Flexibility by Design: A Methodological Approach to Assessing Electrical Asset Potential Inspired by Smart Readiness Concepts

Abstract

The growing integration of distributed energy resources and electrification of end users is driving the need for greater system flexibility in modern power grids. Various electrical assets can contribute to this flexibility, either inherently or through external control mechanisms, although their suitability varies even within the same category of assets. This paper presents a novel methodological approach to assess the flexibility potential of electrical assets based on their inherent technical characteristics and their intended installation context. Inspired by the principles of the Smart Readiness Indicator (SRI) for buildings, the proposed method employs a scoring system to evaluate a set of key functionalities that determine an asset’s readiness to contribute to system flexibility, then through a weighted sum a final index is obtained. These scores are combined through a weighted aggregation to produce a single, easy-to-interpret index that synthesizes multiple characteristics, enabling comparisons across different technologies. Unlike the SRI, this approach is not focused on certification but rather on providing a decision-support tool for end-users. The applicability of the method is demonstrated through a case study evaluating a photovoltaic inverter, followed by a sensitivity analysis to assess the robustness of the weighting scheme. Results indicate that the proposed index provides a transparent and replicable means of quantifying flexibility potential, supporting more informed planning and investment decisions.

1. Introduction

The rapid expansion of renewable energy sources in modern power systems has introduced considerable variability and uncertainty in the electricity supply. As generation from wind, solar, and other intermittent resources increases, maintaining the balance between supply and demand becomes more complex. Technically, enhancing system flexibility has emerged as the most effective strategy to manage this variability and ensure reliable grid operation [1]. In Europe alone, it is estimated that system flexibility must nearly double by 2030 to accommodate the growing share of variable energy production [2].

Traditionally, grid flexibility has been provided by conventional peak load power plants, which ramp up or down to match demand peaks [3]. However, maintaining such excess generation capacity is costly, slow to deploy, and often underutilized; running only a few hours per year. This approach also conflicts with the European Commission’s goal of achieving climate neutrality by 2050 [4].

To meet decarbonization targets, demand-side flexibility (DSF) has gained global attention as a critical solution [5]. The Council of European Energy Regulators defines DSF as “the capacity to change electricity usage by end-use customers in response to market signals, such as time-variable electricity prices or incentive payments, or in response to acceptance of the consumer’s bid” [6]. By shifting or shedding load, DSF can reduce operational costs, defer grid investments, and enhance the integration of renewables—yielding benefits for system operators, market participants, and consumers alike [7].

Quantitative studies have demonstrated the system-wide potential value of DSF: The Det Norske Veritas (DNV), one of the world’s leading technical advisors in the energy sector [8], estimates that widespread adoption of flexibility measures across Europe by 2030 could result in over €71 billion in annual electricity cost savings for providers and €300 billion in indirect savings for consumers. Grid operators could defer €11–29 billion in yearly network investments, while renewable energy producers could avoid curtailment of 15.5 TWh, corresponding to 37.5 Mt of yearly carbon emissions savings [9]. This assessment excluded the potential contribution of smart residential appliances.

Other researchers have shown that smart appliances could play a vital role in advancing the energy transition [10]. Notably, in 2023, households accounted for 26.3% of the EU’s final energy consumption [11].

Despite the considerable potential of DSF, its practical implementation remains hindered by a range of technical, regulatory, and socio-economic challenges [12]. Adoption remains limited, with pilot programs often achieving less than 10% participation [13]. Key barriers include insufficient financial incentives [3], regulatory constraints in flexibility markets [14], low consumer awareness [15], and a lack of standardized tools to evaluate which devices can provide flexibility [16].

Existing work falls into two broad strands. On the one hand, qualitative taxonomies classify residential loads by storage capability, response speed, and predictability [17,18], but these taxonomies often aggregate device classes whose actual flexibility potential differs substantially and tend to focus narrowly on electrical attributes while neglecting observability and controllability. On the other hand, quantitative studies estimate the flexibility potential of specific technologies or installations using consumption measurements and usage patterns (e.g., cooling systems [19], EV chargers [20]), but such approaches are inherently context dependent and do not provide a general, design-centric measure of inherent flexibility.

This paper presents a novel decision-support tool aimed at quantifying the inherent flexibility potential of electrical assets, along with the influence of the installation context. Building on the modular logic of the Smart Readiness Indicator (SRI) [21], the proposed approach utilizes four indices:

• Static Flexibility Index (SFI): Evaluates the asset’s inherent technical properties, grouped into three categories: electrical characteristics, observability, and controllability;
• Dynamic Flexibility Index (DFI): Accounts for environmental conditions and usage behaviors that may affect inherent potential;
• Global Flexibility Index (GFI) and Individual Flexibility Index (IFI): Combine the SFI and DFI into a composite score that supports benchmarking and prioritization.

The originality of this work lies in three main aspects. First, it provides a device-level, design-centric measure of inherent flexibility that is distinct from installation-specific consumption studies. Second, it explicitly incorporates observability and controllability alongside electrical characteristics, thereby capturing dimensions that qualitative taxonomies and many prior studies overlook. Third, it yields a single, comparable score (and associated subgroup scores) computed from clearly defined functionality levels, enabling straightforward comparison, ranking, and decision support across heterogeneous assets and contexts.

The principal motivation of this framework is to provide users with a simple and transparent tool to identify which devices can realistically deliver flexibility services, thereby promoting broader participation in flexibility markets.

The main contributions of this paper are as follows:

• A formalized methodology to compute the Static Flexibility Index (SFI) and Dynamic Flexibility Index (DFI) for electrical assets, each based on modular service groups and discrete functionality levels.
• The definition and specification of functionality levels for services across the groups: Electrical Characteristics, Observability, Controllability, Ambient Factors, and Usage Flexibility, together with a reproducible scoring and aggregation procedure.
• Introduction of the Global Flexibility Index (GFI) and Individual Flexibility Index (IFI) as practical composites that combine design and contextual effects for benchmarking and user-specific assessment.
• A user-friendly, implementable framework (suitable for spreadsheet or database implementation) that produces a single synthesizing score and subgroup breakdowns to aid prioritization and decision making.

This framework thus fills a gap between high-level qualitative classifications and narrowly scoped quantitative studies, providing a standardized and implementable approach to assess inherent device flexibility, facilitate comparison and prioritization, and support practical interventions that promote DSF adoption.

The remainder of the paper is structured as follows: Section 2 reviews power system flexibility, the SRI methodology, and relevant device classification schemes. Section 3 presents the proposed indices and scoring methodology. Section 4 demonstrates the application of the proposed methodology through the evaluation of a photovoltaic inverter and further provides a sensitivity analysis of the SFI. Section 5 presents the discussion of the methodology, including its strengths, limitations, and potential research directions. Section 6 concludes the paper, summarizing the main findings, highlighting the robustness and usability of the proposed framework, and outlining avenues for future work.

2. Background

2.1. Flexibility in Power Systems

Flexibility can be defined as the ability of a power system to reliably adjust the energy demand and generation to account for predictable and unpredictable fluctuations covering time horizons from the short to the long term [22].

