Energy Package [1,2] and new consumption patterns are defined by massive electrification, DERs, and flexible loads, this robust infrastructure now enables new possibilities. In particular, much of the network has latent capacity during off-peak hours or in specific zones.

This presents a valuable opportunity: to expand the scope of incentives toward flexible services that dynamically use existing capacity while preserving robustness and operational security during peak hours.

Targeted regulatory mechanisms, such as performance KPIs or flexibility activation-based remuneration, could complement the existing framework by recognizing the value of grid observability, responsiveness, and customer participation. Rather than replacing the current model, this evolution would build on its latent capacity, unlocking new services that make use of (and further improve) the available digital infrastructure and support the objectives of the Clean Energy for All Europeans Package and Spain’s National Energy and Climate Plan (PNIEC) [13], specifically at the last mile.

2.3. Enabling a Dual Investment Logic for Future-Ready Grids

Building on the strengths of the current model, there is now an opportunity to enable a dual investment logic that supports both long-term infrastructure development and emerging operational capabilities. This approach would preserve the core features of resilience, and stability, while introducing complementary incentives tailored to the needs of a more dynamic and decentralized energy system.

Such remuneration model could include:

• A fixed component: Return on efficient capital investments, maintaining predictability and investor confidence. CAPEX is always deemed neccessary for the deployment of physical infrastructure, but it should not be the only component of the remuneration.
• A variable component: Linked to real-time performance, like energy delivery or flexibility activation metrics.
• Targeted incentives: Focused on grid efficiency, grid utilization rates DER integration, quality of service (QoS), and customer-centric outcomes.

This evolution would not replace the asset investment logic but expand it, enabling DSOs to respond to new challenges while unlocking the value of smart infrastructure already in place.

By recognizing the dual nature of distribution networks (both as physical assets and as dynamic, data-driven systems) this approach would align regulatory incentives with the operational capabilities of modern smart grids.

2.4. Remuneration Models in the EU

Regulatory approaches to distribution remuneration vary in how they balance capital investment, operational efficiency, and innovation. While the Spanish model is firmly defined by an asset-based logic that has enabled robust infrastructure development, other countries have explored complementary frameworks that incorporate performance-based incentives. All of them presents benefits and challenges, reflecting different priorities in balancing cost control, service quality, and innovation.

This section provides a comparative overview of three regulatory paradigms adopted across European jurisdictions: (i) TOTEX-based frameworks, (ii) price-cap regulation (CPI-X), and (iii) efficiency benchmarking through yardstick comparisons. Their implications for digitalization and smart metering deployment are analyzed to illustrate the range of strategies used to incentivize grid modernization and flexibility enablement, and the different outcomes achieved in terms of smart metering deployment and operational efficiency.

2.4.1. TOTEX-Based Regulation: The UK RIIO Model

The United Kingdom (Although the United Kingdom is no longer a member of the EU, its regulatory framework is included in this analysis due to the progressive characteristics of its remuneration model, which may serve as a reference model across Europe.) pioneered the use of TOTEX-based models through its RIIO framework [15], implemented since 2013. This model integrates both CAPEX and OPEX into a unified regulatory allowance:

$$\mathrm{Revenue}=\mathrm{Incentive}\mathrm{Rate}+\mathrm{Innovation}\mathrm{Funds}+\mathrm{Output}\mathrm{Rewards}$$

(2)

This model’s key features include:

• No bias toward CAPEX-based solutions: DSOs are incentivized to choose the most cost-effective approach, whether it involves hardware deployment or service-based contracting.
• Output-based incentives: Metrics include customer satisfaction, reliability indices, and carbon savings.
• Innovation allowances: DSOs receive dedicated budgets for testing new solutions, including demand-side response (DSR) and AMI-based observability.

2.4.2. Price-Cap Regulation (CPI-X): The Nordic Model

In countries such as Sweden, Finland, and Norway, distribution remuneration follows a price-cap logic based on the CPI-X [16], as presented in Equation (3).

$$\mathrm{Revenue}{\mathrm{Cap}}_t=\mathrm{Revenue}{\mathrm{Cap}}_{t-1}·\left(1+\mathrm{CPI-X}\right)$$

(3)

where:

• CPI is the consumer price index (inflation).
• X is the efficiency factor, typically 1–3% per year.

This model creates strong incentives for cost control and continuous improvement, as DSOs must offset inflation with productivity gains.

The CPI-X formula is designed to achieve a balance between cost control, efficiency incentives, and consumer protection. It serves multiple regulatory objectives:

• Limit the impact of price increases on consumers: By capping annual price updates to the rate of inflation minus expected efficiency gains, the formula protects consumers from excessive price hikes.
• Incentivize efficiency: The X factor encourages regulated companies to improve their internal efficiency. Any cost savings achieved beyond the regulatory target can be retained by the company, creating a profit motive for optimization.
• Promote price stability: Linking price adjustments to the Consumer Price Index (CPI) helps stabilize tariffs over time, smoothing the impact of macroeconomic fluctuations.

CPI-X regulation allows regulators to constrain pricing power while giving operators freedom to innovate and reduce costs. This structure has proven particularly effective in countries where distribution systems are mature and cost-efficiency is a central policy goal. In Finland, for example, the regulator also applies bonuses for SAIDI/SAIFI and supports moderate recognition of digital investments, especially when tied to resilience or customer service.

Nevertheless, the CPI-X approach has limitations, specially for large-upfront investments like the ones needed for digital infrastructures. The annual adjustment mechanism may fail to capture the long-term value of smart metering systems, which require significant initial investments but deliver ongoing operational benefits throughout the years.

As a result, while CPI-X models encourage efficiency, they may not sufficiently incentivize the adoption of advanced metering technologies that require substantial OPEX investments.

2.4.3. Efficiency Benchmarking: The German and Dutch Yardstick Models

Germany and the Netherlands [17,18] both apply a yardstick comparison model, based on cost benchmarking across peer companies and with a follow-the-leader approach in mind. This model creates:

• On one hand, transparency and comparability across DSOs.
• But on the other hand, a strong pressure to reduce large-upfront investments.

However, it also introduces a critical disincentive: investments in new or non-standardized digital infrastructure (such as smart metering systems) may increase reported costs in the short term and negatively affect efficiency scores. Despite following the same remuneration logic, the outcomes in these two countries have been quite different.

