Towards Agentic Virtual Power Plants for Grid-Interactive Energy Communities: A Review-Informed Reference Architecture for Operational Flexibility Intelligence

Abstract

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation.

1. Introduction

Electricity systems are becoming more distributed, renewable, digitalised, and demand-responsive. Photovoltaics, storage, electric vehicles, heat pumps, smart buildings, and controllable demand create substantial flexibility at the grid edge [1,2]. That flexibility can support collective self-consumption, congestion management, balancing, ancillary services, and resilience [3,4], but only when heterogeneous resources can be observed, coordinated, activated, verified, and governed within their technical and social constraints.

Energy communities make this coordination problem explicitly socio-technical. They combine local renewable production with collective participation, shared value creation, affordability, and democratic governance [5,6]. Virtual power plants provide the complementary operational mechanism for aggregating distributed resources and exposing them to markets and grid services [7,8]. A community VPP must therefore coordinate assets that remain attached to owners, users, comfort requirements, mobility needs, participation rights, resilience priorities, and benefit-sharing rules.

Established AI methods already provide strong component capabilities. Forecasting supports demand, renewable-generation, price, and flexibility estimation [9,10,11]; optimisation supports scheduling and bidding [12,13], and reinforcement learning supports adaptive decisions under uncertainty [14,15]. VPP research nevertheless remains concentrated on forecasting, optimisation, control, bidding, and uncertainty management [8,16], while community governance, resilience readiness, and accountable autonomous action are less consistently integrated [17]. The unresolved problem is therefore not the absence of intelligent algorithms, but the coordination of specialised analytical and control functions across a complete operational workflow.

Distributed coordination itself is not new. Classical multi-agent systems have supported decentralised representation, negotiation, and control in power engineering for two decades [18,19]. Agentic AI is relevant only where the operational problem extends beyond such predefined agent roles and protocols. Contemporary agentic systems add goal-directed planning, contextual interpretation, tool use, workflow execution, multi-agent coordination, and human-supervised action [20,21,22,23,24]. In the community VPP considered here, these capabilities are proposed as an orchestration layer around established forecasting, optimisation, digital-twin, market, and deterministic control services, not as a replacement for them.

Operational flexibility intelligence names the integrating capability addressed by this study. It is defined here as the ability of a community VPP to transform potential distributed flexibility into deployable action by discovering and forecasting resource availability, selecting and coordinating authorised actions, negotiating or committing services, monitoring execution, verifying delivery, and explaining outcomes under market, grid, community, resilience, regulatory, and trust constraints. The concept therefore differs from flexibility assessment or optimisation alone because it includes the operational and governance conditions that determine whether technically available flexibility may actually be used.

Existing reviews address VPP markets, AI applications, automated VPP decision-making, energy-community governance, digital twins, resilience, and AI regulation largely as separate bodies of knowledge [8,17,25,26,27,28,29,30,31]. The contribution of this study is to integrate those evidence streams around three questions that are central to future community VPP operation: what existing AI and multi-agent approaches already provide; which additional orchestration capabilities are genuinely agentic, and how those capabilities can be bounded by community governance, cybersecurity, resilience priorities, and human authority. The evidence synthesis is then used to derive the operational flexibility intelligence concept, a conceptual reference architecture, and a validation-oriented research agenda.

Figure 1 serves as a conceptual roadmap for the article. It positions the community VPP between distributed resources and community governance on the one hand, and market, grid, and resilience operation on the other, with agentic AI treated as a governed orchestration mechanism rather than an independent control technology.

Figure 1. Conceptual roadmap for agentic-AI-enabled community VPPs, linking distributed resources and community governance to market participation and resilience under human-in-command oversight.

The roadmap emphasises the dual use of the same finite flexibility portfolio. Community VPPs can provide demand response, balancing, congestion management, local trading, and value stacking [3,32,33], while also supporting readiness, emergency flexibility, recovery, and post-event learning [34,35]. The resulting allocation problem makes fairness, reserve policies, secure interfaces, auditability, and accountable human control part of operational performance rather than external considerations.

Section 2 describes the PRISMA-ScR-informed framework development methodology. Section 3 and Section 4 establish the physical and socio-technical conditions that determine deployable community flexibility. Section 5 and Section 6 develop the community VPP concept and its market, grid, and resilience roles. Section 7 critically compares current AI, reinforcement-learning, multi-agent, and digital-twin approaches, and Section 8 identifies the additional capabilities required for bounded agentic orchestration. Section 9 presents the conceptual reference architecture and its implementation and validation boundaries. Section 10 develops the research agenda before Section 11 concludes the article.

2. Methodology

2.1. Research Design

This study used a PRISMA-ScR-informed framework development design to construct a conceptual reference architecture for agentic-AI-enabled community VPPs and operational flexibility intelligence. The literature component served as a focused evidence-identification and synthesis mechanism for framework development rather than as an exhaustive standalone scoping review of the entire field. Scoping review principles were appropriate for this evidence component because the topic spans energy systems, electrical engineering, computer science, smart buildings, artificial intelligence, energy-community governance, cybersecurity, and electricity-market regulation, while the relevant literature is conceptually broad, methodologically heterogeneous, and distributed across research fields that use different terminologies and analytical traditions [36,37].

PRISMA-ScR reporting principles were used selectively to make the evidence-identification and selection processes transparent [38]. The framework development process comprised definition of the study scope and research questions, selection of information sources, development of the search strategy, application of eligibility criteria, study selection, structured evidence extraction, thematic analysis, thematic synthesis, design-requirement derivation, reference-architecture construction, and definitions of the evaluation and validation boundaries. The evidence-selection process is reported through a PRISMA-ScR-informed flow diagram structured according to the PRISMA 2020 statement [39]. PRISMA-ScR therefore supports evidence selection and reporting in this study; it does not define the overall study as a standalone scoping review.

2.2. Study Scope and Research Questions

The study focused on the intersection between community virtual power plants, distributed flexibility, energy-community governance, flexibility markets, resilience operation, and artificial intelligence. Community VPPs were understood as governed aggregation systems that coordinate distributed energy resources within or on behalf of energy communities. Their functions may include collective self-consumption, local energy optimisation, flexibility-market participation, grid-service provision, resilience preparation, event response, recovery support, and benefit allocation.

Agentic AI was understood as an orchestration capability that can interpret operational context, plan actions, use digital tools, coordinate specialised models or agents, monitor execution, manage deviations, generate explanations, and operate within defined human authority limits. This definition distinguishes agentic orchestration from conventional AI applications that perform individual analytical or control functions.

The study was guided by six research questions:

• RQ1. Which distributed energy resources, smart-building systems, and controllable demand assets constitute community VPP portfolios, and what technical, behavioural, and operational constraints shape their contributions to flexibility and resilience?
• RQ2. How do energy-community objectives and governance requirements, including participation, autonomy, fairness, comfort, privacy, local value creation, and benefit sharing, shape the operational design of community VPPs?
• RQ3. Which VPP functions, flexibility-market mechanisms, grid services, and resilience functions are addressed in the literature, and how do they enable or constrain community participation and dual market and resilience operation?
• RQ4. How are conventional AI methods, multi-agent systems, digital twins, and emerging agentic AI approaches applied to distributed flexibility coordination, and what limitations motivate the transition towards agentic orchestration?
• RQ5. Which architectural, interoperability, governance, cybersecurity, regulatory, human-oversight, and validation requirements are identified for trustworthy agentic-AI-enabled community VPPs?
• RQ6. Which research gaps and future development priorities remain for operational flexibility intelligence as an integrated capability for market participation, resilience orchestration, and community-governed operation?