Flexibility in modern power systems can be enhanced by ensuring that all key system assets are capable of adapting to variable conditions [23]. This involves:

• Generation assets with the ability to adjust output rapidly, operate efficiently at partial loads, and achieve deep turndowns;
• Transmission infrastructure with minimal congestion, sufficient capacity to access diverse balancing resources (including cross-border exchanges), and advanced control technologies to optimize network usage;
• Demand-side resources enabled by smart grid functionalities to support demand response, energy storage, and responsive distributed generation;
• Operational practices that maximize the flexibility of existing assets, such as near-real-time decision-making, integration of improved forecasting for variable renewables, and enhanced coordination among system operators.

Once this flexibility is unlocked, it can be harnessed to deliver a range of flexibility services, effectively functioning as ancillary services provided by flexible assets [24]. The flexibility services an asset can provide are intrinsically linked to its technical capabilities.

Table 1. Technical requirements of flexibility services.

Service	Description	Response Speed	Duration	Dispatch Frequency
Continuous Regulation	Applied to manage and stabilize rapid fluctuations between system load and generator supply.	~1 min	Minutes	Minutes
Energy Imbalance Management	Comparable to Continuous Regulation, but with a slower response	~10 min	10 min to hours	10 min to hours
Congestion Management	Control of supply and demand to prevent overloading within the power network	Minutes to months	Minutes to hours	Minutes to months
Fast Frequency Response	Injection or rejection of active power (MW) in a couple of seconds to maintain grid stability	Seconds	Seconds to 15 min
Primary Frequency Response	Arrest the frequency fall or rise outside of the predefined frequency deadbands	~30 s	10 s to min
Secondary Frequency Response	Activated to restore the system frequency to its nominal value	30 s to few minutes	Seconds to minutes
Tertiary Frequency Response	Slowest response frequency regulation service	Minutes to hours	Minutes to hours
Instantaneous Contingency Reserves	Rapid adjustments to balance the system after significant events	Seconds to less than 10 min	10 to 120 min	Hours to days
Replacement Reserves	Activated to back up or replace the Instantaneous Contingency Reserve for stabilizing the system	Less than 30 min	2 h	Hours to days
Voltage Control	Adjusting reactive power to regulate voltages within acceptable levels	Seconds	Seconds	Continuous
Black Start	Self-starting generation with sufficient capacity and control to support system recovery	Minutes	Hours	Months to Years

summarizes these services together with three key technical requirements for their provision, based on the information in [25,26]. The values are provided as reference only and may vary depending on flexibility market design, system operator specifications, or national regulations.

Beyond the three technical characteristics outlined in Table 1, the provision of flexibility services may also depend on additional asset-specific attributes, such as power flow direction, electrical composition (e.g., energy-to-power ratio), and predictability [27]. Consequently, the ability to deliver particular flexibility services is often restricted to certain asset types, and in some cases, only a subset of assets may be capable of contributing to any service at all.

On that matter, understanding how capable an asset is of providing flexibility and which services it can support constitutes an essential decision-making tool for system operators and stakeholders.

2.2. Existing Assessment Frameworks

In this section, some evaluation and classification frameworks are reviewed. The discussion begins with the Smart Readiness Indicator (SRI), originally developed for smart buildings but incorporating concepts that are highly relevant to flexible assets. Subsequently, three classification approaches proposed in the literature are presented, each offering distinct perspectives on how to assess and categorize flexibility potential.

2.2.1. Smart Readiness Indicator (SRI)

The SRI is a standardized European methodology designed to assess the smart readiness of buildings. It was introduced as part of the 2018 revision of the Energy Performance of Buildings Directive (EPBD), which sought to improve energy efficiency and promote the integration of digital technologies within the building sector across EU member states.

Within this framework, smartness describes a building’s capacity to detect, analyze, communicate, and adapt proactively and efficiently to shifts in its technical systems, the surrounding environment (including the power grid), and the requirements of its occupants.

The outcome of the SRI assessment is expressed as an overall score that reflects how close a building is to maximum smart readiness, complemented by specific scores in key dimensions of building smartness [28].

The methodology evaluates seven impact criteria across nine technical domains:

• Technical domains: heating, cooling, domestic hot water, ventilation, lighting, dynamic building envelope, electricity, electric vehicle charging, and monitoring and control;
• Impact criteria: energy efficiency, maintenance and fault prediction, comfort, convenience, health, well-being and accessibility, information to occupants, and energy flexibility and storage.

The SRI methodology involves three main steps:

• Identification of technical domains present in the building. Each domain is marked as: 0: absent and non-mandatory (not penalized), 1: present, 2: absent but mandatory (penalized in scoring).
• Evaluation of smart-ready services within each domain. Services are scored according to a functionality level (0–4), with a weight reflecting their coverage (full building = 100%, or partial share).
• Aggregation of results into overall and domain-specific scores, with the possibility of adjusting weights according to geographical context (e.g., prioritizing air conditioning efficiency in southern regions).

The methodological approach applied in the SRI has been examined in detail, as specific elements such as functionality levels and weighted scoring are used as reference in the flexibility assessment methodology proposed in this study.

2.2.2. Classification of Flexible Assets

In the literature, various approaches have been proposed to classify and group flexible assets For household appliances, a widely used classification distinguishes three categories [14]:

• Uncontrollable devices, which are unable to provide flexibility;
• Thermostatically Controlled Appliances (TCAs), which operate based on thermal dynamics and can leverage the heat-to-power coupling to deliver significant flexibility;
• Non-Thermostatically Controlled Appliances (NTCAs), which generally provide a more limited degree of flexibility compared to TCAs.

This classification provides some insight into the flexibility potential of different assets but does not directly link them to specific flexibility services. To address this gap, another classification proposed by the authors in [17] gives a better understanding on what type of services an asset can deliver:

• Storable loads: Loads in which energy consumption and end-use service are decoupled through storage, such as batteries (electrochemical) or thermal inertia;
• Shiftable loads: Consumption can be shifted in time without compromising the service, typically involving non-interruptible processes that require scheduling (e.g., laundry cycles);
• Curtailable loads: Consumption cannot be shifted without affecting the service but can be instantly interrupted when necessary;
• Base loads: Services that require continuous, immediate power supply and cannot be shifted or interrupted;
• Self-generation: On-site electricity production by consumers, which reduces net demand. When dispatchable, it can also serve as backup supply.

Although this second classification provides a better mapping of assets to potential services, it still overlooks the technical characteristics required by flexibility services, as discussed in Section 2.1. As a result, assets with different technical profiles may still fall under the same category. To address this limitation, the authors in [18] propose further distinguishing assets within each type according to key attributes such as energy-to-power ratio, response direction, response speed, response duration, availability (frequency and timing of activation), and predictability (accuracy of availability forecasts).

Building on this, Ref. [29] introduces a more granular classification, dividing flexibility resources into seven groups: shiftable advance, shiftable delay, shiftable advance/delay, supply-side (dispatchable power plants), stationary storage (standalone), stationary storage with generation, mobile storage (e.g., electric vehicles), and operational flexibility (e.g., network reconfiguration and dynamic line rating). This approach refines the shiftable load category by considering temporal characteristics and differentiates storage resources by location.

