Synergy Between Demand Flexibility and Energy Communities: A Literature Review

Abstract

Energy communities (EnCs) are increasingly promoted as a means to support the decentralisation of energy systems and the integration of renewable energy sources, yet lack popularity and wide adoption. At the same time, demand flexibility is an important mechanism that can support EnCs by bringing additional benefits. Nonetheless, these concepts are often addressed separately or incoherently in policy and research, so the benefits of integrating them remain unclear. This paper aims to bridge these concepts and analyse their interaction. First, it sets the context by analysing legal and policy developments and the research landscape. It presents a systematic literature review of recent peer-reviewed publications, consolidating findings across three thematic areas: benefits and challenges for EnC members and system operators, approaches to enhance flexibility, and enabling factors, such as technical issues and data availability. The paper also outlines suggestions where further research is required to better understand and operationalise demand flexibility at the community level. The review shows that demand flexibility can significantly enhance the economic, environmental, and system-level benefits of EnCs but its integration remains fragmented and uneven. In particular, the complex interactions between internal and external incentives, stakeholder perspectives, technologies, and data availability are still insufficiently explored.

1. Introduction

1.1. Regulatory and Legal Background

From the European Union (EU) legislation point of view, energy communities (EnCs) are introduced in two main directives: 2018/2001 (by defining renewable energy communities or RECs) [1] and 2019/944 (by defining citizen energy communities or CECs) [2].

The concept of demand flexibility in the aforementioned EU legislation is not given, but rather operationalised through “demand response” definition [2], and in 2018/2001 and 2019/944 is used in several description forms, such as “flexibility”, “demand flexibility”, “demand-side flexibility”, etc. Thereby, to merge these forms into one, from this point forward consumption pattern changes are defined as “demand flexibility”.

Although EnCs and demand flexibility are conceptually and legally defined as distinct entities, the evolving EU regulatory framework increasingly, however not explicitly, enables their convergence. EnCs can no longer be confined to the role of small-scale renewable producers. Rather, they are progressively recognised as potential providers of flexibility services in emerging electricity markets. This is supported by Article 16(3)(a) of [2], which states that CECs must be able to “access all electricity markets, either directly or through aggregation.” Therefore, in practice, this means that EnCs can take part in flexibility schemes, either by shifting their own consumption or organising members to do so collectively.

Another example of demand flexibility introduction in EnCs is the Peak-Shaving Product, introduced in Regulation (EU) 2019/943 [3] and amended by Regulation (EU) 2024/1747 [4]. It allows system operators to procure a reduction in electricity demand during peak hours to ensure security of supply. It is a formalisation of the use of demand flexibility, which encourages electricity users to reduce their electricity consumption, avoiding the use of fossil-fuel-based electricity generation. Since the regulation explicitly forbids the activation of fossil generators behind the metre, it aligns well with the renewable character of most EnCs.

Moreover, Regulation (EU) 2018/1999 [5] requires EU Member States (MSs) to report in their National Energy and Climate Plans how they promote both demand flexibility and EnC implementation. This pushes governments to acknowledge these models as well as provide funding, remove legal barriers, and ensure access to relevant energy markets. Yet, EnCs are still not systematically recognised or incentivised to participate in demand flexibility services across all MSs [6].

The first EU-wide initiative to promote the inclusion of demand flexibility in EnCs through long-term planning is “Digitalising the Energy System—EU Action Plan” [7]. It includes the establishment of the Energy Communities Repository project, which aims at identifying and evaluating digital tools, as well as developing guidance on energy sharing and peer-to-peer (P2P) exchange mechanisms. Moreover, the action plan envisions creating a platform to simulate the functioning of EnCs and to test behavioural responses to price fluctuations. In a similar vein, the “EU Action Plan for Grids” [8] outlines the need to invest in new energy grids and upgrade existing ones, to advance the goal of decentralisation, and emphasises interconnectivity and demand flexibility. Another aspect discussed at the EU level is reflected in the “Commission Recommendation on Energy Poverty” [9]. It determines the necessity to focus on vulnerable households to make better use of energy sharing schemes and demand flexibility.

Despite the wide variety of action plans, market design reforms adopted in 2024 reinforce this link but expose its fragility. While the EU policy reforms promote long-term goals, they remain primarily geared toward generation, and demand flexibility is described not as a core market activity but rather as an auxiliary tool [10]. The ambition to “activate consumers” clashes with legacy structures that continue to privilege large-scale supply-side solutions over distributed, demand-responsive ones. This is especially the case in those countries that did not previously incentivise such structures, before the adoption of the European Green Deal and other relevant legislation and regulations.

The EU policy framework obligated MSs to introduce different incentives to make demand flexibility more attractive in practice through EnCs [2]. Within this context, self-consumption can also be viewed as a form of demand flexibility, as it links electricity use with the financial benefits of informed electricity consumption. In many countries, such tools are subsidies; however, the EU legislation framework does not prescribe a uniform minimum self-consumption threshold. Instead, approaches vary across the EU.

In Italy, incentives are directly tied to the amount of electricity collectively self-consumed [11,12]. However, in Spain, the taxation on self-consumed energy and grid fees has been removed, combined with the 2021 time-of-use tariff reform that encourages consumers to shift demand toward cheaper hours [13,14].

A different case can be observed in the Netherlands, where the demand flexibility promotion is motivated by the internal problems with infamous grid congestion [15]. Flex-e subsidy supports large electricity consumers in congested grid areas by funding flexibility scans, feasibility studies, and concrete implementation measures [16]. Therefore, if the EnC is located in a congested grid area and qualifies as a large-consumer connection, it can apply for the subsidy and subsequently implement flexibility measures based on the recommendations provided. On top of that, the Netherlands has a strong local energy cooperative movement (over 700 citizen-led cooperatives with ~131,000 members) [17], which often experiments with demand flexibility, storage, and community-level coordination mechanisms for flexibility [18].

Germany has opted for a more regulatory route: from 2025 onwards, all suppliers are required to offer dynamic electricity tariffs, linking retail prices directly to short-term wholesale markets and giving consumers pricing information and the opportunity to shift their demand [19].