2.3. Databases and Source Selection

The scientific literature search was conducted on Scopus, Web of Science, and IEEE Xplore. These databases were selected to provide complementary coverage of energy systems, electrical engineering, computer science, artificial intelligence, distributed control, smart buildings, and socio-technical energy research.

Authoritative legal, regulatory, institutional, and standardisation documents were used as contextual sources where they established formal definitions, obligations, market conditions, cybersecurity requirements, interoperability principles, data-governance requirements, or AI-governance conditions that could not be derived adequately from the peer-reviewed literature. These documents were recorded separately from the peer-reviewed evidence base and were used to interpret formal requirements rather than as empirical evidence of VPP performance.

2.4. Search Strategy

The search strategy combined a primary interdisciplinary database search with a supplementary search for emerging agentic AI research. The primary search was designed to identify the literature connecting virtual power plants, distributed resource aggregation, energy communities, distributed flexibility, flexibility markets, resilience, and established or emerging AI applications.

The primary master search expression was:

• (“virtual power plant*” OR “energy communit*” OR “distributed energy resource aggregation” OR “DER aggregation” OR prosumer* OR “collective self-consumption”) AND (flexib* OR “demand response” OR “flexibility market*” OR “grid service*” OR “ancillary service*” OR “congestion management” OR resilien*) AND (“artificial intelligence” OR “machine learning” OR “reinforcement learning” OR “multi-agent system*” OR “digital twin*” OR “agentic AI” OR “AI agent*” OR “LLM agent*” OR “large language model agent*”)

Governance was treated as a cross-cutting analytical dimension rather than as a mandatory search block because relevant studies may address participation, fairness, privacy, autonomy, comfort, benefit sharing, or human oversight without using explicit governance terminology in their titles, abstracts, or keywords.

A supplementary search was conducted to identify agentic AI research that was not consistently represented through established VPP and energy-community terminologies:

• (“virtual power plant*” OR “energy system*” OR “power system*” OR “smart grid*” OR “microgrid*” OR “distributed energy” OR “building energy management”) AND (“agentic AI” OR “AI agent*” OR “LLM agent*” OR “large language model agent*” OR “tool-using agent*” OR “agentic workflow*” OR “workflow orchestration”)

The supplementary search covered agentic planning, tool use, workflow execution, coordinated AI agents, and autonomous decision support in energy-system contexts. The Boolean concept structure was kept constant across databases; only the field wrappers, phrase-searching functions, truncation rules, and syntax were adapted to the capabilities of each database. Searches were applied to titles, abstracts, keywords, and equivalent indexed metadata fields where available.

No lower publication-year limit was imposed because foundational research on virtual power plants, energy communities, flexibility markets, distributed control, and multi-agent power systems predates contemporary agentic AI terminology. Searches were restricted to English-language publications.

Backward reference searching was conducted from highly relevant reviews and primary studies. Forward citation searching was used to identify later studies that developed or applied foundational contributions. These supplementary procedures strengthened coverage of emerging terminology and research located at the boundaries between energy systems, smart buildings, digital infrastructure, community governance, and agentic AI.

The database and supplementary searches were completed on 3 June 2026.

2.5. Eligibility Criteria

Peer-reviewed journal articles, peer-reviewed conference papers, systematic reviews, scoping reviews, and technical reviews were eligible for inclusion.

Studies retrieved through the primary search were eligible when they provided substantive evidence or analysis concerning one or more of the following areas: community VPPs, distributed resource aggregation, energy-community operation, distributed flexibility, flexibility markets, grid services, resilience, community governance, AI-enabled coordination, or the digital infrastructure required to integrate these functions. Studies were not required to address every research question, but their relevance to at least one analytical dimension of the study had to be explicit.

Studies retrieved through the supplementary agentic AI search were eligible when they examined agentic or tool-using AI in an energy-system context. Foundational agentic AI studies developed outside the energy domain were included only when their concepts or methods were directly applicable to the planning, orchestration, verification, safety, explainability, or governance requirements of community VPPs.

Studies were excluded when they focused exclusively on centralised power-system operation without relevance to distributed resources, aggregation, or energy communities. Forecasting, optimisation, control, or smart-building studies were excluded when they did not address distributed flexibility, grid interaction, aggregation, or community operation. General AI studies were excluded when they provided no direct architectural, operational, safety, explainability, or governance relevance to the framework development problem. Publications were also excluded when the full text was unavailable, the available information was insufficient for reliable assessment, or the publication lacked substantive analytical, empirical, technical, methodological, or regulatory content.

2.6. Screening and Selection Process

Records retrieved from the primary and supplementary searches were imported into EndNote, which was used to identify and remove duplicate records. DOI, title, author information, publication year, and database metadata were used to resolve duplicate cases. The remaining records underwent title and abstract screening followed by full-text assessment.

Both authors jointly conducted the title and abstract screening and the full-text assessment. Eligibility decisions were made against the predefined inclusion and exclusion criteria. Uncertain cases were discussed until agreement was reached.

Title and abstract screening removed publications that fell outside the scope of community VPPs, distributed flexibility, energy-community operation, flexibility markets, resilience, AI-enabled coordination, or trustworthy deployment. Full-text assessment determined whether each publication provided sufficient evidence or conceptual contribution to address at least one research question.

Full-text exclusions were based on lack of substantive relevance to the study scope, absence of distributed or community-level applicability, lack of connection to flexibility, aggregation, grid interaction, or community operation, generic treatment of AI without relevant operational or governance contribution, or insufficient methodological or technical content. Figure 2 reports the numbers of records identified, duplicates removed, records screened, full texts assessed, exclusions with reasons, and studies included in the final synthesis.

Figure 2. PRISMA-ScR-informed evidence-selection flow diagram.

2.7. Analytical Synthesis and Framework Derivation

Relevant information was extracted from the included sources concerning the study context, distributed resources, community objectives, VPP functions, market and resilience roles, AI methods, agentic capabilities, governance considerations, digital infrastructure, validation approaches, principal findings, and reported limitations. These categories provided the structured evidence base for the subsequent analysis and framework derivation.

The extracted evidence was first organised through thematic analysis across the analytical dimensions defined by the research questions. Recurring concepts, mechanisms, constraints, governance requirements, architectural patterns, validation approaches, and reported limitations were compared across sources. This stage organised the evidence without treating the eventual architecture components as predetermined categories.

Thematic synthesis then integrated the analysed patterns into higher-order findings across the heterogeneous evidence base. Statistical aggregation was not appropriate because the studies addressed different resources, objectives, market conditions, system boundaries, geographical settings, and validation environments. The synthesis followed the research questions and progressed from distributed resource potential and community governance to VPP aggregation, flexibility markets, current AI applications, agentic capabilities, and architectural requirements. Attention was also given to whether reported capabilities were conceptual proposals, simulation or digital-twin experiments, laboratory or hardware-based tests, or operational demonstrations.

Because the evidence base was used to derive a conceptual framework rather than to estimate intervention effects, a single methodological quality score was not applied across the heterogeneous study types. Instead, source evidence was interpreted according to its epistemic role and validation maturity when calibrating synthesis findings and design claims.