In addition, the authors characterize flexibility resources and services using 18 parameters, including power and energy capacity, ramping capability, service and reaction duration, rebound and recovery effects, ramp frequency, efficiency, energy loss, lifetime, location, credibility, and predictability. This multidimensional framework enables a richer yet still simplified qualitative mapping between services and resources. However, a quantitative assessment combined with cost–benefit analysis remains necessary to determine the most suitable assets for providing specific services.

2.3. Justification for a Novel Approach

As shown in the previous subsection, the literature offers several classifications and characterizations of flexible assets that help to identify whether a device is potentially flexible and, in some cases, to map it to specific flexibility services. However, we did not identify a published methodology that (i) assesses the inherent flexibility readiness of devices in a way that is clear, comparable and reproducible, and (ii) reports that readiness as a concise, user-friendly score that can be used for quick decision making. This constitutes an important gap: existing schemes either remain qualitative, are tailored to particular asset families, or require extensive context-specific data and simulation to produce comparable results.

The methodology proposed in this paper addresses that gap. Rather than replacing prior classifications, it builds on them and on the structural logic of the SRI to produce a practical, device-level evaluation framework. The objective is not certification but to provide a transparent, reproducible decision-support tool and knowledge base for end-users, manufacturers and system planners. The resulting score enables rapid comparisons across technologies, informs procurement and deployment decisions, and highlights which assets merit deeper quantitative assessment or field trials.

Key advantages of the proposed approach are its device-level focus, its combination of qualitative and quantitative criteria, and its explicit modelling of contextual effects (location and usage) on flexibility potential. These characteristics make the methodology both operationally useful (fast, interpretable) and scientifically sound (grounded on established classifications and on SRI principles). In the next section we present the methodology in detail, including the indicator definitions, functionality levels and scoring rules.

3. Methodology

This section presents the methodological framework developed to assess the flexibility potential of electrical assets. Inspired by the structure and logic of the SRI, the proposed method combines qualitative and quantitative criteria to evaluate the ability of an asset to contribute to system flexibility. The methodology is composed of two complementary indices, Static Flexibility Index (SFI) and Dynamic Flexibility Index (DFI), as well as two combined indices, the Global Flexibility Index (GFI) and the Individual Flexibility Index (IFI), which integrate the SFI and DFI. Together form a coherent decision-support tool for benchmarking, classification and deployment planning.

3.1. Overview of the Proposed Method

The proposed method is based on a modular, scalable and replicable framework designed to classify flexible energy assets in a consistent and technology-neutral way. Inspired by the multi-criteria assessment approach, SRI, it evaluates both the technical characteristics of the assets and the framework where it is installed.

The methodology comprises four key indices:

• Static Flexibility Index (SFI): Focuses on the own properties of the asset, such as electrical capacity, observability and controllability. These features are normally independent of the installation site and are derived from manufacturer specifications or performance data.
• Dynamic Flexibility Index (DFI): Captures external factors that influence the flexibility potential of the asset. These include climatic conditions, usage patterns, grid interaction capability and environmental framework. The DFI is calculated based on a set of scenarios and user-defined parameters that reflect generic local conditions.
• Global Flexibility Index (GFI) and Individual Flexibility Index (IFI): Aggregate the SFI and DFI into a single score, providing a comprehensive representation of the flexibility potential of an asset. This score would support asset prioritization, potential user investment planning and integration into digital platforms and marketplaces. The GFI aggregates the DFI across multiple users, while the IFI reflects the flexibility potential for a specific user.

Each index is computed independently, based on normalized functionality levels and weighted criteria adapted to the characteristics of different asset types. The method is designed to support a wide range of stakeholders or users —including asset manufacturers, devices purchasers, aggregators and distribution system operators among others— while ensuring compatibility with existing asset catalogues. Furthermore, it is structured to enable future integration into interoperable platforms such as flexibility marketplaces and energy management systems.

3.2. Static Flexibility Index (SFI)

The methodology for computing the SFI and the DFI follows the structure of the SRI framework for buildings, as described in the previous section. It covers nine asset domains:

• Heating;
• Domestic Hot Water (DHW);
• Cooling;
• Ventilation;
• Electric Vehicle Charging;
• Energy Storage System (BESS);
• Photovoltaic System (PV);
• White appliances;
• Lighting.

For the SFI, each domain is evaluated across three service groups: Electrical Characteristics, Observability, and Controllability. The criteria in each group are designed to capture different aspects of static flexibility. The criteria draw upon a qualitative evaluation framework proposed in [29], emphasizing parameters that are intrinsic to the assets, and build upon the concepts of the SRI. Furthermore, they were refined and validated through workshops with experts from ten different institutions as part of the REEFLEX project [30], ensuring that the selected criteria reflect both theoretical considerations and practical knowledge:

• Electrical Characteristics considers attributes such as bidirectional power flow, energy storage capability, and reactive power support.
• Observability evaluates the degree of usage detection, real-time monitoring, and dependence on external IoT modules.
• Controllability focuses on the type and location of control, operation mode, and response time.

These three service groups were identified as the most relevant factors influencing the flexibility potential of an asset. Each group is further broken down into specific services, which are assigned functionality levels. These levels, consistent with the SRI approach, reflect the extent to which a device provides a given service. A functionality level of zero represents no contribution to flexibility, while higher values indicate greater potential.

For a given device, the flexibility subindex of a service group is calculated as

$$\mathrm{SI}={\displaystyle \frac{{\sum }_{i=1}^{\mathrm{N}}\mathrm{F}{\mathrm{L}}_i}{{\sum }_{i=1}^{\mathrm{N}}\mathrm{F}{\mathrm{L}}_i^{\mathrm{m}\mathrm{a}\mathrm{x}}}},$$

(1)

where

• SI is the service group flexibility subindex;

• $\mathrm{F}{\mathrm{L}}_i$ is the functionality level assigned to service i;

• ${\mathrm{F}\mathrm{L}}_i^{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum possible value for service i;

• $\mathrm{N}$ is the number of services in the service group.

Once the service group subindices are calculated, weights are assigned to reflect their relative influence on overall flexibility. In this study, weights of 0.3, 0.35, and 0.35 were assigned to Electrical Characteristics, Observability, and Controllability, respectively. This weighting prioritizes monitoring and control capabilities, as these aspects more directly determine the extent to which flexibility can be exploited. The implications of this weighting scheme are further analyzed in Section 4.

The total SFI of the device is then computed as the weighted sum of the three service group subindices:

$$\mathrm{SFI} = {\sum }_{i=1}^3{\mathrm{w}}_i·\mathrm{S}{\mathrm{I}}_i,$$

(2)

where

• $\mathrm{S}{\mathrm{I}}_i$ represents the subindex of service group i (electrical characteristics, observability, and controllability);

• ${\mathrm{w}}_i$ is the assigned weight of service group i.

The functionality levels were defined in close collaboration with asset experts from the REEFLEX project, ensuring that all relevant aspects of flexibility within each service group were considered. To allow comparability across domains, the same set of functionality levels was applied consistently to all asset types.

The following subsections provide definitions of the functionality levels for each service within their respective groups.

3.2.1. Electrical Characteristics

Electrical Characteristics covers all aspects related to the electric properties of the asset, such as power flow direction and phase balancing. All services included in this group, along with their corresponding functionality levels, are presented in

Table 2. Services and Functionality Levels for Electrical Characteristics of SFI.