Overall, integrating demand flexibility into the operation of EnC is consistent with the underlying rationale, as EU legislation increasingly frames flexibility as a core component of a fair and efficient energy transition. While MSs have created enabling frameworks, the actual incorporation of flexibility into community practices remains partial and uneven. In many cases, citizens lack clear information, regulatory guidance, or technical tools to adjust their consumption in response to price signals or local generation patterns. By contrast, countries such as the Netherlands showcase how flexibility becomes meaningful only when national policies align with local socio-technical conditions, enabling some communities to respond to system needs while simultaneously leaving others excluded. Elsewhere, demand flexibility is recognised in legislation but remains weakly embedded in everyday practice, thus limiting its potential contribution to equity, affordability, and system efficiency. The policy review, therefore, indicates the necessity to enable EnCs to demonstrate how demand flexibility can be organised at the community scale and translated into practical, member-driven action.

1.2. State of the Art

Uptake and expansion of real EnCs across the EU are uneven and limited [20]. As proposed by Bukovszki [20], Lazdins et al. [21], Di Lorenzo et al. [22], and Neij et al. [23], this modest interest is based on a range of mutually connected challenges: complexity of national and local regulatory environments, the scarcity of dedicated financial instruments, and the general lack of awareness among end-users regarding the potential social, economic, and environmental benefits. Moreover, investors often regard community-based initiatives as uncertain or low-return ventures, further slowing down their mainstream adoption. Furthermore, Mutule et al. [24] determine that existing individual studies tend to examine only specific types or characteristics of flexibility, without providing a shared framework that captures its diverse dimensions.

The mentioned gaps between policy ambition and practical realisation reveal a necessity for the investigation of barriers that EnCs are facing and identification of the strategic enablers that might bridge this divide.

Morales-España et al. [25] address demand flexibility aggregation methods for large-scale power system models. Furthermore, several studies explore aggregation techniques for individual energy users, outlining associated challenges and potential solutions [26,27,28,29]. These studies support the adoption of demand flexibility across various scales; however, they often overlook the specific role of flexibility within the EnCs specifically.

Parrish et al. [29] provide an analysis of various demand management techniques, methods, programmes, and participants. However, the study does not discuss how these management strategies are (or could be) applied for different EnC models. Likewise, Honarmand et al. [30] conduct an in-depth review of demand flexibility measures and methods, but only briefly touch on their implementation in EnCs. Bertolini et al. [31] review existing aggregation mechanisms, focusing mainly on the benefits of the aggregator. Furthermore, Pedram et al. [32] list multi-objective flexibility optimisation methods, indicating future work for other research studies to examine existing technological and communication solutions for integrating demand flexibility in EnCs. Finally, Ponnaganti et al. [33] list demand flexibility measures with potential applicability to EnCs, but emphasise the necessity of further examining stakeholder engagement, business models, supportive legislation, and policies, as well as other additional aspects, which must be tackled further to fully realise mutual demand flexibility and EnC integration.

1.3. Study Motivation and Research Questions

Based on the pre-research presented in Section 1.1 and Section 1.2, reviewed publications and regulatory acts lack overall practical guidelines, particularly those addressing contextual differences. In particular, a notable gap persists in the literature concerning the integration of demand flexibility with EnCs. Furthermore, the potential to transfer innovative solutions from individual users to EnCs remains underexplored. While various reviews touch upon relevant aspects, they often fall short in offering a connecting multi-dimensional perspective that explicitly captures the synergy between demand flexibility and EnCs. In this context, synergy refers to reinforcing mutual interaction whereby EnCs enable demand flexibility practices, while demand flexibility, in turn, strengthens the value of EnCs. The synergy interaction between energy communities and demand flexibility, together with their enablers, is shown in Figure 1.

This raises the following research questions:

• How can EnCs promote the implementation of demand flexibility?
• How can demand flexibility strengthen the attractiveness of EnCs?

These research questions are not fully covered by any single literature review developed by other authors. This may be due to the limited number of literature reviews addressing EnCs in combination with overall demand flexibility measures. To tackle this, this paper provides a synthesis of existing studies highlighting trends, principles, and challenges related to flexibility and its application in EnCs.

To address the research questions, this paper focuses on the following key areas, which uncover the interactions that form the synergy between demand flexibility and EnCs:

• Benefits and disadvantages for relevant stakeholders, which arise from the integration of demand flexibility within EnCs;
• Approaches that can enhance the mutual attractiveness of demand flexibility and EnCs;
• Availability of information to perform demand flexibility modelling and analysis studies for their implementation in EnCs.

This literature review has the potential to become a practical reference that brings together individual studies and explains how demand flexibility and EnCs can work together. In doing so, it can support future studies as well as real-world applications.

The paper is structured as follows: Section 2 describes the literature selection methodology and the corresponding key review areas. Section 3 presents the results according to key area subdivisions. Section 4 discusses the main conclusions and identifies directions for future research.

2. Methodology

To ensure a sufficient literature analysis, a multi-faceted approach is employed, drawing upon the methodology described in [34].

The literature selection and filtration flow diagram is depicted in Figure 2.

Initially, the SCOPUS database is used to identify a broad scope of collected peer-reviewed research papers from widely recognised and well-established publishers (MDPI, IEEE, Elsevier, etc.).

Literature selection begins with the use of a determined search strategy, using the following search string: (TITLE-ABS-KEY (energy communities) AND TITLE-ABS-KEY (demand flexibility)) AND PUBYEAR > 2019 AND PUBYEAR < 2026 AND (LIMIT-TO (LANGUAGE, “English”)). It is primarily based on the following inclusion criteria:

• (TITLE-ABS-KEY (energy communities) AND TITLE-ABS-KEY (demand flexibility)): search consists of main and thematically determined keywords of the literature review (“energy communities” and “demand flexibility”);
• AND PUBYEAR > 2019 AND PUBYEAR < 2026: publication period of 2020–2025 to capture recent research trends, analytics, and results in the latest 5 years;
• AND (LIMIT-TO (LANGUAGE, “English”)): only papers in English are considered.

The literature selection strategy avoids the use of citation thresholds in order to promote an unbiased and equitable inclusion of peer-reviewed papers. This approach is enabled to avoid the effect of the length of publication availability or perception of analysis or information by publication readers/quoters.

A search conducted in January–June 2025 obtained a total of 439 publications.