The synthesised findings were translated into design requirements for operational flexibility intelligence and the proposed reference architecture. The derivation distinguished what individual sources reported, what the cross-source synthesis identified, and what the authors proposed as a design response. The resulting architecture should therefore be understood as a conceptual artefact developed through review-informed design reasoning rather than as an empirically validated operational model.

2.8. Methodological Limitations

The evidence-selection and framework development process has several methodological limitations. Terminology varies substantially across the relevant research fields. Agentic AI is a recent term, while earlier studies use concepts such as intelligent agents, multi-agent systems, autonomous coordination, distributed decision support, and agent-based control. The supplementary search reduced the risk of omitting relevant research, but it could not eliminate the possibility that applicable studies used terminology not represented in the search expressions. Some foundational agentic AI studies also originated outside the energy domain, and their relevance required interpretive transfer to energy-specific operational, safety, resilience, and governance conditions.

The database search was limited to Scopus, Web of Science, and IEEE Xplore and included only English-language publications. These databases provide complementary coverage of the principal research domains, but relevant publications indexed elsewhere or published in other languages may have been omitted.

The included studies vary considerably in purpose, methodology, system boundary, resource portfolio, market assumptions, regulatory context, and validation setting. These differences restrict direct comparison and prevent statistical aggregation. Empirical evidence on integrated agentic community VPPs also remains limited because many relevant contributions address individual capabilities through conceptual models, simulation, digital twins, or laboratory environments. Evidence from living laboratories, flexibility markets, regulatory sandboxes, resilience exercises, and longitudinal field deployments remains comparatively scarce.

Definitions of energy communities, aggregator roles, market-access conditions, cybersecurity obligations, data-governance requirements, and liability arrangements differ across jurisdictions. The architectural and governance implications identified through the synthesis must therefore be interpreted in relation to the regulatory and institutional setting in which a community VPP is developed and operated.

3. Distributed Energy Resources and Controllable Loads as the Physical Foundation of Local Energy Communities

3.1. Local Renewable Electricity Generation

Local renewable generation provides the production basis for community VPP operation. Photovoltaics, local wind where appropriate, and shared renewable assets support collective self-consumption, energy sharing, storage charging, and export [5,40]. Their operational significance arises from variability: production and demand rarely coincide, so value depends on whether generation can be forecast and coordinated with storage, flexible demand, export, curtailment, or power-to-heat conversion [9,10,41]. The relevant question for the VPP is therefore not simply how much renewable capacity is installed, but how its time-varying output changes the flexibility that can be committed for local, market, grid, or resilience purposes.

3.2. Distributed Storage and Temporal Flexibility

Electrical and thermal storage convert temporal mismatches into controllable flexibility. Batteries, community storage, behind-the-metre storage, hot-water tanks, building thermal mass, and related resources can increase self-consumption, reduce peaks, absorb renewable surplus, and support external services [2,42,43,44]. Batteries provide fast measurable response, while reconfigurable and community-scale storage designs broaden the range of controllable configurations [40,42,45,46], and thermal storage couples electricity decisions to comfort and heat demand [2,41]. Storage is nevertheless not a neutral reserve: charging for later local use may conflict with market export; external activation may accelerate degradation, and market dispatch may reduce resilience reserve [47,48]. Community VPP control must therefore treat storage availability as purpose-dependent rather than equating state of charge with freely deployable capacity.

3.3. Electric Vehicles and Charging Infrastructure

Electric vehicles combine substantial battery capacity with mobility constraints. Smart charging can align demand with local renewable production, avoid distribution peaks, and provide demand response [49,50,51,52,53,54,55], while bidirectional charging can extend this role to backup and grid services [56]. The flexibility window is determined by connection status, departure time, minimum state of charge, charging location, warranty conditions, and user willingness to participate. EVs must therefore be represented as mobility-constrained resources rather than stationary batteries [57]. This distinction is important because an apparently available battery can become unavailable immediately when the mobility requirement changes.

3.4. Heat Pumps, Electrified Heating, and Sector Coupling

Heat pumps and electrified heating provide flexibility through thermal storage and building inertia rather than direct electrical storage. Their operation can be shifted to absorb renewable electricity or reduce peaks [41,58,59,60,61,62], and digitalisation increasingly connects this flexibility to wider heating-system operation [63]. Available response is constrained by outdoor conditions, building physics, indoor comfort, hot-water needs, and occupant preferences [58,64]. Heating flexibility can also support resilience through preheating or temporary demand reduction [65]. The community VPP must therefore distinguish theoretical heat-pump flexibility from the smaller volume that is technically controllable and socially acceptable.

3.5. Smart Buildings, Controllable Demand, and Flexible Loads

Smart buildings expose otherwise fragmented loads through metres, sensors, BEMS platforms, and automation interfaces. Residential, commercial, public, and light-industrial loads can provide flexibility when operation can be shifted or modulated without violating safety, service, comfort, or process requirements [3,66,67,68,69,70]. Building-level digital models and automation can make HVAC and other loads accessible to VPP coordination [71,72], while urban buildings can contribute flexibility at system level when resource states and constraints are represented consistently [73]. The operational value of demand flexibility therefore depends on observability, controllability, user acceptance, and the ability to measure and verify response [66,74,75,76,77].

3.6. Resource Heterogeneity and Aggregation Requirements

The community portfolio is heterogeneous in response speed, duration, predictability, ownership, controllability, market suitability, and social sensitivity [1,2]. Aggregation can increase capacity and reliability and reduce the transaction burden faced by small resources [78,79,80], but it does not remove resource-specific constraints. Flexible loads, for example, must be represented with the operating boundaries that determine whether they can be offered as dependable capacity [81]. Conflicts also arise between self-consumption, market commitments, battery preservation, mobility, comfort, fairness, and resilience reserve [82]. The physical foundation of a community VPP is therefore a dynamic portfolio of conditional capabilities rather than a simple sum of installed assets.

4. Energy Communities as Socio-Technical Flexibility Environments

4.1. Energy Communities, Prosumers, and Collective Self-Consumption

Energy communities translate distributed resources into collective socio-technical arrangements shaped by participation, ownership, governance, and local benefit creation [83,84,85,86,87]. Prosumers combine consumption with production, storage, sharing, or sale of energy [83], while community coordination turns individual self-consumption into a collective balancing problem involving shared assets, network constraints, and agreed allocation rules [88,89,90]. Flexibility in this setting is therefore not only a marketable technical service; it is a collectively governed capability whose activation can distribute costs, benefits, inconvenience, and control unevenly across members.

4.2. Local Ownership, Participation, and Democratic Governance

European energy-community models emphasise open participation, member control, and environmental, economic, or social community benefits rather than purely financial returns [91,92,93]. Participation and perceived local value influence acceptance of automated energy coordination [94,95,96,97], while demand-response projects show that technological capability alone does not ensure sustained participation [98]. Community governance consequently becomes operational: consent, opt-out and override rights, comfort limits, participation levels, and benefit-allocation rules determine which resources can be coordinated and under which conditions.