Despite introducing the Digitization of the Energy Transition Act [19], Germany’s smart metering deployment remains extremely limited, with coverage levels far below 10%. The principal reasons behind this are:

• Metering mandate: Smart meters are only required for customers with annual consumption above 6000 kWh or installations with generation above 7 kWp, excluding most residential users.
• Strong cybersecurity requirements: The national Smart Meter Gateway must meet highly specific technical certifications, raising costs and deployment delays. With no clear path for regulatory cost recovery, this has led to a conservative approach to metering technology.
• Remuneration conflict: Yardstick benchmarking penalizes early adopters (unless they can demonstrate significant efficiency gains) making DSOs risk-averse.

In contrast, the Netherlands has achieved nearly 100% smart meter coverage under a unified framework because of the following factors:

• Clear legislative mandate: National rollout obligations were introduced via the Electricity and Gas Act [18], with defined milestones.
• Regulatory support: Initial rollout investments were excluded from efficiency benchmarking calculations, protecting DSOs from short-term penalties.

These contrasting outcomes illustrate that yardstick regulation is not the only influence factor in metering rollout: its effectiveness depends on how complementary rules are structured. In Germany, regulatory fragmentation and misaligned cost signals created several disincentive to deploy smart meters. In the Netherlands, early-stage investments in smart metering were excluded from efficiency benchmarking. This regulatory approach prevented DSOs from being penalized for forward-looking digital infrastructure projects not yet reflected in comparable cost baselines. By decoupling these investments from the standard benchmarking regime, specially during initial deployment stage, the regulator enabled innovation without compromising cost-efficiency metrics. This approach was the key in supporting the rapid and universal rollout of smart meters.

2.4.4. Comparison and Implications for Smart Metering

Table 2. Remuneration Models: Infrastructure Bias and Performance Incentives.

Model	CAPEX Bias	OPEX Incentives	EU Context
RAB	High	Low	Spain, Italy, France
TOTEX (RIIO)	Low	High	UK
Price Cap (CPI-X)	Low	High	Nordics
Benchmarking	Low	Low	Germany, Netherlands

summarizes the main remuneration models applied across European DSOs, classifying them by their bias toward capital expenditure (CAPEX), the weight of operational expenditure (OPEX) incentives, and representative countries.

The different remuneration models across the European Union and the United Kingdom reflects different priorities, historical investment patterns, and regulatory cultures. Traditional RAB-based frameworks (still prevalent in countries like Spain, France, and Italy) have delivered robust, capital-intensive infrastructures that ensure reliability and security of supply. These models offer financial predictability, support long-term planning, and have successfully enabled widespread deployment of smart metering infrastructure.

Other approaches, such as the UK’s TOTEX-based RIIO model or the Nordic CPI-X price-cap frameworks, introduce additional flexibility by incentivizing cost efficiency and output-based performance. These models provide DSOs with the autonomy to choose the most effective combination of capital and operational solutions, supporting innovation in areas like data analytics, and customer-oriented services.

Benchmarking approaches, such as those in Germany and the Netherlands, introduce peer-based cost comparisons but often lack clear incentives for innovation, especially when modernization costs are not properly decoupled from standard efficiency baselines (like the initial metering rollout in The Netherlands). This highlights that regulatory design is critical in determining the alignment between infrastructure investment and smart grid functionality.

As the comparison indicates, countries that have adopted TOTEX or CPI-X frameworks show greater alignment between regulatory incentives and the operational capabilities of smart metering. In these contexts, DSOs have a clearer vision to:

• Deploy high-resolution, interoperable smart meters.
• Integrate metering data into demand-side management (DMS) and flexibility.
• Invest in cloud-based analytics or local control via edge computing.

Across all models, the key insight is that regulatory design must evolve in parallel with the functional evolution of distribution networks.

Incentives should reflect not only asset deployment but also the ability of smart metering systems to support observability, flexibility, and customer engagement. For example, the Spanish distribution network, developed under this stable regulatory framework, is characterized by its high resilience, quality of service, and strategic readiness. The planning steps followed over the past two decades have ensured that the infrastructure can withstand peak loads and adverse conditions. As a result, many parts of the network now present available capacity during off-peak hours, thus creating a valuable opportunity to introduce regulated flexible contracts. By leveraging this remanent capacity without compromising reliability, DSOs can support new customer services, increase asset utilization, and contribute to a more efficient and participatory energy system.

2.4.5. Remuneration Models and the Smart Metering Rollout in Europe

The deployment of smart metering infrastructure across Europe has been significantly influenced by the underlying remuneration models adopted in each country. These models shape the incentives for DSOs to invest in advanced metering technologies, which are critical for enabling modern grid functionalities such as real-time monitoring.

To complement the conceptual discussion with empirical evidence, Figure 3 represents the state of smart metering deployment across the EU-28. The rollout remains highly heterogeneous: European countries such as Spain (100% by 2019), Italy (95% by 2020), and France (90% by 2021) have achieved near-universal coverage, primarily based on PLC technologies. The Netherlands has also completed a nationwide rollout (99% penetration by 2021), facilitated by clear legislative mandates and regulatory protection of digital investments. Nordic countries such as Sweden and Finland have reached high penetration (>90%), although often using wireless GPRS or 4G-based solutions. In contrast, several Member States show slower progress in smart metering rollout: Germany (10% as of 2023) for example is the perfect case where restrictive legislation and yardstick benchmarking have hindered progress.

These disparities demonstrate that remuneration models and regulatory frameworks are not only theoretical but critical enablers of deployment. Spain’s case reflects the stability of a CAPEX-oriented model combined with regulatory mandates for universal rollout, while the UK’s RIIO framework has supported pilots and incremental adoption under a TOTEX logic. The Dutch success aligns with the exclusion of smart metering rollout costs from benchmarking calculations, allowing DSOs to invest without efficiency penalties. By contrast, Germany illustrates the disincentives of yardstick benchmarking and stringent certification, where regulatory barriers slowed deployment despite EU directives.

The numerical evidence confirms that the design of remuneration mechanisms directly shapes both the pace of deployment and the technical architectures adopted.

2.4.6. Ongoing Regulatory Pilots and Sandboxes in the EU and Beyond

Multiple countries in the EU and beyond have already implemented pilot projects and regulatory sandboxes. These initiatives provide empirical evidence of how remuneration models can evolve to support smart metering, setting the stage for demand-side flexibility, and decentralized energy integration.