Following the assessment of the obtained papers (abstract reading), further literature filtration is based on their compliance with determined research questions and key areas mentioned in Section 1.3: (a) whether publication includes benefits or disadvantages for the inclusion of demand flexibility in EnCs; (b) whether publication indicates and analyses the approaches that are complimentary to demand flexibility in EnCs; (c) whether it contains information which is or can be used to model or analyse demand flexibility inclusion in EnCs.

By manually reading the publication abstracts, each of them is evaluated if it contains the information specified in the criteria (a) and/or (b) and/or (c) or not. Only papers with a positive evaluation are used in this literature review (80 papers).

Further publication analysis consisted of common points and subdivision identification based on (a), (b), and (c) criteria, including publications whose content met more than one of the determined criteria. In this process, all 80 papers were read in full and divided into subsections used in the literature review: 1. Benefits and disadvantages → EnC participants and system operators; 2. Synergy enhancement opportunities → Electricity sharing mechanisms and generation shifting; 3. Information availability → Load management and data repositories and datasets.

The next section presents the findings of the literature review, according to the determined key areas and research subsections.

3. Revealing Synergy Between Demand Flexibility and Energy Community

3.1. Benefits and Disadvantages for Stakeholders

3.1.1. EnC Participant Perspective

The active involvement of consumers and prosumers is essential for carrying out and sustaining demand flexibility measures. This subsection therefore outlines the key benefits and challenges they may encounter, and examines these issues from economic, technical, environmental, and social perspectives.

Overall, economic/financial benefits are considered to be the primary motivator for the demand flexibility integration into electricity usage operations, followed by environmental benefits emerging as a secondary motivator [29].

Chen et al. [35] and Ponnaganti et al. [33] emphasise that demand flexibility can help to significantly lower overall energy costs for everyone in the EnC, thus mitigating disadvantages associated with flexibility integration. Honarmand et al. [30] support this idea by stating that demand flexibility measures can positively affect controlled power generation equipment operating costs by saving on fuel costs, repairs, maintenance, and overall wear and tear. In other words, shifting electricity demand into a specific period reduces strain on equipment, which allows for expanding its operational life expectancy. This contributes to the related economic savings. In addition, Ponnaganti et al. [33] and Sangaré et al. [36] highlight that implementing flexibility measures can substantially lower overall electricity costs for consumers. This is achieved by better aligning electricity generation and consumption, which induces a smoother load curve and increases the self-consumption rate.

Furthermore, participants engaged in demand flexibility can receive remuneration (cash payments) from aggregators or other flexibility managers for their contributions to aggregation markets [30]. This incentive can motivate EnC participants to get involved more actively in demand management activities, potentially increasing both community-wide and individual financial benefits.

Similarly, Okur et al. [28] support these advantages, noting that additional economic gains can be achieved not only through aggregator services but also via demand shifting correlation with grid electricity prices. Particularly, when dynamic electricity pricing is used, EnC participants are encouraged to consume electricity imported from the grid during lower-price periods. This approach enhances cost savings and promotes greater resource consumption awareness and trust in flexibility initiatives. Ultimately, these asset operations remain fully under the control of the EnC or individual consumers/prosumers within the community.

If the primary goal of EnCs is to achieve financial benefits, Gržanić et al. [26] determine that P2P electricity sharing is one of the key mechanisms to achieve a fair distribution of electricity generation costs through the implementation of internal electricity sharing prices. They also conclude that within the P2P electricity sharing model, prosumers must be able to control their electricity assets according to their own rules and wishes, thus determining that sharing guaranteed electricity may reduce prosumers’ economic benefits due to low feed-in tariffs in some time periods. This is further supported by the findings of Ponnaganti et al. [33]: it is more economically advantageous to maximise internal sharing of electricity among EnC members rather than exporting it to the grid for sale or settling surplus through a net metering or billing system. To increase the economic benefits using the synergy between electricity sharing and demand flexibility, the internal electricity sharing price should be set higher than the grid export remuneration rate but lower than the price of imported electricity from external traders [37]. Such pricing would incentivise immediate consumption of locally generated electricity, thereby enhancing the necessity for demand flexibility, increasing overall self-consumption, and reducing costs associated with grid imports and third-party electricity trading services.

From the environmental perspective, Honarmand et al. [30] and Amin et al. [38] find that by reducing and shifting overall electricity consumption, large-scale and carbon emission-intensive centralised electricity generation will also be reduced and can be partially replaced by the EnC’s generated electricity. Aforementioned authors jointly state that demand flexibility measures aim at reducing the necessity for the centralised electricity grid infrastructure expansion. This therefore helps to avoid ecological impacts associated with new infrastructure construction, reduces greenhouse gas emissions, and improves air quality and public health. These impacts become more significant when the individual consumers/prosumers are integrated into community initiatives to enhance greater communication and cooperation in locally generated clean energy consumption.

Technical and technological advantages go hand-in-hand with environmental and economic benefits. Zahraoui et al. [39] indicate that demand flexibility measures reduce and even require no necessity for battery energy storage systems (BESSs) and other storage technologies, which would be used to artificially shift locally generated electricity supply closer to demand periods. In contrast, Pedram et al. [33] contend that these systems are crucial EnC components to ensure both successful flexibility implementation and increased economic, social, and environmental benefits arising from the increased self-consumption level. Parrish et al. [29] determine that technical issues and specific electricity sharing automation schedules could erode trust of EnC participants over time. However, demand flexibility automation can provide greater benefits than manual direct load control [29]. If automated demand flexibility is not accepted by EnC participants, information and monitoring tools can serve as a middle ground, keeping EnC participants informed about the amount of electricity shared and supporting efficient consumption. Pallonetto et al. [40] support this and conclude that semi-automatic demand flexibility systems (information and monitoring tools) can introduce higher comfort and cost control, since they increase electricity users’ knowledge of their own energy consumption and provide awareness of related environmental and social issues.

Continuing with the social aspects, Gržanić et al. [26] argue that demand flexibility can lead to the highest levels of social welfare. This is largely because these approaches are based on simple, intuitive EnC business models, which are easier for community participants to engage with. Additionally, business models often do not impose the operational complexities and constraints associated with formal electricity trading systems between the EnC and external traders. Other studies [30,39] emphasise that demand flexibility measures, when tailored to align with the values and objectives of community participants, such as lowering electricity bills or enhancing resilience during power outages, can further improve social well-being. They show that demand flexibility can contribute to alleviating energy poverty, foster greater social inclusion by cultivating a stronger sense of community, and support a more equitable sharing of locally generated electricity [31,35].