4.3. Community Objectives Beyond Economic Optimisation

Community objectives extend beyond cost minimisation and market revenue. Documented motivations include affordability, collective self-consumption, social inclusion, energy justice, environmental benefit, local development, and democratic participation [6,99,100,101,102,103]. Operational decisions may therefore favour local renewable use or community-defined objectives even when another action is economically superior [101,104]. Fair value allocation is particularly important because asset ownership and flexibility potential are unevenly distributed [100,105,106]. Resilience adds another non-market objective because the same battery or controllable load may be more valuable as retained reserve than as immediately monetised flexibility [34,35].

4.4. Fairness, Autonomy, Comfort, and Local Value Creation

Fairness, autonomy, and comfort define practical boundaries on technically feasible control. Smart-grid participation can distribute burdens unevenly across social groups and asset owners [107,108], while members may reject repeated interventions that compromise mobility, comfort, privacy, or control over daily routines [109]. Building flexibility is similarly constrained by indoor environmental quality and user acceptance [110,111]. Local value therefore includes not only flexibility revenue but also reduced grid stress, lower collective costs, renewable utilisation, shared infrastructure, and sustained participation [85,86]. A community VPP must make these conditions part of resource selection rather than treating them as external ethical considerations.

4.5. The Operational Challenge of Coordinating Community Flexibility

Community flexibility changes continuously with weather, occupancy, mobility, device state, participation preferences, market conditions, and grid constraints. Multi-agent and optimisation studies show the need to coordinate these distributed conditions rather than optimise isolated devices [112,113]. External flexibility services additionally require standardised products, activation rules, metering, verification, and settlement [114,115]. The resulting control problem is therefore a continuous reconciliation of technical feasibility, community legitimacy, market obligations, and resilience priorities. This socio-technical definition of deployable flexibility provides the basis for the community VPP concept developed next.

5. Virtual Power Plants and the Community VPP Concept

5.1. Virtual Power Plants as Distributed Resource Aggregation Systems

Virtual power plants aggregate distributed resources into coordinated portfolios that can participate in markets and provide grid services [7,116,117,118]. Their capacity is virtual because it emerges from the changing availability of physically distributed generation, storage, EVs, buildings, and flexible loads rather than from a single plant. Portfolio diversity can improve reliability and service capability [119], while the VPP platform provides the forecasting, coordination, market, and control functions needed to transform that diversity into an operational resource [120]. In energy communities, however, the same aggregation must remain compatible with member rights, local objectives, and resilience priorities.

5.2. Core VPP Functions: Forecasting, Scheduling, Bidding, Dispatch, Verification, and Settlement

VPP operation forms a coupled pipeline from forecasting through scheduling, bidding, dispatch, verification, and settlement [121]. Forecast uncertainty affects scheduling and market commitments [122], while optimisation and learning methods increasingly support coordination of community resources [123] and digital twins provide a controlled environment for testing decisions and operating strategies [25,124]. The limitation is not that these functions are absent, but that they are frequently optimised as separate components. Community operation additionally requires preference alignment, authorisation, exception handling, explanation, and accountability across the complete workflow.

5.3. Community VPPs in Relation to Microgrids, DERMS, Aggregators, and Transactive Energy Platforms

Community VPPs overlap with several established concepts but have a different organising logic. Microgrids coordinate resources within identifiable electrical boundaries and may support islanded operation [125,126,127]. DERMS platforms are primarily utility-oriented systems for visibility and control of distributed resources [128], while aggregators pool resources for market or grid-service participation [78,129,130]. Transactive energy platforms coordinate exchange through prices and distributed transactions [131,132]. System-of-systems approaches such as SGAM demonstrate how these functions can be integrated architecturally [133]. A community VPP can incorporate elements of all four, but its distinctive requirement is that aggregation and external participation remain governed by community objectives and rights.

5.4. Community Virtual Power Plants as Governed Aggregation Systems

A community VPP is therefore defined here as a governed aggregation system that coordinates distributed resources within or on behalf of an energy community and enables local optimisation, market participation, grid services, and resilience operation [134,135,136]. Economic optimisation remains important, but it is embedded within broader community purposes [5,6]. Resource availability is consequently conditional: a technically controllable asset may still be unavailable because of comfort limits, ownership rules, reserve requirements, or participation conditions [2,64]. Transparency, consent, explanation, and contestability are required because technically efficient actions can lose legitimacy when members cannot understand or challenge them [96,107].

5.5. Community VPPs as Market Participation and Resilience Infrastructures

Community VPPs allocate a finite resource portfolio across internal balancing, external services, and resilience. Local coordination can increase self-consumption and improve use of shared assets [40,44], while aggregated flexibility can provide market and grid services [137] and can incorporate risk-aware bidding strategies where delivery exposure matters [138]. Value stacking improves economic opportunity but also creates conflicts, double commitments, and delivery risk [48,139]. The same behind-the-metre assets may be strategically valuable during system stress even when they are outside direct utility control [34,79]. Market participation and resilience must therefore be treated as competing uses of the same conditional flexibility rather than independent capabilities.

5.6. Operational Flexibility Intelligence as an Integrating Capability

As defined in Section 1, operational flexibility intelligence is used here as the integrating concept that connects existing component capabilities. Its distinct role is clarified by comparison with neighbouring concepts. Flexibility assessment and forecasting estimate what response may be available [64,140]; VPP platforms aggregate, schedule, and dispatch resources [120]; DERMS provides distribution-oriented monitoring and control [128]; architectural frameworks support interoperable system integration [133]. These capabilities are necessary, but none by itself determines whether a particular action is simultaneously technically feasible, authorised, market-compatible, resilience-appropriate, socially acceptable, verifiable, and explainable.

The comparison shows why none of these neighbouring concepts are equivalent to operational flexibility intelligence. The integrating concept requires resource state and forecast information to be evaluated together with user permissions, comfort boundaries, market rules, grid constraints, community priorities, resilience reserve, verification requirements, and human authority. Operational flexibility intelligence therefore defines the integration problem that must be satisfied across analytical, control, governance, market, grid, and resilience functions without becoming a synonym for any individual component.

6. Flexibility Markets and Grid Services for Energy Communities

6.1. Collective Self-Consumption and Internal Community Balancing

Collective self-consumption is the first value domain for community flexibility. Coordinated batteries, flexible loads, EV charging, and electrified heating can align demand with local renewable production [40,141,142,143] and reduce reliance on external supply [44]. The benefits depend on allocation rules because locally generated electricity and flexibility must be distributed among members and shared assets [144,145]. Static allocation can also become unfair as resource states and member participation change over time [105]. Internal balancing therefore combines technical optimisation with transparent allocation of local value.

6.2. Demand Response, Local Flexibility Markets, and DSO Congestion Management

Demand response makes consumption an operational resource [3,66] and can be strengthened by dynamic tariffs and other grid-aware signals [146]. Aggregated buildings, heat pumps, EVs, batteries, and controllable loads can provide measurable response [2,3], but practical participation depends on baselines, metering, activation rules, verification, and settlement [147]. Local flexibility markets add spatial specificity by allowing DSOs to procure response where congestion occurs [32,114,115,148,149,150]. Community portfolios can contribute location-specific flexibility [151,152], provided the VPP can reconcile grid requests with local generation, comfort, mobility, reserve, and participation constraints [32,153].