Table 3. Representative regulatory pilots, sandboxes, and incentive schemes worldwide.

Ongoing Pilots and Sandboxes Enabling Flexibility-Oriented Remuneration
Country/Region	Project(s)	Mechanism	Focus & Outcomes
A. Europe
Spain [23,24,25,26]	S2F I-Flex	National regulatory sandbox DSO-led pilots	Flexibility activation at LV level Consumer and aggregator participation Demonstration of demand-side response Propose regulatory reform
Germany [27,28,29,30,31]	SINTEG Netzlabor Sonderbuch FlexQGrid	National-level showcase pilots DSO “living labs” applications	Tested large-scale integration of RES, DR, storage Increased PV hosting capacity Provided regulatory guidelines for flexibility
UK [32,33]	Ofgem Sandbox RIIO NIC/NIA	Regulatory sandbox Innovation incentives	Peer-to-peer energy trading and flexible tariffs MW-scale contracted flexibility Deferred reinforcements
Sweden [34,35,35]	Stockholm Flex (sthlmflex)	Regional flexibility market TSO-DSO coordination	Market-based congestion management Reduced grid stress in Stockholm Provided a model for EU TSO-DSO cooperation
Italy [36,37]	UVAM	Pilot regulation Ancillary services	Opened balancing markets to aggregated DER < 1 MW Enabled aggregator participation Scaled into permanent market design
B. Asia-Pacific
Australia [38]	Project EDGE	Incentive-based pilots Regulatory sandbox	200 MW of DR capacity demonstrated Creation of wholesale DR rule Dynamic Operating Envelopes (DOEs) concept.

summarizes the most representative examples. They range from sandbox-based exemptions (Spain’s S2F and I-Flex projects, Ofgem’s Innovation Sandbox in the UK and the Australia’s Project EDGE) to large-scale pilots (Germany’s SINTEG, Sweden’s Stockholm Flex) and performance-based incentive schemes (Italy’s UVAM aggregator participation).

These projects confirm that regulatory experimentation delivers measurable outcomes: Spain and Germany demonstrated DSO-orchestrated flexibility activation, the UK and the Netherlands enabled peer-to-peer and community trading under temporary exemptions, the Nordic countries tested TSO-DSO coordinated markets. These initiatives are shaping long-term regulatory reforms and illustrating that flexibility-oriented remuneration is already operationally feasible.

3. Metering Technologies and Protocols

Smart metering technologies are at the core of the digital transformation of electricity-distribution networks, enabling a shift from unidirectional billing systems to multifunctional platforms for near-real-time observability, control, and automation. As low-voltage grids become increasingly populated by DERs, EVs, and other flexible residential loads, the smart meter evolves from a passive measurement device into an active sensing and communication node.

This section explores that evolution by analyzing the architecture, capabilities, and operational models of modern smart meters and the communication protocols that integrate them into the broader energy data infrastructure. The first part characterizes device-level features, focusing on typological classifications, and metrological performance. The second part reviews the communication technologies and protocol stacks that enable interoperability, scalability, and control in AMI systems. By combining device-level and network design perspectives, this section aims to provide a comprehensive assessment of the technical readiness and strategic importance of smart metering within digitalized and decentralized power systems.

3.1. Smart Meters Taxonomy

The deployment of smart metering systems across Europe has followed heterogeneous approaches, reflecting diverse regulatory priorities and technical standards. To understand their role as enablers for active grid management, it is essential to classify smart meters not only by their physical characteristics but also by their functional capabilities.

From a regulatory perspective, smart meters are typically categorized according to their metering accuracy class, communication features, and the type of installation (residential, commercial, or industrial). The European Commission and standardization bodies (e.g., CENELEC, IEC) have developed minimum functional requirements, but national implementations vary. In Spain, smart meters are subject to different regulations [39], which define technical criteria for data acquisition, and remote reading capabilities. A more detailed classification, aligned with functional roles in the grid, is presented below and depicted in Figure 4.

• Type 1 and type 2 m: They are not part of advanced metering infrastructure (AMI) systems. Instead, they are high-precision instruments integrated into SCADA and low-latency architectures, often communicating via IEC 61850 [40] or MODBUS TCP/IP. Therefore, they fall outside the low-voltage-oriented regulatory control requirements.
• Type 3: Installed at MV or LV connections for industrial consumers or distributed energy resources (DERs), are CT-connected and may support protocols such as DLMS/COSEM or MODBUS. Management and control is needed at this level.
• Type 4 and type 5: Fully governed by the smart metering obligations. Remote management functionality has been mandatory for all meters since 2018 [41].

The progression towards more advanced metering devices reflects an increasing alignment with smart grid requirements. In Spain, the full rollout of smart meters for low-voltage customers was completed by 2019, predominantly using PLC-based Type 4–5 devices [42] configured for hourly measurement and daily data transmission. These meters were designed to support billing automation and load profiling under time-of-use tariffs, aligning with the regulatory and functional requirements at the time of deployment.

As distribution networks evolve toward more dynamic, bidirectional operation, new use cases are emerging (such as event-driven telemetry, distributed flexibility coordination, and low-latency applications). Addressing these requires not only technological improvements but also regulatory mechanisms that recognize the strategic role of advanced metering in active grid management. By building on the existing infrastructure and adapting incentives accordingly, DSOs and aggregators can unlock greater value from smart metering in supporting a more responsive and decentralized electricity system.

3.2. Network Topologies for Smart Metering

The architectural design of communication infrastructures within distribution networks relies heavily on the selected topology for data exchange. Topologies not only determine how devices are interconnected, but also define the scalability, resilience, and operational complexity of the system. While the comparative assessment presented in

Table 4. Network topologies comparison—Part I: Topologies for wired network. L: Low. M: Medium. H: High. Arrows indicate advantages (upwards) and disadvantages (downwards).