Service	Functionality Levels
Two-Way Power Flow	Level 0: Indicates one-way power flow capability, implying limited flexibility in handling bidirectional power flow.
	Level 1: Represents two-way power flow capability, allowing for bidirectional power exchange, enhancing flexibility.
Energy Storage Capability	Level 0: Denotes the inability to store energy, indicating limited flexibility in managing energy flow
	Level 1: Signifies the ability to store energy, enhancing flexibility by enabling energy storage and retrieval
Reactive Power Management	Level 0: No reactive power control capability, suggesting limited flexibility in managing reactive power flow
	Level 1: Represents reactive power control capability, enhancing flexibility by enabling management of reactive power flow
Balance Phases	Level 0: Implies inability to balance phases, suggesting limited flexibility in maintaining phase equilibrium
	Level 1: Indicates the ability to balance phases, enhancing flexibility by ensuring phase equilibrium
Maximum Ramp-Up Rate	Level 0: 0–20% of load/minute. Specifies a limited ramp-up rate range, suggesting constrained flexibility in adjusting power output
	Level 1: 20–50% of load/minute
	Level 2: 50–80% of load/minute
	Level 3: 80–100% of load/minute. Signifies an expanded ramp-up rate range, enhancing flexibility by allowing for more rapid adjustments in power output
Maximum Ramp-Down Rate	Level 0: 0–10% of load/minute. Denotes a limited ramp-down rate range, indicating restricted flexibility in reducing power output
	Level 1: 10–30% of load/minute
	Level 2: 30–60% of load/minute
	Level 3: 60–100% of load/minute. Represents an extended ramp-down rate range, enhancing flexibility by facilitating quicker reductions in power output
Active Power Management	Level 0: Indicates no active power control capability, suggesting limited flexibility in managing active power flow
	Level 1: Represents active power control capability, enhancing flexibility by enabling management of active power flow
Divisibility	Level 0: Implies inability to modulate load consumption, suggesting limited flexibility in adjusting energy consumption levels
	Level 1: Signifies the ability to modulate load consumption, enhancing flexibility by allowing for adjustments in energy consumption levels

.

3.2.2. Observability

Observability pertains to the device’s monitoring capabilities and communication functions.

Table 3. Services and Functionality Levels for Observability.

Service	Functionality Levels
Usage Detection	Level 0: Denotes no usage detection capability, indicating limited flexibility in monitoring asset usage
	Level 1: Represents isolated usage detection capability, enhancing flexibility by enabling monitoring of asset usage
	Level 2: Indicates communicated usage detection capability, further enhancing flexibility by facilitating remote monitoring of asset usage
Reporting Information	Level 0: Specifies no actual or past operation data availability, suggesting limited flexibility in accessing operational data
	Level 1: Represents actual operation data availability, enhancing flexibility by providing access to historical operational data
	Level 2: Level 1 + forecasted operation data available. Indicates inclusion of forecasted operation data availability, further enhancing flexibility by offering insights into future operational trends
Real-Time Monitoring	Level 0: Denotes no real-time monitoring capability, suggesting limited flexibility in monitoring asset status
	Level 1: Represents basic real-time monitoring capability, providing periodic status updates at intervals
	Level 2: Indicates continuous real-time monitoring capability, facilitating constant data streaming for enhanced flexibility
Dependence on External IoT Module	Level 0: Implies no possible control through an external IoT module, suggesting limited flexibility in external control
	Level 1: Represents control solely through an external IoT module, enhancing flexibility by enabling external control
	Level 2: Indicates independent control possible, further enhancing flexibility by allowing for autonomous control

presents all services in this group and their associated functionality levels.

3.2.3. Controllability

Controllability describes how the device can be operated and how it responds to control actions. All services in this group, together with their functionality levels, are summarized in

Table 4. Services and Functionality Levels for Controllability.

Service	Functionality Levels
Control Location	Level 0: Specifies manual control in place, suggesting limited flexibility in remote control
	Level 1: Represents telematic control, enhancing flexibility by enabling remote control
Operation Type	Level 0: Denotes ON–OFF operation, suggesting limited flexibility in operation mode
	Level 1: Included in level 0 and set point modification. Represents set point modification capability, enhancing flexibility by allowing adjustments in operation settings
	Level 2: Included in level 1 + scheduling. Indicates scheduling capability, further enhancing flexibility by enabling predefined operation schedules
	Level 3: Included in level 2 + self-learning optimal use. Signifies self-learning optimal use capability, maximizing flexibility by adapting operation based on learned patterns
Response Time	Level 0: Response time equal or greater than 30 s. Specifies a limited response time range, suggesting delayed responsiveness and reduced flexibility
	Level 1: Response time from 10 to 30 s
	Level 2: Response time less than 10 s. Represents a faster response time, enhancing flexibility by allowing for quicker responses to commands
Control Type	Level 0: Denotes no control capability, suggesting limited flexibility in asset control
	Level 1: Represents manual control capability, enhancing flexibility by enabling user-driven adjustments
	Level 2: Indicates automatic control capability, facilitating autonomous operation and maximizing flexibility

.

3.3. Dynamic Flexibility Index (DFI)

In contrast to SFI, the DFI considers the operational context of assets, reflecting their ability to adapt to changing environmental conditions. This may include factors such as climate, surrounding infrastructure, available services, and the demand patterns of end users.

Similarly to SFI, the DFI builds on three fundamental elements: service groups, services, and functionality levels. Service groups categorize scenarios along three dimensions considered critical for flexibility: ambient factors, electrical characteristics, and usage flexibility. Within each group, services represent specific operational scenarios, understood as sets of conditions that remain consistent over time. Finally, functionality levels quantify the degree to which a service is achieved, using a scale from zero to two.

The service groups, services and functionality levels of the DFI were designed with the involvement of the purchaser in mind. As the tool requires users to select a single level for each service, some of the level definitions are based on official statistical data published by European sources. In all cases, the functionality levels serve as user-friendly guidelines, reflecting typical decisions made on a daily or seasonal basis.

To evaluate the DFI, the first step is to calculate the flexibility subindex for each service group using (1). Subsequently, the DFI is obtained as a weighted sum of the three service group subindices, as expressed in (3). The weighting scheme—0.4 for ambient factors, 0.1 for electrical characteristics, and 0.5 for usage flexibility—was defined in consultation held during REEFLEX project meetings. Feedback was collected from representatives of ten partner institutions, including research centers, technology manufacturers, and energy-sector organizations. During these meetings, each partner provided their perspective on the relative importance of the three flexibility dimensions based on their expertise and field of activity. The final weights were obtained by averaging the values proposed by the participants, ensuring a balanced representation of the different viewpoints within the consortium.

$$\mathrm{DFI}={\sum }_{i=1}^3{\mathrm{w}}_i·\mathrm{S}{\mathrm{I}}_i,$$

(3)

where $\mathrm{S}{\mathrm{I}}_i$ represents the subindex of service group i (ambient factors, electric characteristics, and usage flexibility), and ${\mathrm{w}}_i$ is the assigned weight to that group.