6.3. Balancing Markets, Ancillary Services, and External Grid Services

Balancing and ancillary services impose stricter requirements on response speed, prequalification, metering, communication, and delivery reliability [74,139,153,154,155,156]. Community participation therefore normally requires aggregation [157] and resource portfolios whose upward and downward response can be committed with sufficient confidence [158,159]. Forecast-based and multi-market approaches help manage delivery risk [160,161,162], while verification links actual response to settlement and future participation [163,164]. These requirements make external services a governance problem as well as a market problem because member-owned resources can be exposed to penalties, degradation, or inconvenience through commitments made on their behalf.

6.4. Peer-to-Peer Trading, Local Energy Markets, and Value Stacking

Peer-to-peer and community markets support local exchange among prosumers [33,165,166,167,168,169] and can use local pricing or trading mechanisms to allocate renewable energy and flexibility [170,171,172]. Local-market designs also include coalitional and agent-based coordination approaches [173,174,175], while strategic and learning-based trading agents illustrate increasingly automated local participation [176,177,178]. Digital platforms, including distributed-ledger approaches, can support transaction recording and settlement [179,180,181,182,183], but local trading still requires physical feasibility, fairness, grid compatibility, and protection against market power [184,185,186,187]. Value stacking extends the same portfolio across self-consumption, congestion management, balancing, price arbitrage, and resilience [48,188,189]. Because services can overlap or compete [82], the central operational problem is a continuous allocation of finite flexibility rather than the independent optimisation of each revenue stream.

6.5. Barriers to Flexibility Market Access for Energy Communities

Market access remains constrained by technical, market, regulatory, and governance barriers [5,17,30,190]. Aggregation depends on reliable communication, secure digital platforms, and coordination across organisational boundaries [191,192], while interoperability limitations can leave otherwise flexible assets inaccessible [193]. Metering granularity and data quality constrain baselines and verification [194], and national rules can create substantial administrative and commercial barriers [195,196]. Member trust is equally important because opaque activation or unfair benefit allocation can undermine participation even when the technical platform functions correctly [107,110].

6.6. Resilience as a Non-Market Flexibility Value Domain

Resilience is a distinct value domain because valuable resource use is not always represented by a market transaction. Distributed resources can support preparedness, degraded operation, critical functions, recovery, and post-event learning during physical or cyber disruption [35,197,198,199]. Preserving storage, thermal capacity, mobility, or flexible demand for uncertain events can therefore conflict with short-term market value [35,200], while domestic heating portfolios illustrate that present activation can alter future response capability [65]. Resilience-oriented operation requires explicit reserve and priority rules and event-aware coordination [4,197]. The synthesis identifies this market-versus-resilience allocation problem conceptually; it does not provide a quantitatively validated objective function for resolving the trade-off.

7. Current AI Applications in VPPs and Energy Communities

7.1. AI for Load, Renewable Generation, Price, and Flexibility Forecasting

Forecasting is one of the most mature AI functions in VPP research. Machine-learning and statistical methods estimate load, renewable generation, prices, and flexibility availability [9,10,140,201,202]. Community-scale demand remains difficult because smaller portfolios are sensitive to occupancy, mobility, weather, and intervention effects [9,194,203,204,205,206], while renewable and price forecasts remain subject to exogenous uncertainty [10,11,207,208,209]. Flexibility forecasting adds technical, behavioural, comfort, and contractual constraints to the prediction problem [64,210,211,212]. These methods improve visibility but do not decide how predicted flexibility should be allocated among community, market, grid, and resilience objectives.

7.2. AI-Based Optimisation for Scheduling, Bidding, and Dispatch

Optimisation translates forecasts into schedules, bids, reserve allocations, and dispatch plans [12,116,213]. Stochastic, robust, predictive-control, and related methods coordinate resources under uncertainty [122,213,214,215,216,217,218,219,220,221,222] and support market bidding across different products [13,217,223,224,225]. The principal limitation for community VPPs is objective and constraint-completeness. A mathematically optimal plan can remain unacceptable if it ignores autonomy, repeated burden, comfort, asset degradation, resilience reserve, or benefit allocation. Optimisation is therefore a necessary decision tool, but its outputs require governance-aware orchestration before they become authorised actions.

7.3. Reinforcement Learning for Adaptive Control and Market Participation

Reinforcement learning addresses sequential decision problems and has been applied to batteries, EVs, buildings, demand response, bidding, and VPP control [14,15,226,227,228,229,230,231,232,233,234]. Multi-agent reinforcement learning extends this capability to distributed portfolios [235,236,237,238,239,240] and multi-market or community coordination [15,241,242,243,244,245,246]. Its strength is adaptation under uncertain dynamics, but exploration, reward specification, explainability, and transfer from simulation to physical operation create safety and governance concerns [14,227]. Safe RL approaches illustrate how adaptive policies can be constrained [187,247], but they do not by themselves provide the complete workflow, authority, and accountability structure required for community VPP operation.

7.4. Multi-Agent Systems and the Boundary to Agentic AI

Classical multi-agent systems are the direct conceptual predecessor of distributed agentic VPP coordination. They represent resources, buildings, prosumers, markets, or grid actors as autonomous or semi-autonomous agents and have long supported scheduling, demand response, microgrid control, and distributed energy management [18,248,249,250,251,252,253,254]. Recent work extends this logic through learning-based coordination across multiple VPPs and uncertain communication environments [255,256,257]. MAS therefore already provides distributed representation, negotiation, local autonomy, and coordinated decision-making; these capabilities must not be relabelled as agentic AI merely because contemporary terminology has changed [18,235].

The additional agentic role proposed in this study concerns functions that conventional MAS commonly treats as fixed within the design: interpretation of heterogeneous operational context, multi-step planning across changing workflows, governed selection and use of external tools, dynamic exception handling, evidence-grounded explanation, and explicit escalation under delegated human authority. Contemporary agentic AI research provides relevant foundations for tool-using and orchestrating systems [20,21,258,259,260,261,262,263], while emerging energy applications illustrate both opportunities and risks [264,265,266]. Table 1 therefore distinguishes the proposed agentic orchestration role from conventional VPP/DERMS functions and from classical MAS/MARL. The distinction is functional rather than chronological: where a conventional controller or MAS can solve the task safely and transparently, an agentic layer is not architecturally necessary. This distinction does not imply that these capabilities are theoretically impossible to implement within an advanced MAS; rather, agentic AI denotes their explicit integration into runtime, context-sensitive, tool-using, and human-governed orchestration.

Table 1. Functional distinction between conventional VPP/DERMS, classical MAS/MARL, and the agentic orchestration role proposed for community VPPs.