However, studies have identified that there are several factors affecting EnC participants’ willingness to participate in demand flexibility measures. As suggested by Parrish et al. [29] and Amin et al. [38], involvement of community members in the demand flexibility measures encompasses two main aspects: trust in the overall process and related knowledge of how these measures operate. More specifically, EnC participants may mistrust the perceived motivations of flexibility organisers due to unfamiliarity with flexibility organisation aspects and cost savings calculations. Other concerns are related to electricity demand data leakage, privacy, and the enrolment of direct load control without any automation or compliance with user preferences. Authors determine that the acceptance rate of user enrolment in demand flexibility is ambiguous due to several socio-demographic factors, mainly household income and size.

In summary, EnC participants’ financial gains depend on internal, external, and combined pricing signals, but the interaction between these factors is still poorly understood and needs better frameworks to guide flexibility choices. Environmentally, there is limited research comparing household- and community-scale renewable systems; however, many important factors like land use, emissions, and infrastructure impacts have been taken into account in benefit assessment. On the technical and social side, the value of BESSs, automation, and user trust in demand flexibility remains uncertain, pointing to the necessity for detailed research on both technology and electricity consumption behaviour.

3.1.2. System Operator Perspective

The emergence of EnCs marks a significant shift in the structure of modern energy systems. While demand flexibility can offer environmental, economic, and social benefits for EnC members, in parallel, it also introduces new challenges for system operators responsible for maintaining grid stability, reliability, and efficiency. From the system operator’s perspective, integrating flexibility into grid operations requires new approaches to forecasting, control, communication, and market design. Thereby, this subsection explores the role of EnCs in providing demand flexibility, focusing on the opportunities, challenges, and benefits from the viewpoint of power system operators.

Uzum et al. [41] discuss the unique and interconnected roles Transmission System Operators (TSOs) and Distribution System Operators (DSOs) play in managing the flow of electricity, ensuring a reliable and responsive grid, and enabling the integration of flexible resources that can adjust to changing demand and generation patterns. Rosales-Asensio et al. [42] and Kara et al. [43] investigate a pivotal role of TSO in ensuring grid stability by balancing electricity supply and demand across the entire system. Koltsaklis et al. [44] point out that electric power balance is ensured by regulation services: frequency containment reserves, automatic frequency restoration reserves and manual replacement reserves. These energy products could be offered by EnCs and exchanged via the balancing market.

In the context of flexibility services, the DSO’s role is crucial at the local level, as it works to integrate flexible energy resources into the grid, such as small-scale distributed generation. Duma et al. [45] describe that DSO is involved in ensuring that the distribution grid can accommodate these flexible resources, which help to balance supply and demand within the local grid.

The recent surge in electricity prices has strained both households and businesses, prompting consumers and developers, as noted by Falk et al. [46], to adopt solutions that reduce exposure to price fluctuations. Moving to decentralised electricity generation, growth of controllable loads, electric vehicles (EVs), and heating pumps causes new challenges for the stable operation of electricity grids, both for DSOs and TSOs. Etanya et al. [47], Khalid [48], Mataczynska et al. [49], and Yeboah et al. [50] indicate that one of the main drawbacks of renewables is electricity generation variability and unpredictability that causes challenges for DSOs (Figure 3) associated with their integration into the electricity grid, such as:

• Continuity (electricity generation depends on weather conditions);
• Grid stability (balance of real-time electricity generation and demand is necessary);
• Curtailment due to the excess of renewable energy generation to prevent grid overloading.

Rehman et al. [51] explain that one of the most important aspects to be assessed in the context of increasing power generation is the efficient use of the electricity generated locally. Electricity generation at a given moment may exceed the consumption needs of prosumers or locally connected consumers, which results in surplus electricity exported to the distribution or even to the transmission grid. The effective use of surplus electricity can be ensured by accumulating it or using it within an EnC. Cosic et al. [52] report that in a real case study of nine EnCs in Austria, the total photovoltaic (PV) self-consumption increased from 26.5% (before EnCs were created) to 65.2% by enabling the renewable energy transfer between EnC participants. Authors conclude that EnCs can reduce total energy costs by 15% and carbon dioxide emissions by 34% through optimal selection and operation of energy technologies. This includes increasing installed PV capacity and adding BESSs to store PV-generated electricity, enabling greater demand flexibility benefits and sharing of produced electricity among EnC participants.

To meet increasing demand or integrate more renewables into the grid, expensive expansion and modernisation of the grid infrastructure, such as building new distribution lines or building new substations, is required. DSOs are required to consider alternatives to investment in new infrastructure to maximise the hosting capacity of the existing distribution grid and to operate more efficiently while contributing to greater integration of renewables. For DSOs, reducing power peaks through demand flexibility is one possible alternative that offers additional grid resilience. Holweger et al. [53] demonstrate in a low-voltage grid case study that the use of distributed flexibility is more profitable than the grid reinforcement until the PV generation covers 145% of the network’s annual energy demand.

Panda et al. [54] analyse and describe demand flexibility-related solutions that can provide a more cost-effective alternative by optimising the use of existing grid infrastructure. Honarmand et al. [30] describe that flexibility can reduce the operational costs associated with grid maintenance by improving the efficiency of energy distribution, asset performance, and reducing power losses. As noted by Lopez et al. [55], EnCs can support DSOs by helping manage grid congestion and maintain voltage stability during peak generation, peak demand, or unexpected events.

Conversely, Adegoke et al. [56] analyse how locating distributed generation far from the place of consumption leads to higher power losses. For instance, while participants of the EnC are typically connected to the distribution grid in urban areas, their PV plant may be situated in a rural location. In some cases, excess electricity from these rural areas is transported to consumption sites not only through the distribution grid but also via the transmission grid, increasing overall system power losses. That is why, to decrease power losses in the distribution grid, generation used by EnCs should be located close to EnCs’ members.