In line with the procedure applied for the SFI, the following subsections present each service’s functionality levels within their respective groups, along with the relevant application domains. For all services, a functionality level of zero denotes the absence of flexibility, whereas a value of two represents the maximum flexibility achievable by the asset under the given scenario. It is important to highlight that, within the DFI framework, assets operating continuously or not being used at all are assigned a flexibility value of zero, since their load cannot be curtailed or shifted. Conversely, scenarios that permit modulation of asset operation—while still meeting user requirements—are regarded as providing the highest level of flexibility.

3.3.1. Ambient Factors

Ambient Factors evaluate external and internal conditions—such as outdoor temperature, indoor humidity, building characteristics, and user preferences—that influence how frequently and for how long an asset is used. The services included in this group, together with their corresponding functionality levels, are presented in

Table 5. Services and Functionality Levels for Ambient Factors.

Service	Functionality Levels
Average outdoor temperature during the year: The outdoor temperature at the location where assets such as DHW, heating and cooling systems, and white appliances operate significantly affects their flexibility. To represent average annual temperatures conditions, functionality levels are determined based more accurately on the Heating Degree Days (HDD) and Cooling Degree Days (CDD) [31,32].	Level 0 (DHW, heating, cooling): CDD are below 10, or HDD are below 2300
	Level 0 (White appliances): CDD exceed 25, or HDD exceed 3300
	Level 1: CDD range between 10 and 25, or HDD range between 2300 and 3300
	Level 2 (DWH, heating, cooling): CDD exceed 25, or HDD exceed 3300
	Level 2 (White appliances): CDD are below 10, or HDD are below 2300
Extreme temperatures during a season: The domains related to DHW, heating, cooling, white appliances and BESS become particularly prominent during the winter and summer seasons due to the extreme temperatures experienced in certain regions. Consequently, the functionality levels for these services are defined based on the number of Hot Days (HD) and Frost Days (FD) [33,34].	Level 0: HD during summer exceed 30 or FD during winter exceed 50, indicating extreme seasonal conditions
	Level 1: HD during summer range between 3 and 30, or FD during winter range between 10 and 50, indicating mild seasonal conditions
	Level 2: HD during summer are below 30, or FD during winter are below 10, indicating soft seasonal conditions
Comfort preferences for DWH: This service addresses the temperature considered comfortable by the end users.	Level 0: the water temperature setpoint is maintained above 45 °C
	Level 1: the water temperature is regulated to provide hot water within the range of 30 °C to 40 °C
	Level 2: water temperature is set below 30 °C.
Hot water demand on a daily basis: Users can make a rough estimate of daily hot water demand by considering the frequency of how water is used and the rate at which the water cools to an uncomfortable temperature after the tap is opened	Level 0: There is a rapid depletion of the asset storage tank, or the state of charge consistently remains below 50% of its capacity
	Level 1: The average state of charge of the tank ranges between 50% and 80% of its capacity
	Level 2: Hot water demand is low, and the state of charge remains consistently above 80%
State of the building envelope: The building envelope, particularly insulation and windows, is considered one of the key factors in assessing a building energy performance, due to its direct impact on the operation of heating and cooling systems [35]. A well-insulated dwelling contributes positively to the flexibility of these domains and is one of the focal areas promoted by the European Energy Performance of Buildings Directive [36].	Level 0: Visible signs of poor insulation are present, and the overall construction does not support effective thermal insulation
	Level 1: Insulated windows and doors are present, but the construction exhibits features that may reduce thermal inertia
	Level 2: The construction supports thermal inertia through the use of high-quality materials that enhance energy efficiency
Indoor activities: The flexibility of the ventilation domain can be assessed by considering the frequency with which users engage in indoor activities that affect air quality and circulation.	Level 0: Activities with a substantial impact on air quality are performed regularly, contributing to significant pollutant accumulation
	Level 1: Routine activities such as cooking or cleaning are carried out using methods that demonstrate user awareness of indoor air quality
	Level 2: Indoor activities have no meaningful impact on air quality
Humidity: Humidity affects the flexibility of white appliances, particularly dryers and dehumidifiers. In the absence of humidity sensors, typical regional values can be obtained from official sources [37,38,39].	Level 0: Humidity levels exceed 60% or fall below 30%
	Level 1: Humidity levels range between 50% and 60%
	Level 2: Humidity levels range between 30% and 50%
Appliances load: The flexibility of certain white appliances, such as those used for laundry and dishwashing, may be influenced by usage load	Level 0: Wet appliances are used infrequently
	Level 1: Wet appliances are used on a daily basis
	Level 2: High demand for specific wet appliances results in multiple uses per day
Exposure of house windows to sunlight: This scenario considers the availability of natural light based on window placement, which directly affects the flexibility of the lighting domain.	Level 0: Windows in the dwelling are rarely exposed to direct sunlight
	Level 1: Only a limited number of windows receive direct sunlight
	Level 2: The majority of rooms have windows with good exposure to natural sunlight
Level of activity during non-daylight hours: Flexibility of the lighting domain is also influenced by user preferences regarding lighting levels during activities performed outside daylight hours.	Level 0: Intermittent activities are performed with a preference for soft, ambient lighting
	Level 1: Intermittent activities are performed with a preference for well-lit conditions
	Level 2: Intensive activities are carried out with a consistent requirement for well-lit conditions
Home-installed charging infrastructure: On of the factors influencing the flexibility of the Electric Vehicle (EV) charging domain is the availability of a dedicated charging point, along with its associated maintenance requirements.	Level 0: No home-installed charging points are available, or maintenance tasks are scheduled irregularly, resulting in charging point unavailability for periods exceeding one day
	Level 1: Maintenance tasks are scheduled irregularly, leading to charging ports unavailability for durations extending to several hours
	Level 2: Maintenance tasks are regularly and properly scheduled and executed, ensuring the continuous functionality and availability of charging points
Charging points available outside home: The distribution of the public charging network across the European Union, including the availability of a sufficient number of charging points (CPs) for EV drivers, is also a key factor impacting the EV charging flexibility [40,41].	Level 0: The availability of CPs is below 1 CP per 1000 inhabitants or per 10 km of road
	Level 1: The availability of CPs exceeds 1 CP per 1000 inhabitants, or per 10 km of road, approaching the European average of 1.3 CPs per 1000 inhabitants
	Level 2: The availability of CPs surpasses the European average, exceeding 1.3 CPs per 1000 inhabitants or 1.4 CPs per 10 km of road
Objective functionality: In the case of the BESS and PV, the objective functionality refers to their automated operational configuration, which can be verified through consultation with the supplier or directly reviewed within the asset management tool.	Level 0 (BESS): Charging mode is activated based on predefined conditions, such as energy-price fluctuations or user-defined preferences.
	Level 0 (PV): Output power modulation is not permitted
	Level 1 (BESS): Primary operational objective is self-consumption
	Level 1 (PV): Output power modulation is enabled but remains the only controllable feature
	Level 2 (BESS): BESS operates to fulfill aggregated objectives, which may include bill reduction, self-consumption, and system health
	Level 2 (PV): Output power modulation is governed by aggregated objectives, potentially including efficiency optimization, system health, and extension of lifespan
Integration with other systems: The flexibility of BESS and PV is typically enhanced when these assets are integrated, allowing surplus renewable energy to be stored.	Level 0: There is no integration between BESS and PV, resulting in potential curtailment or loss of excess generated power
	Level 1: BESS and PV are integrated. However, the storage capacity of the BESS is insufficient to accommodate the full amount of renewable energy produced
	Level 2: BESS and PV are integrated, and the BESS has sufficient capacity to store the total of energy generated
Cloud cover and snow days throughout the year: In the context of PV systems, weather conditions play a critical role in determining flexibility.	Level 0: High cloud cover persists throughout the year, with frequent snow days in winter, which significantly limit solar production
	Level 1: Cloud cover is intermittent and mitigated by favorable wind patterns. Snow during winter melts at a favorable rate
	Level 2: Cloud cover is not significant year-round, and snow days are sporadic during winter

.