Dimension	Conventional VPP/DERMS	Classical MAS/MARL	Agentic Orchestration Proposed Here
Primary role	Portfolio monitoring, optimisation, scheduling and control	Distributed representation and coordinated decision-making among predefined agents	Cross-domain orchestration of existing models, services, agents and approval mechanisms
Workflow structure	Primarily predefined control and market workflows	Predefined agent roles, protocols, policies or learned coordination structures	Multi-step workflows that can be dynamically sequenced within bounded operating policies
Tool and model use	Fixed integration of configured models and control functions	Agent-specific algorithms and communication functions	Governed selection and invocation of forecasts, solvers, digital twins, registries and external services
Context interpretation	Structured operational states and configured rules	State and messages represented in agent models	Heterogeneous signals, rules, preferences and event context translated into operational constraints
Exceptions and deviations	Deterministic re-optimisation, alarms and fallback	Negotiation, policy adaptation or relearning within the agent design	Detect, diagnose, replan, substitute resources, preserve provenance and escalate when authority is exceeded
Human interaction	Operator supervision and configured set-points	Usually explicit supervisory or agent-interface mechanisms	Role-specific explanation, approval, contestability and delegated authority
Physical actuation	Through established EMS/DERMS/device control paths	Through defined agent-to-controller protocols	Must remain mediated by governed tools/APIs and validated local controllers rather than direct unrestricted agent control
Appropriate use	Mature, structured and predictable operational tasks	Distributed coordination where objectives and protocols can be specified	Workflows requiring changing context, tool use, cross-domain reasoning, exceptions and explicit human governance

7.5. Digital Twins and Simulation-Based AI Validation

Digital twins and simulation provide the principal environment in which AI-enabled VPP decisions can be tested without exposing physical assets, market commitments, or occupants to unvalidated behaviour [25,124,267,268]. VPP and power-system co-simulation supports comparison of schedules, markets, and control strategies [8,269,270], while operationally connected twins can represent buildings, DERs, networks, and community conditions [271,272,273,274,275,276,277]. Their limitation is representational: social behaviour, overrides, governance, cybersecurity, and data-quality failures are difficult to reproduce completely [26]. Digital twins are therefore enabling infrastructure for safe experimentation and validation, not evidence that the complete agentic architecture has already been demonstrated [268,269].

7.6. Critical Synthesis: What Current AI Does and Does Not Provide

The reviewed literature establishes a mature component foundation for intelligent community VPPs, including forecasting, optimisation, adaptive control, distributed coordination, and simulation-based validation [27,278,279,280,281]. These capabilities remain fragmented across specialised tasks [26,140], and resilience is less systematically integrated than normal market operation [8,34]. Recent works on automated VPP decision-making and agentic AI indicate a movement towards higher-level orchestration [16,20,21,28,258,259,260,261,262,263,264], but the evidence does not establish integrated agentic community VPPs as a mature operational technology. The defensible transition is therefore from component intelligence to governed orchestration where the workflow requires capabilities beyond conventional optimisation, MAS, or deterministic control.

8. Agentic AI Capabilities for Operational Flexibility Intelligence

8.1. From Conventional AI and MAS to Bounded Agentic Orchestration

The comparison in Table 1 narrows the role of agentic AI. Forecasting, optimisation, deterministic control, reinforcement learning, and classical MAS should remain responsible for tasks they already perform effectively. Agentic orchestration becomes relevant when a community VPP must coordinate several such capabilities across changing context, external services, exceptions, and human decision points [20,21,258,259,260,282]. The architecture must therefore preserve the strengths of established distributed control [18,19] while adding bounded workflow agency rather than unrestricted autonomy.

That boundary is essential because community VPP decisions affect assets, comfort, mobility, privacy, financial exposure, market commitments, and resilience. Regulatory and smart-grid governance requirements reinforce the need for explicit authority, transparency, cybersecurity, and accountability [29,30]. Low-risk actions may be delegated within predefined limits, whereas conflicting, novel, or high-impact actions require escalation. Multi-agent collision risks further show why portfolio-level coordination cannot be assumed to emerge safely from independently acting agents [265,266].

8.2. Flexibility Discovery, Resource Characterisation, and Context Interpretation

Agentic orchestration begins by maintaining a current representation of deployable flexibility rather than merely installed assets. Governed access to metering, building, resource, market, and preference services can combine current state with forecasts and constraints [22,23,283,284,285]. Resource characterisation must also distinguish response speed, duration, predictability, social sensitivity, and market suitability [286,287,288]. External prices, product rules, DSO requests, emergency signals, and contractual obligations then have to be interpreted against portfolio conditions [32,148,289,290]. Product matching and spatial grid relevance determine whether an apparently attractive opportunity is actually feasible [153,291].

8.3. Planning, Offer Construction, and Multi-Actor Coordination

Where the task exceeds a fixed control sequence, agentic planning can decompose a market or resilience objective into governed steps that query forecasts, run optimisation, test candidates in a digital twin, check community rules, estimate delivery risk, and prepare an authorised action [292,293,294,295,296,297]. Market offers must still conform to explicit product requirements [115] and multi-actor coordination may use specialised agents or services to represent resources, markets, grids, or community rules [24,298,299,300]. Portfolio-level orchestration is responsible for reconciling these outputs and preventing incompatible commitments. Fairness is operationalised by tracking activation history, inconvenience, compensation, and repeated burden rather than by assuming that the cheapest resource should always be selected [301].

8.4. Scheduling, Activation, Verification, and Accountability

Scheduling and activation connect planning to physical operation across day-ahead, intraday, and real-time horizons [302]. The agentic layer should coordinate established BEMS, charger, battery, generation, and device controllers rather than bypass validated control and safety functions [247]. When devices disconnect, forecasts change, users override control, or communication fails, the orchestration function can diagnose the deviation, identify feasible substitutes, replan within authorised constraints, and escalate when necessary [303]. Verification then links measured response to baselines and market delivery [163,164], while explanation must remain grounded in actual workflow evidence [304,305]. Persistent provenance of tool calls, approvals, actions, deviations, and outcomes supports auditability [306] and keeps organisational accountability separate from machine execution [307].

8.5. Learning, Resilience Orchestration, and Human-in-Command Operation

Learning should use operational evidence to improve forecasts, resource models, delivery-risk estimates, fairness rules, and preparedness without allowing uncontrolled self-modification. Resilience-oriented agentic AI can support preparedness, event interpretation, resource prioritisation, degraded operation, recovery, and post-event analysis [308]. Delegated autonomy provides a useful model for separating routine automatic action from decisions that require explicit approval [309]. Human-in-command operation therefore requires role-specific explanations, approval interfaces, override rights, and clear organisational responsibility [310,311,312]. During major events, these mechanisms must preserve the priority of critical services and resilience rules rather than simply maximising short-term economic value [4,197].

9. Proposed Conceptual Reference Architecture, Governance, and Validation Framework for Agentic-AI-Enabled Community VPPs

The architecture proposed in this section is a conceptual reference architecture for design and validation, not an implemented or empirically validated VPP. Where Section 8 defined the capabilities that may justify an agentic layer, Section 9 now specifies how those capabilities can be embedded in a system that preserves established analytical models, distributed control, deterministic safety functions, and accountable human authority.

The novelty is integrative rather than attributable to any single layer. Existing VPP platforms already provide aggregation and control functions [120]; DERMS provides distribution-oriented coordination [128]; classical MAS provides distributed agency [18,19]; digital twins provide modelling and validation infrastructure [25,124], and system-of-systems frameworks provide architectural integration principles [133]. The proposed contribution combines these established elements with explicit tool-mediated orchestration, community-governance constraints, resilience allocation, provenance, approval gates, and human-in-command authority. No quantitative performance superiority over existing architectures is claimed because the proposed architecture has not yet been implemented as a common benchmarked system.

Figure 3 presents the resulting layered architecture. Table 2 maps the principal agentic capabilities to implementation responsibilities and concrete governing controls. Table 3 defines the corresponding evaluation boundary by requiring assessment across deployable flexibility, operational coordination, market and grid performance, community value, trustworthy agentic AI, and resilience rather than through algorithmic accuracy or revenue alone.

Figure 3. Proposed conceptual reference architecture for agentic-AI-enabled community VPPs, with agentic orchestration bounded by governed interfaces, validated control services, and human-in-command oversight.