Abada et al. [57] point out negative economic effects from demand flexibility related to the distribution tariff. The paper discusses that EnCs often save on grid tariffs, decreasing the electricity consumed from the distribution grid (variable part of the tariff) but DSOs’ costs are mainly unchanged. In other words, a decrease in the amount of distributed electricity will result in lower income for DSOs, although the costs of maintaining the existing grid remain the same. As a result, DSOs need to increase tariffs. This leads to more consumers investing in PV systems and increases the amount of EnCs, thus creating a negative snowball effect.

Moreover, Panda et al. [54] describe risks for DSOs if demand flexibility is provided by participants of EnCs: customers’ responses to price signals, which influence their consumption behaviour, can be unpredictable. Unlike incentive-based flexibility service (Banaei et al. introduce an incentive-based method for procuring flexibility in [58]), system operators cannot control (for example, remotely) EnC consumption or generation. Thus, there is no guarantee for DSOs that demand flexibility will be provided in the necessary amount in the specified area and time when it is needed.

The findings from the review of the literature indicate that tariff structures rewarding EnCs for providing flexibility should be developed in a way that maintains fairness and reliability. Dedicated tariffs can promote flexibility. However, EnCs consume less electricity from the distribution grid, reducing DSO revenues while DSO costs remain mostly unchanged to maintain the network. The review also highlights the need for control platforms that enable system operators to monitor and interact with distributed resources in EnCs, making the availability of flexibility uncertain and complicating grid stability and planning. Finally, clear operational boundaries and responsibilities between system operators and EnCs must be established in legislation to avoid conflicts and ensure transparency, including specifying consequences when flexibility is not delivered. These recommendations aim to bridge the gap between community-level activities and system-level reliability, ensuring that demand flexibility becomes an important and scalable asset in future energy systems.

3.2. Synergy Enhancement Opportunities

Beyond conventional demand flexibility measures, several emerging but often overlooked approaches could have the potential to further support the implementation, development, and acceptance of EnCs. These approaches can also enhance the overall benefits of demand flexibility. Therefore, the following subsection investigates innovative mechanisms that can potentially strengthen the integration of demand flexibility within EnCs.

3.2.1. Electricity Sharing Mechanisms

EnCs differ from other renewable energy projects, as their performance depends on both collective actions and the behaviour of individual members. In this context, flexibility integration can be potentially strengthened with the rewarding of individual efforts, including by aligning benefit-sharing mechanisms.

Economic viability of EnCs, as well as their potential to support grid flexibility, is linked to two distinct energy trading levels: an external market for the whole community energy services and an internal market or other methods employed to redistribute locally generated energy and benefits among the EnC members [59,60]. External market arrangements vary across EU MS, and electricity retailers may also offer different contractual options. Minuto and Lanzini [61] identify three general categories of pricing models: fixed pricing, time-of-use pricing, and dynamic pricing. Under dynamic pricing, the electricity price is adjusted in response to demand-to-supply relations, grid congestion or market conditions. The latter model paves the way for exploiting flexibility. At the same time, authors summarise different categories of methods for redistributing energy and benefits: equal distribution, individual contribution-based, ownership-based, community-centric, and third-party reward.

Two general approaches to redistributing locally generated energy exist in the EnCs: costless P2P energy distribution (i.e., without trading) or P2P trading [61]. Various algorithms have been proposed to implement both models, most commonly using optimisation or game theory [62]. While these approaches can improve the EnCs’ flexibility potential, they may also entail considerable computational complexity and remain difficult to realise in practice.

In collective self-consumption schemes, electricity and benefits sharing usually follow simpler rules. These internal rules are referred to in the literature as sharing mechanisms [60], sharing keys [63], repartition keys [64], distribution coefficients [65], distribution keys [62], and similar terms. These allocation rules can be defined and computed in many ways and may be fixed or dynamic.

In many countries, no explicit regulations exist regarding these mechanisms, leaving the opportunity for EnC members to set their own rules. Some countries have proposed a national sharing mechanism framework. For instance, in Spain, the relevant DSO is in charge of allocating energy production according to the community’s predefined allocation coefficient and calculating the balance between production and consumption on an hourly basis [65,66]. If distribution coefficients are not defined, the DSO allocates energy based on the contracted power [65]. Similarly, in France, an EnC can apply either a custom or default allocation coefficient, according to which energy is allocated between consumers in proportion to their consumption compared to the total consumption of all the EnC participants [67].

The literature analyses allocation rules in terms of prosumer benefits [68], profitability [67,68], conceptual simplicity [69], members’ bill reduction [70], and fairness [71,72]. Roberts et al. [73] emphasise the difficulty of balancing the objectives of cost recovery, efficiency, acceptance, and universal benefit. Flexibility potential of EnCs is usually analysed theoretically and embedded in complex optimisation models [62]. Some examples are provided in the following paragraphs.

Volpato et al. [74] quantify the economic benefits of three main factors affecting the profitability of EnCs: the complementarity between energy demand and generation profiles of prosumers, cost allocation criterion, and the application of price-based demand flexibility. The analysis assumes that every household in the EnC is equipped with a home energy management system (HEMS) capable of dispatching flexible loads. Authors conclude that price-based demand flexibility may reduce EnC costs beyond 50%.

Similarly, Zhao et al. [75] present a management method that integrates demand flexibility into P2P sharing using a multi-objective optimisation, considering that all households are equipped with HEMS. Authors conclude that this approach can lower EnC costs, power fluctuations, and enable fair revenue distribution.

Görgülü et al. [76] introduce an optimisation model for P2P trading considering demand flexibility of different EnC setups combining PV with storage and responsive EV.

Gomes and Vale [61] propose the use of demand flexibility to minimise the EnC electricity imports from the grid by balancing consumption and generation inside the community. They present an electricity distribution model of community-owned generation based on cooperative game theory, considering member participation in demand flexibility. Thus, the paper addresses demand flexibility as an internal mechanism rather than relying on common grid-based signals.

In practice, there is a research gap in quantifying how electricity allocation mechanisms complement the flexibility potential of EnCs. Analysis of a real-world example shows that without HEMS, neither grid tariffs nor sharing coefficients are evaluated to incentivise or justify demand peak shifting and shedding activities [77]. Moreover, it must be underlined that the installation of HEMS can be a financial hurdle for electricity users to implement demand flexibility measures. To sum up, there is a trade-off between complexity and practical feasibility: sophisticated models can unlock flexibility, but their implementation in real-world EnCs remains limited.