Bus network topology: All nodes connected to a shared communication line.
Scalability: L	Redundancy: L		Complexity: L	Latency: M
Advantages			Disadvantages
↑ Simple to implement and extend ↑ Minimal cabling requirements ↑ Well-suited for linear networks			↓ Single point of failure on the bus line ↓ Performance degrades with more nodes ↓ Difficult to isolate faults
Use case:
SCADA field buses (RTUs, IEDs) Narrowband Power Line Communication (PLC) for LV feeders.
Tree network topology: Hierarchical, nodes connected as parent-child
Scalability: H	Redundancy: M		Complexity: H	Latency: M
Advantages			Disadvantages
↑ Scales well across hierarchical regions or zones ↑ Natural fit for structured power-distribution networks ↑ Easy to manage data aggregation from leaves to root			↓ Single point of failure at higher branches ↓ Unbalanced traffic and load at intermediate nodes ↓ Difficult rerouting if branch links fail
Use case:
Aggregated AMI or SCADA networks with tiered concentrators MV LV communication architectures structured by transformer-meter hierarchy
Ring network topology: Closed communication loop.
Scalability: M	Redundancy: H		Complexity: M	Latency: M
Advantages			Disadvantages
↑ Redundancy enhances fault tolerance ↑ Predictable and orderly data routing ↑ Can reroute traffic if a single link fails			↓ Complex reconfiguration after failures ↓ Latency increases with number of nodes ↓ Not suitable for dynamic topologies
Use case:
Fibre optic rings connecting primary and secondary substations WAN backbone for substation automation and control

and

Table 5. Network topologies comparison—Part II: Topologies for wireless networks. L: Low. M: Medium. H: High.

Star network topology: All nodes connect individually to a central gateway
Scalability: M	Redundancy: L		Complexity: L	Latency: L
Advantages			Disadvantages
↑ Simple deployment and management ↑ Low latency due to direct communication ↑ Ideal for centralized architectures and polling systems			↓ Central hub is a single point of failure ↓ Limited communication radius without repeaters ↓ Low resilience against topology changes or load spikes
Use case:
AMI systems using GPRS or NB-IoT-based smart meters Legacy meter reading with GSM modems connected to a central HES
Mesh network topology: Each node connects to multiple other nodes.
Scalability: H	Redundancy: H		Complexity: H	Latency: M
Advantages			Disadvantages
↑ Self-healing and highly fault tolerant ↑ Dynamic routing adapts to topology changes ↑ Optimized bandwidth use through localized forwarding			↓ High implementation and configuration complexity ↓ Increased routing overhead and latency variability ↓ Challenging diagnostics and maintenance
Use case:
Wireless smart metering networks based on 6LoWPAN [46] or IEEE 802.15.4 [47] Smart city IoT platforms with decentralized sensor and actuator nodes

remains qualitative in nature, it is grounded in an in-depth bibliographical review, including performance analyses, studies, and review papers that address bus, star, ring, tree, and mesh network topologies [43,44,45]. A simplified categorization (e.g., high, medium, low) regarding four key characteristics (scalability, redundancy, network complexity and expected latency) is proposed.

In the context of power systems (where reliability, latency, and scalabiliy are critical) each topology offers a unique set of trade-offs. Table 4 and Table 5 present a comparative analysis of five representative communication topologies, highlighting their structural characteristics and typical use cases. This comparison is done on the basis of the layer 1 (physical layer) and layer 2 (data link layer) of the OSI model, as shown in Figure 5.

3.3. Communication Protocols for Smart Metering

The performance of AMI depends not only on the capabilities of the metering devices themselves but also on the robustness, scalability, and adaptability of their communication protocols. These protocols and infrastructures govern how data are transmitted between meters, data concentrators, and central head-end systems, and they directly affect latency, network reliability, data granularity, and integration with grid control platforms.

Four representative communication protocols widely used or emerging in smart metering are compared: two wired-, PLC-based protocols (PRIME-PLC [48,49] used in the Iberian Peninsula, and G3-PLC [50] used in France), and two wireless-based protocols (6LoWPAN [46] based on meshed-type networks, and LoRa [51] for point-to-point communication). Each has been deployed with varying degrees of success in European low-voltage networks, and each exhibits specific strengths and limitations depending on the application context.

A series of benchmarking parameters can be analysed for comparing these representative protocols, organized in the following three groups (A–C):

A.

Architectural design:

1.

Routing and topology.

2.

IPv6 Integration.

3.

Modulation and channel access.

B.

Communication performance:

4.

Data Rate: Effective throughput in AMI deployments.

5.

Latency: Measured delays in transmitting and receiving data.

6.

Robustness (modulation): Signal reliability under noisy environments.

C.

System impact:

7.

Scalability and Maturity: Real-world deployment scale, stability.

8.

Interoperability: Integration with other third-party systems.

9.

Security: Encryption, authentication and protection schemes.

10.

Hardware Simplicity (Cost): Complexity and cost of hardware.

3.4. Wired-Based Communication Protocols

Although PLC-Based protocols make use of narrowband power line communication over existing electrical infrastructure, they differ significantly in architectural design, protocol stack maturity, and alignment with modern metering standards [52].

3.4.1. PRIME (PoweRline Intelligent Metering Evolution)

PRIME is a narrowband power-line communication (PLC)- based protocol used in Spain. It operates in the CENELEC A-band (9–95 kHz) and it is based on OFDM (Orthogonal Frequency-Division Multiplexing) modulation for noise and narrowband interference mitigation. The PRIME protocol is designed for bus-based or radial topologies and operates over existing low-voltage distribution cables, avoiding the need for additional wiring. While its theoretical data rates range from 20 to 130 kbps [48,49], effective throughput is highly dependent on line conditions, network density, and electrical interference.

PRIME’s primary advantage lies in its ability to leverage existing infrastructure, enabling rapid deployment with minimal physical intervention. Its centralized architecture aligns well with the hierarchical network topologies found in legacy LV distribution systems. This alignment, together with its proven scalability in field deployments, has made PRIME the protocol of choice in countries such as Spain and Portugal. While effective for periodic load profiling and billing automation, PRIME’s design is optimized for centralized polling rather than event-driven architectures.

Its performance may be affected by high levels of DER-induced noise or complex impedance scenarios. In such contexts, complementary approaches (e.g., localized supervisory devices or hybrid communication solutions) may enhance real-time visibility when needed.

The protocol exhibits some performance constraints that call for innovation: Signal quality deteriorates significantly in the presence of electrical noise caused by massive DER penetration [53,54], and impedance mismatches, quite characteristic in power systems. Scalability becomes a concern in dense or meshed LV networks, particularly where logical routing cannot be aligned with physical conductors. Additionally, PRIME is not well suited for event-driven or low-latency applications, as it is based on a polling-based architecture (i.e., a concentrator that sequentially polls meters) and shared medium limit responsiveness and real-time observability.