3.3.2. Electrical Characteristics

Electrical Characteristics consider, in a simplified manner, the amount of power available to provide flexibility. All services classified under this group and their associated functionality levels are summarized in

Table 6. Services and Functionality Levels for Electrical Characteristics of DFI.

Service	Functionality Levels
Consumed power related to the total energy consumption: For the domains DHW, heating, cooling, ventilation, white appliances, and lighting, this scenario addresses disaggregated consumption, which is an effective approach for identifying consumption patterns that influence domain-specific flexibility. Regardless of asset type, the functionality levels are defined based on the proportion of total dwelling energy consumption attributed to each asset, as informed by reported data [42].	Level 0: the asset demand exceeds L0 * % of total consumption
	Level 1: the asset demand accounts for L2 * % to L0 * % of total consumption
	Level 2: the asset demand is below the L2 * % of total consumption
Access to fast charging options: With the rapid expansion of EV charging infrastructure, access to fast charging stations significantly enhances the flexibility of the EV charging domain [40].	Level 0: The charging power of the accessible charging points is below 7.4 kW in AC mode
	Level 1: The charging power of the accessible charging points ranges from 7.4 kW to 22 kW in AC mode
	Level 2: Accessible charging points offers fast or ultra-fast charging capabilities, with both AC and DC modes available
BESS energy limits: Addresses the energy storage behavior of the battery in terms of its State of Charge (SOC).	Level 0: The SOC is maintained above 50% to prevent significant degradation in cycle life
	Level 1: SOC limits are conservatively defined to prioritize long-term system health
	Level 2: SOC limits are configured in alignment with manufacturer recommendations, balancing battery health, with the goal of maximizing energy storage capacity and output
Sizing of the PV installation: Considers the impact of the PV nominal power on its flexibility.	Level 0: The PV system nominal power is lower than the average daily demand, resulting in no noticeable reduction in grid power consumption
	Level 1: The PV system nominal power is sufficient to reduce daily grid power demand
	Level 2: The PV system nominal power exceeds the average daily power demand

* The corresponding thresholds per asset are reported in Table 7.

.

Table 7. Consumption thresholds to assess the asset consumed power.

Domain	L0	L2
DHW	20	10
Heating, Cooling	60	40
Ventilation, White appliances	30	20
Lighting	10	5

Table 7. Consumption thresholds to assess the asset consumed power.

Domain	L0	L2
DHW	20	10
Heating, Cooling	60	40
Ventilation, White appliances	30	20
Lighting	10	5

3.3.3. Usage Flexibility

Usage Flexibility assesses how adaptable the operation of a device is, for instance, whether its load can be shed or shifted, and within which time frames. The services within this group and their respective functionality levels are detailed in

Table 8. Services and Functionality Levels for Usage Flexibility.

Service	Functionality Levels
Load shedding or shifting: Loads associated with DHW, heating, cooling, and white appliances can be intentionally shed or shifted by the users, contributing positively to the overall flexibility. In contrast, domains such as ventilation and lighting typically allow only for load shedding, as their operation is essential for comfort.	Level 0: Load shedding or shifting is not permitted
	Level 0 (Ventilation): Must remain operational for periods exceeding eight hours to maintain air quality
	Level 0 (Lighting): Required during daylight hours for visual comfort
	Level 1: Load can be shed or shift for up to two hours per day
	Level 1 (Ventilation): Can be turned off for periods ranging from 8 to 12 h
	Level 1 (Lighting): Required in some areas during daylight hours, but only for short durations
	Level 2: Load can be shed or shifted for more than two hours per day
	Level 2 (Ventilation): Short periods of operation are sufficient to maintain acceptable air quality
	Level 2 (Lighting): Can be shed during daylight hours and it is primarily needed for evening activities
Time window for EV charging: Addresses the time window during which the EV remains plugged in and available for charging	Level 0: Immediate charge is always required as soon as the EV is plugged in
	Level 1: The EV remains plugged in and available for charging for at least 5 h per day
	Level 2: The EV remains plugged in and available for charging for more than 5 h per day
Usage modes of the renewable assets: For a more flexible operation, it is expected that renewable assets such as the BESS and PV systems are configured as active devices within the grid, capable of supplying energy to a variety of nodes	Level 0: BESS operates solely as backup system, discharging only during main power outage. The PV system is dedicated to powering specific assets with no grid interaction
	Level 1: The BESS responds only to predefined demand thresholds. The PV system is primarily used for self-consumption
	Level 2: The BESS operates continuously and automatically responds to demand events. The PV system is fully integrated into the grid, with the generated energy available for use, storage or sale

.

3.4. Scoring Framework: Individual and Global Flexibility Indices

To incorporate the influence of the operational context on the theoretical flexibility potential (derived from the technical specifications of assets), two indices are defined: the Individual Flexibility Index (IFI) and the Global Flexibility Index (GFI). These indices provide a final score that integrates both the static and dynamic aspects of asset flexibility.

The IFI quantifies the flexibility potential of a specific asset in a given operational context. It is computed as the product of the SFI, which reflects the intrinsic technical capabilities of the asset, and the DFI, which captures the impact of the operational environment and usage conditions as provided by the user:

IFI = SFI × DFI.

(4)

In contrast, the GFI evaluates the flexibility potential of a given asset type at a specific location by averaging the dynamic indices reported by multiple users. This value reflects a more general and location-dependent perspective:

$$\mathrm{GFI} = \mathrm{SFI} \times \stackrel{\mathsf{-}}{\mathrm{DFI}},$$

(5)

where $\stackrel{\mathsf{-}}{\mathrm{DFI}}$ represents the mean DFI calculated across all users for the same asset type and geographical context.

The objective of these indices is to enable comparisons between different technologies, supporting quick decision-making in planning. The following is a guideline for interpretation:

• A score of 0% indicates that the device is not flexible;
• A score around 50% suggests that the device could provide flexibility with external support, such as an Energy Management System (EMS);
• A score of 100% indicates that the device is fully capable of delivering flexibility without requiring external control systems.

It is important to emphasize that these indices are designed as a simplified quantitative decision-support tool. They indicate the degree to which an asset is flexible, but not the actual amount of flexibility it can deliver. The magnitude of flexibility must still be quantified through more detailed assessment methods.

3.5. Implementation of the Methodological Framework

The methodological framework was implemented within the REEFLEX project through a Python-based tool—built in Python 3.12—that enables users to evaluate devices and automatically compute their Static, Dynamic, and Global Flexibility Indices. The tool gathers technical and contextual data through validated input forms and stores all results in a collaborative catalogue.