Table 2. Mapping of agentic capabilities to architectural implementation responsibilities and concrete governing controls.

Agentic Capability	Primary Layers	Architectural Responsibility	Concrete Governing Controls
Flexibility discovery and context interpretation	Physical; data; analytical AI	Maintain a current deployable-flexibility map.	Role-based data access; consent checks; data-quality validation; stale-state rejection.
Planning and offer construction	Digital twin; analytical AI; orchestration; interfaces	Generate, test, and translate candidate commitments.	Tool allow-list; deterministic constraint checks; digital-twin testing; risk thresholds; approval gates.
Multi-actor coordination	Orchestration; governance	Reconcile preferences, community rules, market obligations, and grid requirements.	Explicit priorities; fairness and activation-history checks; conflict detection and resolution; escalation.
Scheduling, activation, and deviation management	Orchestration; interfaces; physical	Maintain workflow state and coordinate verified control.	Authenticated command paths; least privilege; pre- and post-condition checks; fallback controllers; manual override.
Verification, explanation, and settlement support	Data; orchestration; interfaces; governance	Link delivery evidence to settlement and explanations.	Provenance logging; evidence-grounded explanation; baseline and delivery checks; contestability.
Learning and resilience orchestration	Digital twin; analytical AI; orchestration; governance	Update models, strategies, preparedness, and recovery procedures.	Versioning; sandboxed updates; rollback; change approval; resilience-reserve policies.

Figure 3 does not represent a direct agent-to-device control path. Southbound execution should pass from the orchestration layer through governed tool and API adapters into established VPP, DERMS, BEMS, EMS, or device-management services, which in turn use validated local controllers to operate physical resources. The architecture therefore specifies an API-mediated integration pattern rather than a universal southbound device protocol. At this boundary, protocol-specific adapters map governed orchestration requests onto the validated interfaces already used by building automation, EV charging, inverter and storage, and DER control systems, while the agentic layer sees only authorised capabilities and abstracted state. Existing works support OpenADR-compliant demand-response interfaces [193,313], semantic interoperability through SAREF and related platform models [314,315], and architecture-level integration through SGAM [133]. Agentic interfaces can then operate over these controlled services rather than exposing unrestricted physical actuation [22,23,283,284,285].

9.1. Physical Resource Layer

The physical layer contains generation, batteries, EV chargers, heat pumps, thermal storage, smart buildings, controllable loads, and community-owned assets [1,2]. Each resource must be exposed as an operational capability with state, response envelope, availability window, ownership condition, authorisation status, market suitability, and resilience role [2,64]. Reliable sensing, actuation, local safety, and fallback remain prerequisites because no orchestration layer can compensate for missing controllability or unsafe device behaviour [316]. Physical safety therefore remains anchored in validated resource and local control functions.

9.2. Data, Sensing, and Interoperability Layer

The data and interoperability layer supplies measurements, communication, semantic context, and access-controlled services to the rest of the architecture. Relevant sources include metres, building systems, EV chargers, battery systems, weather services, grids, markets, and preference platforms [317,318]. Communication performance and data quality affect whether distributed optimisation and VPP coordination remain dependable [191,319,320], while interoperability remains a persistent barrier across heterogeneous vendors and systems [193,313]. Standardised or mapped resource descriptions are therefore needed for both technical exchange and semantic consistency [79,248,314,315]. Privacy controls must remain purpose-limited because granular energy, mobility, occupancy, and preference data can expose sensitive behaviour [321,322].

9.3. Digital-Twin and Simulation Layer

The digital-twin and simulation layer provides a controlled environment for testing schedules, offers, activation plans, resilience actions, and fallback strategies before higher-risk actions reach real assets [124,271,323,324,325,326]. Calibration and sensitivity analysis determine whether simulated behaviour remains representative [327], and benchmark environments can compare control strategies under reproducible conditions [272]. Resource-, building-, community-, and grid-level twins can be combined according to the decision being evaluated [328,329,330]. Their outputs remain conditional on model fidelity and operational data quality [124,271].

9.4. Analytical AI Layer

The analytical AI layer retains the forecasting, optimisation, estimation, anomaly detection, reinforcement-learning, and explainability functions established in Section 7 [16,331]. Anomaly detection can identify abnormal device, data, or cyber–physical behaviour [332], while federated approaches can reduce the need to centralise sensitive training data [333,334,335]. The layer should expose outputs together with uncertainty, provenance, validity conditions, and model version. The orchestration layer then decides whether and how those outputs can be used within community, market, resilience, and authority constraints.

9.5. Agentic Orchestration Layer and Governed Tool Interfaces

The agentic orchestration layer maintains workflow state and coordinates authorised tools rather than replacing the analytical or physical control layers. Agents may access forecasts, optimisation solvers, digital twins, resource registries, market services, preference systems, and audit functions only through a controlled tool registry. Authentication, authorisation, input validation, output checking, logging, and sandboxing constrain tool use [192,306]. Classical resource or market agents can remain part of this structure [18,19], but portfolio-level consistency and delegated authority must be enforced by explicit policies. Routine actions may proceed inside predefined limits, while consequential or anomalous actions are suspended or escalated [307].

9.6. Market, Grid, and Resilience Interface Layer

The external interface layer translates internal portfolio states into market, grid, and resilience interactions and converts external requests into machine-readable operational requirements. OpenADR-compliant mechanisms provide one established pattern for demand-response exchange [193,313], while SAREF and related semantic models can support consistent interpretation of energy assets and services [314,315]. SGAM provides a broader architectural reference for coordinating actors, information, communication, and component layers [133]. These interfaces should expose product rules, activation signals, baselines, metering, settlement, location requirements, and emergency priorities to the orchestration layer rather than resolving conflicts independently. Resilience interfaces additionally require robust communication and restoration-oriented information flows [336,337].

9.7. Governance, Trust, and Human-in-Command Layer

Governance operates across all layers by defining participation, consent, override, comfort, privacy, compensation, reserve, data-use, and approval rules [93]. Human oversight must correspond to actual authority and intervention mechanisms rather than symbolic review [310], while fairness controls should monitor activation history, burden, compensation, and access to participation opportunities [105,301].

Conventional digitalised electricity markets and smart grids already require architectural cybersecurity and resilience controls [338,339]. Agentic operation introduces additional failure modes beyond conventional cyber–physical control. Language-based systems can generate plausible but unsupported outputs [305]; prompt injection, manipulated inputs, unsafe tool calls, compromised external data, and service disruption can propagate through digital workflows [340,341,342,343,344,345], and independently acting agents can produce conflicting system-level actions [265,266]. The architecture therefore requires least-privilege tool access, provenance checks, deterministic validation against physical and market constraints, anomaly detection, conflict resolution, approval gates with explicit integrity checks, fallback controllers, secure logging, rollback, and manual override [192,306,307]. The aim is not to make autonomous reasoning intrinsically safe, but to make unsafe or unauthorised action difficult to execute and easy to detect, reconstruct, and stop. The threat model must also include failed, bypassed, or incorrectly interpreted human-approval mechanisms, because an approval gate that cannot reliably block unauthorised execution does not provide meaningful human-in-command control.