3.4.2. G3-PLC

G3-PLC is a robust, also OFDM-based PLC communication protocol designed for low-voltage distribution networks. It supports mesh-network based topology (i.e., devices are able to communicate with each other rather than through a central supervisor as in PRIME) and IPv6 compatibility, while the MAC layer follows a CSMA/CA approach with dynamic routing [50]. Adaptive modulation allow G3-PLC to operate under varying line conditions, with typical data rates between 10 and 100 kbps, depending on impedance and noise levels.

G3-PLC improves upon traditional PLC by supporting IP-based integration, enabling greater flexibility and resilience. This ability to natively support IPv6 facilitates integration into modern data platforms and allows smoother transitions to hybrid AMI architectures.

Despite its improvements, G3-PLC remains constrained by the physical characteristics of the power line medium. Inverter-based DERs, EV chargers, and reactive loads introduce electromagnetic interference that degrades signal quality. Mesh routing can lead to increased latency and instability in topologies with low signal-to-noise ratios. Like other PLC solutions, G3-PLC is not well suited for low-latency or event-driven applications where real-time responsiveness is critical. But the main drawback is that it requires more computational resources than PRIME, which may limit its deployment in low-cost residential meters.

3.5. Wireless-Based Communication Protocols

Wireless communication solutions for smart metering offer an alternative to power line or wired connections by transmitting data over the open air, effectively decoupling the communication channel from the physical electrical infrastructure. This separation is particularly beneficial in scenarios where PLC communication is degraded due to network impedance variability, signal attenuation, or supraharmonic interference caused by inverter-based DERs [53]. Wireless protocols support scalable topologies and offer additional degrees of freedom for routing, redundancy, and deployment flexibility. Among the wireless technologies relevant to AMI deployments, two protocols appear to be the most suitable ones for their maturity and technical advantages: LoRa [55] and IETF 6LoWPAN-based mesh networks [46]. These protocols differ widely in terms of frequency spectrum, transmission power, routing strategies, and stack integration, thus representing a broad spectrum of wireless design philosophies and protocol architectures. But the previously mentioned protocols have something in common: They are all based on communication frequencies typically ranging from 440 MHz up to 900 MHz. In other words, sub-1GHz frequencies. These set of frequencies have proven to be the preferred choice for developments in lossy environments.

3.5.1. LoRa (Long Range)

In the case of energy-saving and long-range communication, the main representative is LoRa: A proprietary low-power wide-area network (LPWAN) technology that operates in the unlicensed ISM bands [55]. It uses chirp spread spectrum modulation to achieve long-range communication with minimal energy consumption. It is based on a star topology, where end devices communicate directly with gateways. LoRa offers low data rates (typically between 0.3 and 50 kbps) with very long communication ranges (up to 15 km in rural areas).

LoRa’s key strength lies in its infrastructure simplicity and long-range capability, enabling distant gateway deployments in wide geographic areas. Its ultra-low power consumption makes it ideal for battery-powered or passive devices, including smart meters in rural or hard-to-reach locations. LoRa’s star topology allows for easy scalability, as new devices can be added without complex routing protocols or network reconfiguration.

Despite its efficiency, the protocol’s reliance on Aloha-based medium access introduces significant scalability limitations in dense environments, leading to collisions and packet loss. Downlink communication is constrained by strict duty-cycle limits, hindering real-time control or event acknowledgment. LoRa is best suited for periodic reporting scenarios and does not support IP-based protocols natively, complicating integration with modern AMI platforms.

3.5.2. 6LoWPAN over IEEE 802.15.4

6LoWPAN (IPv6 over Low Power Wireless Personal Area Networks) [46] is a lightweight adaptation layer that enables the transmission of IPv6 packets over IEEE 802.15.4-based wireless mesh networks. It typically operates using devices with limited processing and energy resources. Routing is handled via a routing protocol for low-power and lossy networks (RPL), and transport protocols such as CoAP and LwM2M enable application-layer interaction with constrained devices. Data rates typically reach up to 150 kbps, with mesh topologies supporting hundreds of nodes. Its main advantage is its fully IP-compliance, allowing seamless integration with modern SCADA, cloud platforms, and IoT infrastructure.

It supports event-driven communication and is well suited for applications requiring local intelligence or edge processing. Its mesh topology enhances coverage in dense urban areas and facilitates redundant paths in case of node failure or interference.

Nevertheless, it is worth noticing that effective deployment requires careful planning of node density and placement, as range per hop is limited and signal propagation is sensitive to environmental obstacles. The protocol’s multi-hop communication can introduce latency if routing tables are not optimal or congestion occurs due to large number of devices. Still, it is an emerging technology: 6LoWPAN represents a promising evolution for event-driven and mesh-based telemetry, particularly in pilot deployments or targeted use cases, but still needs to be validated in large-scale AMI scenarios.

3.6. Performance Comparison: Latency, Scalability, Interoperability

The choice of communication protocol must balance multiple factors: deployment environment (urban vs. rural), required data frequency, interoperability goals, and long-term maintenance costs. While PLC technologies remain dominant in countries with existing AMI infrastructure (e.g., Spain, France, Italy), wireless protocols are gaining traction as DSOs look for greater modularity, interoperability, and responsiveness.

The practical utility of a communication protocol in smart metering deployments depends not only on its physical and logical architecture, but also on its performance under real-world constraints.

Three metrics from the previously mentioned ones are particularly critical in the context of digitalized low-voltage networks: latency, scalability, and interoperability. These dimensions determine the extent to which metering systems can support time-sensitive applications, grow with increasing device density, and remain adaptable to evolving grid architectures.