To ensure accessibility and multi-user participation, the catalogue is hosted on a Microsoft Azure relational database located in Dublin, Ireland. A Python-based REST API manages all interactions, performing validation, securing communication, and ensuring data integrity. The data model follows a normalized schema with migration support. API endpoints provide pagination, server-side filtering, and batched writes to maintain low latency at moderate loads. This architecture provides a reliable and scalable solution, allowing the catalogue to grow while maintaining performance and security.

3.6. Summary of the Methodology

In summary, Section 3 presents the methodology developed to evaluate the flexibility potential of assets across nine domains. Two complementary indices are defined: the Static Flexibility Index (SFI), which assesses design-related attributes grouped as Electrical Characteristics, Observability, and Controllability; and the Dynamic Flexibility Index (DFI), which captures operational and contextual influences grouped as Ambient Factors, Electrical Characteristics (with emphasis on available installation power), and Usage Flexibility. Both indices follow a structure inspired by the Smart Readiness Indicator (SRI): each service within a group is assigned a functionality level (0–3) according to the definitions given in this section, service scores are aggregated into weighted group scores, and these group scores are combined to yield the SFI and DFI. Building on these, the Global Flexibility Index (GFI) and the Individual Flexibility Index (IFI) integrate the DFI’s operational effects with the SFI’s design assessment: the GFI represents an average DFI across installations/users, while the IFI pertains to a specific installation or user. This framework provides the basis for the analysis in Section 4.

4. Results

In this section, the proposed methodology is applied to evaluate the flexibility potential of a commercial photovoltaic (PV) inverter, providing a concrete example of its use. Following this case study, a sensitivity analysis is carried out using eight different commercial solutions. This analysis examines how changes in the weighting assigned to each service group affect the resulting static flexibility index, thereby highlighting the robustness and adaptability of the methodology.

4.1. Case Study: Flexibility Evaluation of a PV Inverter

This subsection presents the application of the proposed methodology to a commercial PV system inverter, the INGECON SUN 3, which corresponds to one of the flexible assets installed at the CIRCE Technology Center, part of the REEFLEX project pilot site. The inverter was selected for the case study based on the availability of detailed technical information, allowing a clear illustration of the methodology. The static and dynamic flexibility indices are calculated to illustrate how the methodology can be used to quantify the flexibility potential of an individual asset.

As the first step of the methodology, the flexibility subindex of each service group contributing to the SFI is calculated with Equation (1). Once the subindices are obtained, they are combined through a weighted sum to derive the overall SFI using Equation (2).

As detailed in the previous section, each service group is composed of several individual services, which are assessed according to predefined functionality levels. The results of these evaluations are summarized in the following tables:

Table 9. Evaluation of electrical characteristics of PV inverter.

Service	Maximum Achievable Value	Device Functionality Level	Flexibility Subindex
2-way power flow	1	0 = One way power flow	75%
Energy storage capability	1	0 = It can’t store energy
Reactive power management	1	1 = Reactive power control possible
Balance phases	1	0 = Unable to balance phases
Maximum ramp up rate	3	3 = 80–100% of load/minute
Maximum ramp down rate	3	3 = 60–100% of load/minute
Active power management	1	1 = Active power control possible
Divisibility	1	1 = Able to modulate load consumption

presents the evaluation of electrical characteristics,

Table 10. Evaluation of PV inverter observability.

Service	Maximum Achievable Value	Device Functionality Level	Flexibility Subindex
Usage detection	2	2 = Communicated usage detection	75%
Reporting Information	2	1 = Actual operation data available
Real-time monitoring	2	2 = Continuous real-time monitoring capability
Dependence on external IoT module	2	1 = Controlled only through external IoT module

corresponds to observability, and

Table 11. Controllability evaluation of the PV inverter.

Service	Maximum Achievable Value	Device Functionality Level	Flexibility Subindex
Control location	1	1 = Telematically controlled	63%
Operation type	3	1 = ON–OFF and set point modification
Response time	2	2 ≤ 0 s (fast)
Control type	2	1 = Manually controlled

covers controllability. To simplify presentation, the table shows only the functionality level applicable to the PV inverter. A complete breakdown of functionality levels for all services is available in Section 3.

Based on these results, the SFI is calculated with Equation (2), yielding a value of 71%:

$$\mathrm{SFI} = {\sum }_{i=1}^3{\mathrm{w}}_i·\mathrm{S}{\mathrm{I}}_i=0.3·75\mathrm{\%}+0.35·75\mathrm{\%}+0.35·63\mathrm{\%}=71\mathrm{\%}.$$

(6)

This result shows that, when only the technical specifications are considered, the device can be classified as flexible. However, its full flexibility potential may not be accessible without external support. For instance, an additional IoT module is required to enable advanced communication and control functions. Moreover, the flexibility it can provide remains limited, as it only supports one-way power flow. To overcome this limitation and significantly enhance its flexibility potential, the device would need to be integrated with a battery storage system.

To evaluate how operational context influences flexibility, the next step is the calculation of the DFI.

Table 12. Dynamic Flexibility Evaluation by Service Group for a PV Inverter in a Mediterranean Context.

Service Group	Service	Maximum Achievable Value	Device Functionality Level	Flexibility Subindex
Ambient factors	Objective functionality.	2	1 = The PV system is controllable, with power output modulation being its only adjustable feature.	50%
	Pairing with storage systems.	2	0 = No storage system is paired with the PV system
	Cloud cover and snow days throughout the year.	2	2 = Cloud cover is not significant year-round, and snow days are sporadic during winter
Electric characteristics	Sizing of the installation.	2	2 = The nominal power of the PV system exceeds the average daily power demand	100%
Usage flexibility	Usage modes defined for the PV.	2	2 = The power produced by the PV system is used to supply immediate load demand, stored for future use, or sold to the grid.	100%

presents the results for each service group contributing to this index, considering a Mediterranean location as the reference context. For clarity, the table displays only the functionality level relevant to the device. Detailed functionality levels for each service can be found in Section 3.

The DFI is then calculated similarly to the SFI, by taking the weighted sum of the evaluated subindices for each service group:

$$\mathrm{DFI} = {\sum }_{i=1}^3{\mathrm{w}}_i·\mathrm{S}{\mathrm{I}}_i=0.4·50\mathrm{\%}+0.1·100\mathrm{\%}+0.5·100\mathrm{\%}=80\mathrm{\%}.$$

(7)

Finally, the IFI for this inverter, considering a Mediterranean location, is calculated using Equation (4):

IFI = SFI × DFI = 71% × 80% = 56.8%,

(8)

This result indicates that the inverter can provide flexibility; however, its potential is limited because it is not paired with a battery energy storage system (BESS), meaning that some of the generated power may remain unused.

The example presented highlights the ease of use of the methodology, providing a clear framework to assess the flexibility potential of different devices across various contexts without requiring advanced technical expertise.

4.2. Sensitivity Analysis of the Static Flexibility Index

To assess the robustness of the methodology, a sensitivity analysis is performed using eight different commercial assets. These assets correspond to flexible equipment located at one of the REEFLEX project pilot sites, implemented at the CIRCE Technology Center. These assets were included in the analysis due to the availability of detailed technical information.