3.2.2. Direct Generation Shifting

Flexibility includes more than shifting or adjusting electricity consumption in response to available generation. It also refers to adapting electricity generation to better match consumption (supply-side or generation flexibility). Generation flexibility in the following paragraphs refers to adjustments made within the own generation assets of EnCs. Both forms of flexibility increase self-consumption in EnCs. This is especially important when demand flexibility is limited, for example, when load shifting is difficult due to a high share of non-shiftable loads. Together, these also help reduce the load on the electricity grid.

Generation flexibility can be achieved in two distinct ways: directly, by modifying the operating parameters of generation units (e.g., output amplitude, start-up timing, and duration) or indirectly, through the use of BESSs that charge and discharge without altering the generation unit’s operation [78]. BESS inclusion can also serve as a complementary approach to amplify the overall goals to better match EnC generation and consumption. While several review studies have already provided in-depth assessments of BESS integration and operation within EnCs [59,79,80], the systematic exploration of direct generation flexibility potential remains limited. Thereby, this subsection considers the potential of direct generation flexibility with the aim of drawing together existing insights and offering a clearer perspective on how it could complement demand flexibility benefits.

Gönül et al. [81] and Barbón et al. [82] investigate the techno-economic potential of PV panel electricity generation flexibility based on key technical parameters, including fixed tilt and solar tracking strategies, efficiency coefficients, irradiance levels, geographical location, and temperature. A similar theoretical approach for wind energy is presented in [83], where the authors examine how different wind turbine control strategies affect power generation patterns to better match consumption profiles. Thereby, these calculation principles serve as a basis for determining compatibility between generation and consumption for both individual prosumers and EnC configurations.

A review of studies on the implementation of generation flexibility in EnCs reveals that research in this area remains highly limited. To our understanding, only one publication directly addresses the flexibility of renewable energy sources included in EnC without the assistance of BESSs. In detail, Minuto et al. [84] implement Monte Carlo simulations and Italy-based case studies to demonstrate that conventionally adopted south-facing PV panel orientation, while optimal in terms of annual electricity generation, is not the most suitable for EnCs. Instead, east- and west-facing orientations with reduced tilt angles can achieve a generation profile that better matches EnC electricity consumption and improves self-consumption in PV systems without solar tracking. Moreover, Minuto et al. further note that this line of research has so far been overlooked or undervalued, thus stressing the importance of conducting similar investigations in other European country contexts.

In conclusion, the number of case study-based investigations is currently insufficient. Nonetheless, the theoretical foundations for applying generation flexibility through both PV panels and wind turbines are well developed and can support real-world implementation or further case study exploration. Overall, direct generation flexibility in EnCs appears to be underexplored in the existing literature, indicating a significant research gap and potential in its practical application and assessment.

3.3. Factors Supporting Demand Flexibility Implementation

Effective flexibility depends not only on enabling technologies but also on the availability and quality of supporting information, including datasets. This subsection reviews existing flexibility-related resources and their relevance for EnCs.

3.3.1. Load Management Potential

Demand flexibility is based on the ability to individually manage the electrical load of devices. Thereby, this section takes a closer look at load types and their role in providing demand flexibility in EnCs.

Electrical loads are commonly classified based on their ability to be operated outside their typical usage hours without reducing user comfort or convenience, namely, shiftable and non-shiftable loads. Shiftable loads could be shifted to a different time interval depending on the consumer’s preferences and availability, while the non-shiftable loads cannot be used at various times without reducing consumers’ comfort or appliance operating task execution. Examples of shiftable and non-shiftable loads used in the research studies are given in

Table 1. Examples of shiftable and non-shiftable loads used in studies.

Shiftable Loads	Non-Shiftable Loads
Vacuum cleaner, dishwasher, laundry machine, clothes dryer, EV charging, phone charger, BESS, electric boiler, electric heating, pool pumps	Conditioner, ventilation, air cooling, refrigerator, lighting, iron, hair dryer, TV, medical equipment, stove, oven, microwave, security systems, alarms, cameras

[33,85,86,87,88,89,90].

Despite the growing body of research addressing demand flexibility for individual electricity users [85,86,91,92,93,94], existing EnCs-related studies identify and include load-related limitations.

For example, Rollo et al. [95] employed uncoordinated and coordinated demand flexibility to facilitate load shifting for only washing machines and dishwashers. The authors determine that these two appliances are the ones with the highest demand flexibility potential and the lowest effect on user comfort. However, the results of the case study show that economic benefits are relatively modest due to the low number of shiftable appliances included. This is also indicated by Capper et al. [96], Shepherd and Mohagheghi [97], who conclude that the majority of demand flexibility studies in local electricity markets, including EnCs, are aimed at including only a small number of shiftable appliances, thereby overlooking the full demand flexibility potential in EnCs.

Ponnaganti et al. [33] conclude that the main concept of EnCs is to incorporate various multi-carrier energy systems; however, only a few studies explicitly include low-power household appliances in their demand flexibility studies. This shows that a wider number of appliances (and especially low-power-consuming appliances) could further improve the justification of demand flexibility inclusion in EnCs.

There are strong indications from relevant studies that existing research results overlook the broader structure of EnCs by emphasising a low number and high-capacity loads (washing machines, dishwashers, electric boilers, etc.). Including these appliances in related studies would reveal the hidden potential for more effective demand flexibility inclusion, not only for individual electricity users but also for EnCs.

3.3.2. Data Repositories and Datasets for EnC Flexibility Planning

To design and optimise demand flexibility inclusion in EnCs effectively, high-quality data and data repositories are essential. Without reliable data, planners and researchers face significant challenges in evaluating system performance, comparing scenarios and ensuring regulatory compliance. Moreover, transparent and standardised open-access datasets promote reproducibility, strengthen stakeholder collaboration and enable informed decision-making across different stages of EnCs development. This subsection explores the landscape of data repositories and datasets relevant to the EnC planning process, highlighting their importance, current gaps and opportunities for improvement.

Datasets and platforms identified in the literature are organised into functional categories reflecting their purpose, scope and granularity.