3.6.1. Latency: Data Transmission Delay

Latency refers to the time delay between the generation of a data packet at the meter and its successful reception by the central system or data concentrator. Low latency is essential for event-driven applications such as fault detection and isolation, voltage and frequency support in near-real time and flexibility activation with fast-response flexible loads. Comparing the previously mentioned protocols, the following performance characteristics can be summarized:

• PRIME: Typical end-to-end latency ranges from several seconds to minutes, depending on line noise and network congestion. PRIME is not optimized for real-time operation and relies on periodic polling rather than push mechanisms.
• G3-PLC: Offers improved latency over PRIME due to mesh routing and adaptive modulation. Typical latency under stable conditions, performance may degrade in high-impedance branches or dense mesh topologies.
• LoRa: Not suitable for low-latency applications. Uplink messages may experience delays of up to several minutes, particularly under duty-cycle constraints or in dense deployments. Downlink capacity is extremely limited.
• 6LoWPAN: Designed for low-latency applications, especially with event-driven messaging. Hop-to-hop latency is typically under 100 ms, with total delays under 1 s for multi-hop networks in field tests. Ideal for near-real-time event-driven telemetry.

3.6.2. Scalability: Network Expansion and Node Density

Scalability is defined as the ability of the communication protocol to maintain acceptable performance (throughput, reliability, energy consumption) as the number of connected devices increases. High scalability is crucial in urban LV networks with thousands of meters, especially in dense residential areas.

Again, the protocols can be compared as follows:

• PRIME: Scalability is limited by the centralized concentrator model and the sensitivity of the PLC channel to electrical noise. Networks above 300–400 nodes per concentrator may experience polling delays and significant data loss.
• G3-PLC: Improves scalability through mesh routing and auto-discovery, allowing logical topologies to adapt to physical grid reconfigurations. Still constrained by the physical (power line) layer limitations regarding impedance and noise.
• LoRa: Star topology and Aloha-based MAC limit scalability. As the number of nodes increases, packet collisions and retransmissions grow exponentially. Requires duty-cycle management and more gateways for large-scale deployment.
• 6LoWPAN: Highly scalable via multi-hop mesh and IPv6-based addressing. RPL enables efficient routing in large deployments of end-nodes, though performance depends on link quality and parent selection algorithms.

Each protocol must be evaluated in the context of the grid’s topology, regulatory framework, and operational objectives. PRIME-based deployments, such as those in Spain, demonstrate that centralized PLC systems can deliver high reliability for AMI functions. Where event-driven telemetry is required (e.g., flexibility activation), complementary devices or hybrid communication layers can be introduced without disrupting the core metering infrastructure.

3.6.3. Interoperability: Protocol Flexibility and Integration

Interoperability reflects the protocol’s ability to integrate with other systems, platforms, and devices, including legacy telemetry systems operated by DSOs, as well as aggregators and third-party flexibility platforms. High interoperability is increasingly tied to the adoption of standardized, open protocols (e.g., IP-based communication, DLMS/COSEM, CoAP).

• PRIME: Although being an open and standardized protocol supported by the PRIME Alliance, its commercial deployments often rely on manufacturer-specific firmware for network management, diagnostics, or firmware updates. As a result, integration with modern IP-based platforms may require protocol converters or adapters (PRIME is not IPv6-native). Nevertheless, PRIME adopts DLMS/COSEM as its data model, which ensures a high degree of interoperability within the energy sector, far beyond what is possible with other fully proprietary solutions which lack standardization and open data-exchange frameworks.
• G3-PLC: Supports 6LoWPAN and native IPv6, enabling easier integration with distributed control systems and internet-based services.
• LoRa: Proprietary modulation with limited support for standard IP protocols. Integration requires middleware adaptation layers, which can reduce interoperability.
• 6LoWPAN: Fully IP-compliant, allowing seamless integration with CoAP, LwM2M, and cloud-native platforms. Supports heterogeneous device networks and modular upgrades. This makes it highly interoperable with modern IoT and smart grid applications, leveraging multi-service communication in smart cities environments.

3.6.4. Simulation-Based Evidence

Advanced metering infrastructures impose strict requirements in terms of latency, packet delivery reliability, scalability, and cost. These characteristics are often difficult to assess solely from protocol specifications, making performance evaluation under realistic deployment conditions an essential complement to technical descriptions.

For this reason, despite simulations are not part of this work, a critical part of the research has investigated simulation models (using tools such as NS-3 (version 3.45), OPNET (version 14.5), or MATLAB (version R2025a Academic)) to assess communication behavior in scenarios directly related to smart metering. These studies typically model adverse conditions such as impulsive noise on power lines, dense node populations in wireless networks, or multi-hop topologies with packet fragmentation. They report quantitative outcomes including throughput, packet delivery ratio, and end-to-end delay, thereby providing validated insights into protocol behavior without requiring duplication of results already established in the literature.

The evidence highlights the challenges of each technology: PLC protocols must cope with noise and line impedance variations, LoRaWAN and other wireless protocols must manage collisions and duty-cycle limitations in dense deployments, and moreover 6LoWPAN must address fragmentation and routing inefficiencies in large meshes. As a result of this in-depth review work,

Table 6. Representative simulation-based evaluations of smart metering communication protocols.

Simulation Studies on PRIME, G3-PLC, LoRaWAN, and 6LoWPAN for Smart Metering
Protocol	References	Simulation Setup	Key Findings
PRIME PLC	Matanza et al. (2015) [56]	MATLAB-based PLC channel with impulsive noise modeling	Performance degrades under bursty noise Proposed enhanced coding improves reliability
G3-PLC	Buayairaksa et al. (2013) [57]	Testbed and simulation with smart meter traffic and impulsive noise	Over 99% message success rate Robust to impulsive noise compared to PRIME
PRIME G3-PLC	Matanza et. al. (2013) [58]	Performance evaluation of two narrowband PLC systems: PRIME and G3	Real channel measurements from Spanish LV networks Statistical models of transfer functions and noise Simulation of PRIME and G3 OFDM performance
PRIME G3-PLC	Casella et. al (2024) [59]	Comparative review of NB-PLC standards (PRIME, G3-PLC and IEEE 1901.2)	PLC (PRIME, G3) identified as viable wired backbones Wireless options considered complementary Highlights trade-offs in latency, cost, and coverage
LoRaWAN	Jebroni et al. (2020) [60]	MATLAB and city-wide propagation measurements	Achieved ∼98.5% packet reception rate Demonstrated scalability with careful planning
LoRaWAN	Varsier et al. (2023) [61]	NS-3 dense-network scenarios	Throughput limited to ∼100 bps per device Capacity limits in dense deployments as a key limitation
6LoWPAN	Chen, Brown, Khan (2014) [62]	OPNET Simulation staggered link design	Fragmentation increases latency and retransmissions Packet aggregation increases throughput capacity
6LoWPAN	Kippke et al. (2025) [63]	Meshed, smart-lighting network based on IEEE 802.15.4	Use of public infrastructure for smart metering application Meshed networks can achieve high reliability (self-healing)

consolidates representative findings from simulation-based research, offering a comparative view of how these protocols perform in practice. This overview complements our regulatory and architectural analysis by anchoring the discussion in empirical evidence from the literature, thus clarifying the strengths and limitations of each option for smart metering applications.