The analysis investigates how variations in the weights assigned to each service group influence the resulting SFI. This provides insight into the extent to which weighing choices affect the evaluation outcome.

Table 13. Static flexibility results for eight commercial assets.

Asset Name	Asset Type	Electric Characteristic	Observability	Controllability	SFI
ACDC DCDC 25 kW	Energy storage system (BESS)	100%	88%	88%	91%
Wallbox eNext	Electric vehicle charging	67%	100%	88%	86%
INGEREV FUSION Wall FW1	Electric vehicle charging	67%	100%	88%	86%
GEISER INOX	DHW	75%	88%	88%	84%
MultiPlus-II GX	Energy storage system (BESS)	92%	75%	62%	76%
INGECON SUN 3	Photovoltaic systems	75%	75%	63%	71%
MASTER INOX	DHW	67%	38%	62%	55%
CORAL VITRO CV-R	DHW	50%	12%	50%	37%

presents the evaluation results for each service comprising the SFI, as well as the overall SFI calculated using the defined weights: 0.3 for electrical characteristics, 0.35 for observability, and 0.35 for controllability. The assets are sorted in descending order based on their SFI values.

The sensitivity analysis was performed by varying the service group weights by ±0.1 and ±0.2 in all possible combinations. As summarized in

Table 14. Summary of sensibility analysis for the Static Flexibility Index.

Asset Name	Asset Type	Static Flexibility Index
		With Original Weights	Weights +/−0.1		Weights +/−0.2
			Min	Max	Min	Max
ACDC DCDC 25 kW	Energy storage system (BESS)	91%	90%	93%	89%	94%
Wallbox eNext	Electric vehicle charging	86%	83%	89%	79%	93%
INGEREV FUSION Wall FW1	Electric vehicle charging	86%	83%	89%	79%	93%
GEISER INOX	DHW	84%	83%	85%	82%	87%
MultiPlus-II GX	Energy storage system (BESS)	76%	73%	79%	70%	82%
INGECON SUN 3	Photovoltaic systems	71%	69%	72%	68%	73%
MASTER INOX	DHW	55%	52%	58%	49%	61%
CORAL VITRO CV-R	DHW	37%	33%	41%	29%	44%

, a ±0.1 variation produced only minor changes in the SFI, and the ranking of devices remained stable. In contrast, a ±0.2 variation occasionally altered the ranking, particularly when weights were highly unbalanced (e.g., 0.1 for electrical characteristics and 0.55 for observability), favoring devices strong in a single attribute. Such skewed weighting is not advisable, as it underrepresents essential characteristics that are critical to assessing flexibility potential.

5. Discussion

The proposed methodology offers a structured and practical approach to evaluate the flexibility potential of electrical assets. One of its major strengths lies in its accessibility and simplicity, particularly for manufacturers, resellers, and technology integrators who already possess the required technical specifications to populate the asset forms. By relying on predefined criteria and drop-down scoring options, the method facilitates rapid data entry, minimizes ambiguity, and ensures consistent evaluation across asset types and locations. The integration of the method into a lightweight, user-friendly application further enhances its usability, avoiding complex modeling or intrusive data collection.

Another key advantage is its privacy-preserving design. Since the method does not require high-resolution user behavior data or real-time monitoring, it can be applied without compromising consumer privacy. This makes it especially suitable for large-scale implementation across distributed assets in residential and commercial sectors.

The framework also draws strength from its conceptual alignment with the Smart Readiness Indicator (SRI), a methodology already recognized and promoted at the European policy level. By leveraging the SRI’s structure and adapting it to a flexibility-focused context, the method benefits from a certain degree of legitimacy and interoperability with existing building assessment schemes.

In relation to existing approaches, several studies have proposed qualitative classifications to assess the flexibility of electrical assets. As discussed in Section 2.2.2, the frameworks proposed by Golmohamadi et al. [14] and He et al. [17] offer valuable first attempts but rely on a limited number of criteria, often grouping assets with significantly different technical capabilities under the same category. This restricts their practical applicability when comparing assets of varying complexity or control potential. The classification proposed by Degefa et al. [29] provides a more granular and technically grounded categorization, addressing some of these shortcomings; however, the resulting framework can be difficult to interpret and apply for comparative purposes. The methodology proposed in this paper builds upon and extends the work of Degefa et al. by incorporating additional criteria and introducing a quantitative scoring approach. This structure facilitates more intuitive comparisons between assets, while the breakdown into service groups—Electrical Characteristics, Observability, and Controllability—enables users to focus on the aspects most relevant to their objectives. In doing so, the proposed method bridges the gap between qualitative taxonomies and highly detailed quantitative models, advancing both the practical usability and scientific rigor of flexibility assessment.

Nonetheless, the approach also presents some limitations. First, its effective use by prosumers or end-users may be challenging, as they may not be familiar with all the required technical inputs—such as battery state-of-charge thresholds, Heating, Ventilation, and Air Conditioning (HVAC) control interfaces, or PV system configuration modes. While the methodology is designed to be simple, its full value may only be realized when completed by a knowledgeable third party (e.g., installer or aggregator). Second, the current version of the tool is limited to a defined set of asset types, primarily residential in nature. Although these represent a significant portion of flexible demand, broader coverage is needed to address industrial and commercial systems, as well as more complex control architectures.

Despite these constraints, the proposed method constitutes a promising first step toward the standardization of asset-level flexibility classification. Its modular structure provides a strong foundation for future enhancements, including the extension to new asset categories (e.g., industrial chillers, process loads, building energy management systems), or the development of custom scoring profiles for different user types and market contexts.

Another promising research direction would be the creation of an aggregated Flexibility Readiness Indicator at the prosumer or building level, combining individual asset scores with behavioral metrics such as historical participation in flexibility programs or response to external signals. This would enable aggregators, DSOs, or local energy communities to benchmark and optimize the flexible capacity of their user base.

From a broader perspective, this tool contributes to the democratization and operationalization of flexibility assessment. It allows stakeholders to shift from qualitative assumptions to quantifiable metrics, paving the way for greater transparency, interoperability, and inclusion of smaller players in flexibility markets. The standardized cataloging of assets could facilitate automated onboarding processes in digital platforms, improving scalability and reducing administrative burden.

6. Conclusions

In summary, while improvements are still needed—particularly in terms of scope, automation, and personalization—the proposed method already provides valuable functionality and fills a key gap in current flexibility assessment frameworks. The analysis presented in Section 4 demonstrates the methodology’s ease of implementation and highlights the robustness of the proposed Static Flexibility Index (SFI) across different asset categories.

The methodology offers a practical pathway toward wider integration of distributed assets in smart grids and demand-side response strategies. Its accessibility, privacy-preserving design, and modular structure make it a useful decision-support tool for a range of stakeholders, from manufacturers and technology integrators to aggregators and policymakers, supporting more informed deployment and participation in flexibility markets.

The main limitation of this work is that it was not possible to evaluate a sufficient number of commercial assets to draw statistically significant conclusions about current market tendencies. Addressing this limitation and extending the dataset to include a broader variety of assets represents a clear direction for future work, enabling a more comprehensive assessment of flexibility potential across real-world installations.