General energy system and market data platforms offer foundational information on electricity generation, consumption, transmission, and market conditions. These repositories support scenario studies and boundary-condition modelling. Gjorgiev et al. [98] describe an ENTSO-E Transparency Platform which provides a lot of different data on the generation (installed capacity, current generation and forecast), load (actual and day-ahead forecast), transmission (load flows, total commercial schedules), and balancing (prices of balancing energy, imbalance volume and price) for the European market. Open Power System Data (OPSD) is used by Alonso Pedrero et al. [99] to obtain the PV profile. Additionally, OPSD offers household load profiles as well as PV and wind generation and weather-related time series.

Residential and commercial demand datasets provide insight into detailed consumption patterns and appliance-level behaviour. Researchers and practitioners collected several datasets for residential energy demand: these datasets differ based on the geography of the buildings and the number of buildings they provide data for—some provide only electrical consumption, while others also provide metadata or other sensor data such as water and gas consumption. Kazmi et al. [100] mention 13 open-source residential buildings and six open-source commercial buildings datasets that could be used for EnC planning steps. Faia et al. [101] present a dataset representing a complete European residential community based on real-life data that includes consumers, prosumers, BESSs, and EV charging stations. Goncalves et al. [102] create a dataset of a residential community, desegregated by individual appliances used, where sample consumption and PV profiles were attributed to 50 residential households and a public building (municipal library). The designed dataset could be used in advanced energy management models for real-life simulating and planning of EnCs and smart buildings.

The increasing amount of EV leads to an increased demand for residential charging, but current knowledge of the characteristics of residential EV load profiles is limited. While EV electric loads can have a negative impact on the power grid, they also represent a large potential for demand flexibility in EnCs. Sørensen et al. [103] provide methodologies for analysing charging habits, while the dataset in [104] presents nearly seven thousand real charging sessions. These datasets support integration planning for charging infrastructure, useful for forecasting energy loads and flexibility potential.

Country-specific smart-metre datasets offer detailed local information. Thomson et al. [105] mention several UK residential energy demand datasets such as the UK Power Networks dataset of energy consumption readings for a sample of 5567 London Households and the REFIT Electrical Load Measurements dataset, which includes cleaned electrical consumption data for 20 households at aggregate and appliance level, timestamped and sampled at 8 s intervals. Berg et al. [106] present a data set (household consumption, appliance consumption, EV charging, PV power generation, and market electricity prices) that represents Norwegian EnCs. Lazdins and Mutule [37] use the Latvian largest DSO AS “Sadales tikls” provided data set of aggregated hourly profiles over a three-year period, including information on electricity users and generators from different regions of Latvia.

Synthetic, simulated and tool-generated datasets provide additional opportunities for testing algorithms in the absence of real-world data. Velosa et al. [107] present PROCSIM—an open-source simulator designed especially to create EnC datasets for multiple purposes, including tests and evaluation of different algorithms and models. It includes integration with a consumption profile generator, tools to simulate electricity generation from PV and wind, a module that generates an EnC dataset, and a set of metrics to evaluate the EnC.

Weather and climate data sources strongly influence energy demand and renewable generation and thus may affect the functioning of an EnC and its demand flexibility implementation potential. Kazmi et al. [100] mention different online services that provide access to weather data, both historically and in near-real time and short-term forecast, for example, Accuweather, OpenWeatherMap, Weatherbit. Some data sources also provide long-term climate change forecasts (for example, WorldClim 2 and World Bank dataset) needed for long-term scenario modelling and regional renewable assessments.

The development and operation of EnCs rely heavily on access to accurate, granular and interoperable data. While several universal data sets and platforms exist, few are tailored specifically to EnCs, and gaps remain in terms of standardisation, accessibility and country-specific relevance, because consumption and electricity production profiles in different countries vary widely. Data transparency and openness are important not only for robust modelling and simulation, but also for fostering trust and collaboration among stakeholders. But it introduces challenges (which could be more relevant during EnCs’ operation) related to privacy and data security: to ensure responsible and effective use of data, open data initiatives must comply with GDPR and employ robust anonymisation techniques.

Existing data repositories and datasets are helpful, but not fully sufficient for comprehensive and detailed demand flexibility research and its integration into EnC activities: many datasets are too broad or lack local context, making them unsuitable for modelling real EnCs with local socio-technical conditions. Based on the literature review, improvements in data quality, accessibility and interoperability with modelling tools are essential and needed for enabling accurate demand flexibility analysis and successful integration into EnCs. Without well-maintained and context-aware datasets, EnCs risk suboptimal design, inefficient operation and a loss of trust from stakeholders.

4. Discussion and Future Work

The literature review summarises existing knowledge in the following key areas:

• Benefits and disadvantages for relevant stakeholders, which arise from the integration of demand flexibility within EnCs.
• Approaches that can enhance the mutual attractiveness of demand flexibility and EnCs.
• Availability of information to perform demand flexibility modelling and analysis studies for their implementation in EnCs.

In each thematic area, the literature review reflects the current situation and the results of the latest research to determine future research directions. The results of each area-related review subsection are summarised and shown in

Table 2. Summary of main conclusions and identified areas for future research.

Review Subsections		Main Conclusions	Main Areas for Improvement
Benefits and Disadvantages for Stakeholders	EnC Participants	Benefits: Reduced costs; reliance on carbon-based centralised generation; emissions; need for BESS; energy poverty; increased control and knowledge of energy resources; social welfare; community cohesion; air quality and overall public health. Disadvantages: reduced comfort.	Tackling overall trust and awareness in demand flexibility and EnC management processes, data protection.
	System Operators	Benefits: Increased grid efficiency; lower grid maintenance costs; grid stability assistance by EnCs. Disadvantages: power balancing actions due to demand and generation variability and unpredictability; lower income from tariffs.	Further increasing grid stability and developing advanced curtailment reduction mechanisms.
Synergy Enhancement Opportunities	Electricity Sharing Mechanisms	P2P energy distribution and trading mechanisms can improve EnC flexibility potential, but entail considerable computational complexity, and are usually analysed theoretically.	Further efforts to determine price-based demand flexibility use in fair revenue sharing among EnC participants.
	Direct Generation Shifting	Existing direct generation shifting calculation principles serve as a theoretical basis for the assessment of compatibility between electricity generation and consumption, and demand flexibility.	Clear identification of direct generation flexibility and its implementation benefits across different EnC configurations.
Factors Supporting Demand Flexibility Implementation	Load Management Potential	Existing research emphasises a low number of high-capacity loads in demand flexibility studies.	Assessment of the impact of a coordinated high number of low-capacity load bundle flexibility implementations on EnC stakeholders’ benefits.
	Data Repositories and Datasets	Existing data repositories and datasets are helpful, but not fully sufficient for comprehensive and detailed demand flexibility research and its integration into EnC activities.	Efforts to enhance dataset repository interoperability with modelling tools, dataset standardisation, availability of country-specific data repositories.