These perspectives reinforce the conclusion that PLC technologies (notably PRIME and G3) are the most practical wired solutions for large-scale AMI, while wireless technologies are best positioned as complementary options in specific contexts and emerging contexts with shared public infrastructure retrofitting.

4. Aligning Technology Capabilities with Regulatory Incentives

The previous sections demonstrated that smart metering is no longer confined to consumption registration: it has evolved into a foundational infrastructure for observability, automation, and distributed control in low-voltage distribution networks. Modern metering systems range from simple metering endpoints to multi-protocol gateways and supervisory devices with edge intelligence, each imposing specific requirements on latency, bandwidth, data granularity, and power availability.

Wired technologies such as PRIME and G3-PLC continue to dominate many European contexts due to their compatibility with radial LV topologies. However, their structural limitations (electromagnetic interference, impedance variability, and the topological constraints) are becoming more apparent in networks characterized by inverter-based DERs. While G3-PLC introduces IP compatibility and mesh-routing capabilities, its effectiveness remains limited by the shared medium and lack of path redundancy, proper limitations for bus-based and three-based topologies.

Wireless protocols such as 6LoWPAN, and LoRaWAN offer flexible alternatives, each with distinct advantages and disadvantages: 6LoWPAN works well in dense urban deployments with clustered topologies and device-level intelligence but is sensitive to environmental factors and added latency in highly-constrained environments. LoRaWAN provides exceptional long-range coverage and minimal infrastructure requirements but struggles in high-density scenarios, requiring additional placement for gateways and thus increasing the overall cost of deployment. These contrasts reveal a critical insight: no single communication protocol can satisfy all AMI use cases or flexibility requirements.

The technical architecture of metering infrastructure must be understood as highly context-dependent. Therefore, regulatory frameworks must evolve to reflect this complexity: by recognizing the diverse operational roles of smart meters and by aligning incentives with infrastructure capabilities.

4.1. Remuneration Models and Technology Adoption

The selection of metering technologies is influenced not only by technical specifications, but also by regulatory context, infrastructure legacy, and strategic priorities. In asset-based frameworks, technologies that align with established planning logic, leverage existing infrastructure, and support large-scale deployment tend to be more used. Not being a constraint, this alignment has enabled rapid modernization of the low-voltage network under a unified, interoperable architecture. PLC protocols (particularly PRIME) have become the preferred choice of AMI rollouts in the Iberian Peninsula. Their adoption was not just a result of cost and efficiency, but of technical compatibility with radial LV topologies, robust field performance, and seamless integration with concentrator-based architectures. This decision allowed to:

• Efficient capitalization within the RAB framework.
• Reuse of existing conductors, reducing installation complexity
• Hierarchical data management compatible with DSO supervisory systems

In Spain, the selection of PRIME as a national standard facilitated the deployment of smart meters [39,42], while optimized for periodic data collection and centralized control. This infrastructure now serves as a strong platform for future enhancements (including flexibility activation and event-based observability) without requiring full replacement of existing devices.

Wireless Innovation

Wireless metering technologies introduce new architectural possibilities that could complement traditional AMI deployments. These protocols offer flexibility in deployment, enable event-driven communication models, and support dynamic routing, especially in meshed environments like urban networks. Their adoption, however, often involves new operational models that differ from conventional asset-based rollouts:

• OPEX-intensive operation: These technologies often involve licence fees (e.g., LoRa), license management, or higher operational complexity, making them harder to integrate into regulated cost recovery.
• Service-oriented architectures: They support event-driven models, distributed control, and dynamic routing, which require DSO adaptation in IT platforms and operational processes.
• Asset classification ambiguity: Cloud-based platforms or device-management systems are not always clearly eligible for inclusion in the RAB.

Rather than representing a challenge, this situation highlights the importance of regulatory adaptability. Existing AMI framework, built on a strong PLC foundation, can be extended through pilot programs, regulatory sandboxes, or complementary service layers to explore wireless solutions where they provide added value, such as in behind-the-meter assets for flexibility activation.

4.2. Aligning Regulatory Incentives with Technical Capabilities

The evolution of smart metering infrastructure towards real-time observability, flexibility management, and interoperability demands an evolution in how incentives are structured. Current remuneration frameworks have already facilitated a robust and standardized AMI deployment.

The next step is to complement this foundation with mechanisms that reflect how infrastructure is used, not just how it is installed. Future regulatory developments may consider:

• Recognize the full system value of communication capabilities, including those delivered via OPEX or user-centric models.
• Create flexible remuneration frameworks for wireless deployment and cloud-based metering services, whether through TOTEX integration, performance KPIs, or innovation incentives.
• Support interoperable, multi-protocol architectures, enabling DSOs to tailor their technology choices to specific grid segments without financial penalty.

Smart metering and AMI are evolving into a strategic asset for enabling flexibility activation, voltage management, and the decentralized coordination of flexible resources. As their role expands beyond traditional billing, regulatory frameworks can be progressively adapted to recognize not only the cost of infrastructure deployment, but also the functional value they provide.

The analysis presented has highlighted the potential of modern metering systems to enhance observability, responsiveness, and localized control, combined with existing infrastructure. While the existing asset-based remuneration model has been necessary for enabling a widespread and standardized metering rollout, future improvements may focus on incorporating complementary incentives that recognise digital intelligence and system performance.

By aligning investment planning with the evolving operational role of AMI, regulators can further support the goals of electrification, decentralization, and decarbonization established by the European Third Energy Package [1,2]. This evolution does not require abandoning the existing model, but rather a flexible extension of its logic to fully harness the capabilities of AMI within a more adaptive and sustainable regulatory framework towards flexibility management and distributed control.

4.3. Interoperability, Cybersecurity, and Data Privacy Challenges

While smart metering infrastructures are strategically positioned to enable electrification goals, large-scale adoption raises new challenges that must be addressed in parallel with incentive design.Those can be described as follows:

• Interoperability: One of the main barriers in AMI deployment is the coexistence of heterogeneous protocols and vendor-specific implementations. PLC-based systems (such as PRIME and G3-PLC) profit from the reuse of the DSO’s existing electrical infrastructure, avoiding the need for third-party providers and reducing dependency on external actors. However, this advantage comes at the cost of limited interoperability: PRIME, for example, is not natively IP-based and often relies on proprietary firmware layers, which complicates integration with modern digital platforms. In contrast, wireless technologies (e.g., 6LoWPAN) already support IPv6 and standardized application protocols (such as CoAP), enabling seamless integration with IoT ecosystems. This makes wireless solutions naturally more interoperable across vendors and more adaptable to heterogeneous environments, although often at higher operational costs.
• Cybersecurity: Security and avoiding data interception is a key requisite for successful metering infrastructures. While PLC networks are physically constrained to the grid (therefore, less exposed than open wireless channels) they still require strong encryption and authentication. G3-PLC has advanced further by incorporating AES encryption at the MAC and transport layers, together with IPv6 compatibility, making it the most secure PLC protocol currently deployed at scale in PLC deployments. In all cases, regulatory enforcement of security standards (such as mandatory end-to-end encryption, secure firmware updates, and intrusion detection) is essential to safeguard AMI as a critical infrastructure.
• Data privacy: Smart meters generate highly granular load curves. If intercepted, they can reveal sensitive information about end-customers’ behavior. This raises concerns not only of security but also of consumer trust. The European General Data Protection Regulation (GDPR) establishes strict requirements for explicit consent, anonymization, and access control. In Spain, DSOs directly operate AMI platforms, profiting from owning the metering infrastructure and the communication channels. Nevertheless, they still remain bound by GDPR requirements, and some utilities are already exploring edge-based computing to minimize data exposure [64]. Data privacy is a requisite for public acceptance of advanced grid services such as dynamic tariffs, behind-the-meter flexibility, or participation in local energy markets.

These three dimensions, apart from the remuneration schemas and legislation, form the key conditions for unlocking the operational potential of AMI. Interoperability ensures scalability, cybersecurity ensures reliability, and data privacy targets end-customer trust. Addressing these challenges requires a coordinated effort between different actors (regulators, DSOs, third-party providers, and end consumers). By making clear standards and promoting innovation, the full potential of smart metering can be realized in a manner that is both interoperable and secure.

5. Conclusions

Smart metering capabilities have evolved into a key-role player of decentralized distribution networks as part of the digitalization process. They enable DSOs to transition from passive infrastructure managers to active system operators, capable of handling system’s real-time observability, distributed control of flexible assets, and integrating behind-the-meter flexibility services. However, the full potential of smart metering remains highly constrained, not because of technological limitations, but by a regulatory framework that fails to recognise its operational value.

The Spanish distribution system’s experience has shown that a asset-based remuneration framework can successfully deliver a resilient, high-quality distribution grid, while setting the stage for more advanced operational use of metering data. As digital technologies mature and the demand for system flexibility increases, the regulatory framework can evolve to recognize not only the installed infrastructure, but also its contribution to efficient, real-time, and customer-centric operation.

Rather than replacing the CAPEX logic that characterises the current model, targeted objectives such as performance-based metrics, flexibility indicators, or grid utilization incentives can extend the regulatory framework toward system optimization: Considering the grid utilization factor into the remuneration logic would incentivize DSOs to optimize asset utilization rates, invest in targeted digitalization for operational purposes, and unlock hosting capacity at distribution level. It would also encourage the deployment of smart metering infrastructures capable of measuring and managing utilization in real time, encouraging efficiency and deferring costly grid upgrades for when they are truly needed.

In short, recognizing the full potential of smart metering does not require discarding the strengths of the current regulatory model, but rather leveraging them.

As metering evolves from billing devices to an advanced structure for flexibility, observability, and customer engagement, the remuneration framework can be progressively adapted to reflect this added value. By integrating functional incentives into an already solid regulatory foundation, DSOs will be motivated to deliver a more responsive, efficient, and sustainable distribution system, fully aligned with the goals of the European Commission energy transition targets.

Future Directions: Hybrid Architectures, Standardization, and Interoperability

The evolution of smart metering lies not in replacing existing infrastructures, but in leveraging it, improving functionality, adaptability, and interoperability. The existing robust infrastructure can now be complemented with hybrid architectures that integrate additional communication layers (e.g., wireless mesh networks) where needed for flexibility services, real-time diagnostics, or customer-oriented control. Such hybrid architectures could enable:

• PLC-based communication for billing and backward compatibility with existing concentrators, profiting from the robustness and reliability of wired connections.
• Wireless multi-services networks (such as 6LoWPAN), for faster event detection, and behind-the-meter flexibility activation.
• Local aggregation through edge-computing gateways that merge different data streams and optimize bandwidth usage.

Hybrid architectures could also allow DSOs to adjust metering deployments to specific use cases and topologies: optimizing performance in dense urban settings by using mesh-based wireless networks, or proposing long-range, low power point-to-point communication in rural areas.

The key lies in standardization. Protocol stacks must be modular, vendor-agnostic, and enable plug-and-play compatibility across devices and networks. Interoperability profiles based on open standards (such as DLMS/COSEM, CoAP, and IPv6) must be standardized and massively adopted by the industry. In this context, interoperability would enable seamless integration between metering infrastructure and flexibility markets, aggregators, or consumer platforms, following the principles of the European Directives. Regulation should consider metering infrastructures not as a large-upfront investment, but as a dynamic system whose value increases over time as new functionalities are added.

Looking at other models, countries such as Germany Smart Meter Gateway architecture, in combination with regulatory instruments like §14a EnWG [65] highlights the potential of multi-port metering devices to act as active control interfaces for flexibility from behind-the-meter assets. This enables secure, standardized communication with DERs and flexibility activation, positioning smart meters as operational assets within the distribution grid.

This requires a shift in regulatory frameworks towards performance-based remuneration, where the value of metering is assessed based on its operational capabilities, data quality, and contribution to system flexibility. As distribution networks become more electrified, decentralized, and data-driven, smart metering will remain a key-player of grid’s observability and controllability. Ensuring an adaptable and interoperable architecture will be key to supporting the next generation of energy services, from local flexibility markets to data-driven enhanced grid operation.