.

The findings of the review indicate that the economic benefits for EnC participants depend on three types of demand flexibility initiators: internal (e.g., P2P pricing signals), external (e.g., imported electricity, aggregation, and ancillary service pricing signals), and a combination of both. While the individual impacts of internal and external initiators on EnC participants’ economic benefits are relatively well researched in existing studies, the combined interaction between these initiators introduces significant complexity. Specifically, the combined effect of internal and external pricing mechanisms complicates the implementation and optimisation of demand flexibility measures within EnCs, as well as the fair distribution of associated economic benefits. Electricity sharing mechanism choice can also affect these benefits by determining EnC-shared electricity pricing and distribution according to the community’s own decision and participant mutual obligations.

From an environmental benefit perspective, demand flexibility measures within EnCs’ aims of reducing the necessity for the centralised electricity grid infrastructure expansion, helping to avoid ecological impacts associated with new infrastructure construction, reducing greenhouse gas emissions, and improving air quality and public health.

From a technical and technological perspective, there is ongoing debate within the scientific community about whether demand flexibility can replace the use of BESSs in EnCs. This is partially due to the limited availability of studies that establish a clear evaluation of scenarios including BESS and/or demand flexibility measures in aligning local EnC-level generation with consumption. Furthermore, while automated demand flexibility measures can increase self-consumption, they may also introduce challenges related to user trust and comfort. The evidence suggests a balanced approach, leveraging advanced communication technologies. However, communication technology inclusion can have a significant effect on EnC economic benefits due to its purchase, installation, and maintenance costs. However, the effectiveness and broader impacts of these technologies necessitate more detailed investigation, particularly due to the complex nature of user behaviour in demand flexibility contexts.

From the system operator’s point of view, the integration of EnCs into the broader energy system presents both opportunities and challenges. EnCs with their decentralised generation and active participation in energy markets can offer valuable demand flexibility services for grid operators—a resource increasingly critical for balancing generation and consumption in grids with high shares of renewables. By using flexibility measures that EnCs could provide, system operators can mitigate the necessity for large-scale infrastructure projects and costly grid upgrades, making the overall energy system more economical. These services could help prevent congestion and allow system operators to efficiently distribute electricity across regions, even as demand and renewable energy generation vary. Additionally, demand flexibility measures can reduce the operational costs associated with grid maintenance by improving the efficiency of electricity distribution. In this way, flexibility services not only help integrate renewables and new load connections but also provide a more affordable approach to grid modernisation. Exploiting flexibility requires overcoming technical, regulatory, and operational hurdles. Key concerns include visibility of distributed assets, coordination across multiple actors, data exchange, and ensuring grid reliability. While EnCs can contribute to peak shaving, frequency regulation, and congestion management, their participation must be structured and predictable, aspects which must be tackled in future research works in this field.

The reviewed literature shows that electricity allocation and benefit-sharing mechanisms are central to both the economic viability and the flexibility potential of EnCs. Based on this, benefits can also amplify trust in demand flexibility inclusion in EnCs. The existing P2P approaches theoretically enable substantial flexibility gains; however, their practical deployment remains constrained by regulatory heterogeneity, computational complexity, and high technological requirements, such as the widespread availability of HEMSs. In contrast, simpler sharing mechanisms currently adopted in collective self-consumption schemes offer greater feasibility but provide limited incentives for demand flexibility. Consequently, there is a clear research gap in empirically quantifying how different allocation mechanisms, especially using price-based demand flexibility, can effectively reward individual flexibility contributions without imposing excessive complexity.

Direct generation shifting shows promising potential to enhance the synergy between EnCs and demand flexibility from a theoretical perspective, but existing studies remain insufficient to demonstrate real-life benefits from its implementation. Moreover, direct generation shifting is linked to solar tracking and control equipment purchase, installation, and maintenance costs. At the same time, direct generation flexibility can increase EnC self-consumption and complement demand flexibility, contributing to stakeholder benefits (based on the self-consumption increase). It creates benefit prioritisation and effect paradigms. This, together with effectiveness, scalability, and user acceptance evaluation, must be tackled in future studies.

The literature review indicates that demand flexibility in EnCs is often and predominantly assessed through a narrow subset of high-capacity and easily shiftable appliances. This approach simplifies modelling and increases overall trust by ensuring traceability of demand flexibility measures. However, it overlooks the broader and more heterogeneous load composition that characterises real-world EnCs and does not fully utilise the demand flexibility potential. Future research should therefore adopt more holistic load representations that integrate a wider range of appliances to better capture the realistic flexibility potential, operational dynamics, and socio-technical implications.

And lastly, the development and operation of EnCs rely heavily on access to accurate, granular, and interoperable data. As decentralised energy systems become more complicated, the structured datasets become increasingly essential. Despite the existence of several universal data repositories, gaps remain in terms of standardisation, accessibility, and community- and country-specific relevance. Existing data repositories and datasets are helpful, but not sufficient for detailed demand flexibility research and its integration into EnC activities. Many datasets are too broad or lack local context, making them unsuitable for modelling real EnCs with local socio-technical conditions. This can be a significant factor affecting the EnC participant’s trust and acceptance of demand flexibility evaluations and overall opinion regarding its implementation and expected benefits. Improvements in data quality, accessibility, and interoperability are essential and needed for enabling accurate demand flexibility analysis and successful integration into EnCs.

Overall, the reviewed literature highlights that demand flexibility has strong potential to enhance the value and benefits of EnCs, but its effective integration remains uneven and incomplete. While individual aspects of flexibility in EnCs are well studied, their combined interactions (particularly between internal and external incentives, technological solutions, and user behaviour) are still insufficiently researched. Key gaps persist in the areas of benefit-sharing mechanisms, holistic load modelling, and data availability. Addressing these gaps will be essential to unlock the full synergy between demand flexibility and EnCs and to support their scalable, reliable, and user-accepted deployment within future energy systems.