9.8. Framework Evaluation and Validation Requirements

The framework must be evaluated across the same technical, economic, social, governance, cybersecurity, and resilience dimensions that it integrates. Table 3 therefore defines seven evaluation dimensions and appropriate validation settings. It should be read as an evaluation structure, not as evidence that the architecture has already been validated. In particular, the table does not prescribe a quantitative weighting between market value and resilience reserve; developing and validating such a decision framework remain for future work.

Table 3. Evaluation dimensions and validation settings for agentic community VPPs.

Evaluation Dimension	Core Evaluation Question	Representative Indicators	Validation Setting
Deployable flexibility	What flexibility can be committed without violating technical, user, contractual, or resilience constraints?	Observable, controllable, authorised, socially acceptable, market-committed, and reserve capacity; response time; duration; reliability; and rebound.	Simulation, digital twin, and pilot.
Operational coordination	Can the VPP forecast, schedule, activate, and recover from deviations reliably?	Forecast error [9,11]; scheduling quality; activation success; delivery accuracy; and causes of deviation.	Digital twin, software-in-the-loop, and hardware-in-the-loop testing.
Market performance	Can local flexibility be converted into compliant economic value without weakening community priorities?	Bid success; revenue; imbalance cost; settlement accuracy; delivery risk; and value-stacking performance.	Market simulation, sandbox, and pilot.
Grid support	Does activation produce the intended system effect at the required location and time?	Peak reduction; congestion relief; voltage support; balancing contribution; hosting-capacity improvement; location accuracy; and rebound avoidance.	Network simulation, digital twin, DSO pilot, and field trial.
Community value	Are benefits, burdens, participation conditions, and local priorities acceptable to members?	Cost reduction; local renewable utilisation; comfort; fairness; satisfaction; benefit distribution; opt-outs; overrides; and participation continuity [190].	Living laboratory, pilot, survey, and longitudinal study.
Trustworthy agentic AI	Is autonomous coordination bounded, explainable, auditable, robust, privacy-preserving, and subject to meaningful human control?	Explanation quality; provenance completeness; escalation accuracy; override capability; robustness to failures and adversarial inputs; and cybersecurity performance [346].	Adversarial testing, audit exercise, software-in-the-loop, and hardware-in-the-loop testing.
Resilience	Does the VPP improve preparedness, response, recovery, and learning during system stress?	Reserve readiness; communication availability; activation speed; critical-load support; restoration time; rebound management; and post-event learning [4].	Digital twin exercise, resilience drill, pilot, and post-event assessment.

Table 3. Evaluation dimensions and validation settings for agentic community VPPs.

Evaluation Dimension	Core Evaluation Question	Representative Indicators	Validation Setting
Deployable flexibility	What flexibility can be committed without violating technical, user, contractual, or resilience constraints?	Observable, controllable, authorised, socially acceptable, market-committed, and reserve capacity; response time; duration; reliability; and rebound.	Simulation, digital twin, and pilot.
Operational coordination	Can the VPP forecast, schedule, activate, and recover from deviations reliably?	Forecast error [9,11]; scheduling quality; activation success; delivery accuracy; and causes of deviation.	Digital twin, software-in-the-loop, and hardware-in-the-loop testing.
Market performance	Can local flexibility be converted into compliant economic value without weakening community priorities?	Bid success; revenue; imbalance cost; settlement accuracy; delivery risk; and value-stacking performance.	Market simulation, sandbox, and pilot.
Grid support	Does activation produce the intended system effect at the required location and time?	Peak reduction; congestion relief; voltage support; balancing contribution; hosting-capacity improvement; location accuracy; and rebound avoidance.	Network simulation, digital twin, DSO pilot, and field trial.
Community value	Are benefits, burdens, participation conditions, and local priorities acceptable to members?	Cost reduction; local renewable utilisation; comfort; fairness; satisfaction; benefit distribution; opt-outs; overrides; and participation continuity [190].	Living laboratory, pilot, survey, and longitudinal study.
Trustworthy agentic AI	Is autonomous coordination bounded, explainable, auditable, robust, privacy-preserving, and subject to meaningful human control?	Explanation quality; provenance completeness; escalation accuracy; override capability; robustness to failures and adversarial inputs; and cybersecurity performance [346].	Adversarial testing, audit exercise, software-in-the-loop, and hardware-in-the-loop testing.
Resilience	Does the VPP improve preparedness, response, recovery, and learning during system stress?	Reserve readiness; communication availability; activation speed; critical-load support; restoration time; rebound management; and post-event learning [4].	Digital twin exercise, resilience drill, pilot, and post-event assessment.

Validation should progress through increasing levels of realism. Co-simulation supports controlled cyber–physical testing [347,348]; living laboratories and pilots expose the architecture to real devices, users, and organisational procedures [349,350], and smart-living-lab experience illustrates the need to evaluate multiple operational objectives together [351]. Regulatory sandboxes and related controlled environments are relevant where autonomous workflows interact with legal, market, data, or accountability requirements [29,30]. Longitudinal field evidence is ultimately required before claims of operational effectiveness or generalisability are justified.

9.9. Cross-Layer Integration and Operational Modes

Cross-layer integration is what turns the architecture from a collection of familiar technologies into a governed operational system. Physical resources provide conditional flexibility; interoperable data describes state and meaning; analytical models and digital twins produce evidence; the orchestration layer coordinates authorised workflows through external interfaces and established controllers. Agentic AI is therefore a coordination mechanism rather than an isolated control component [282,352]. During normal operation the architecture supports balancing, markets, grid services, and value stacking. During system stress, governance rules change the priority structure towards reserve preservation, critical-load support, degraded operation, recovery, and learning.

10. Research Gaps and Future Research Agenda

10.1. From Asset Aggregation to Operational Flexibility Intelligence

Research must move from representing aggregated capacity to representing deployable flexibility. Existing VPP studies provide mature aggregation, scheduling, and market models [8,116], but resource capability remains conditional on state, comfort, authorisation, and reserve requirements [2,64]. Multiple simultaneous services create opportunity costs and conflicting commitments [48,82], while small community portfolios remain sensitive to local uncertainty [9,203]. Future work should therefore model availability, confidence, substitution, rebound, reserve, and authorisation as part of the flexibility product itself.

10.2. From Conventional AI Components to Safe Agentic Orchestration

The priority is not to replace successful control and optimisation methods, but to determine when a governed orchestration layer adds value. Digital twins and established analytical tools already provide essential decision and validation functions [124], while agentic architectures can coordinate them across changing workflows [24,261]. Future work should formalise delegated authority, approval thresholds, rollback, and human intervention [307,310] and verify that language-based planning and explanation remain grounded in operational evidence rather than plausible but incorrect output [20,21,305]. Tool access must follow least-privilege, validation, sandboxing, and provenance principles [23,306].

10.3. From Market Participation to Dual Market and Resilience Operation

Community VPP research remains strongly market-oriented [8,33], while resilience creates a competing use for the same distributed resources [35,200]. Value stacking can produce overlapping commitments and settlement conflicts [82,163], and major events can invalidate assumptions about communication, data, and normal market operation [4,197]. A central research requirement is therefore a quantitatively validated framework for valuing retained resilience reserve against expected market value, including event probability, critical-load support, vulnerable-member protection, recovery value, and opportunity cost. Behind-the-metre resources should be incorporated into that analysis where community governance permits [34].

10.4. From Technical Optimisation to Community-Governed Flexibility

Community VPPs cannot be governed through technical efficiency alone because participation, autonomy, fairness, comfort, privacy, trust, and local value define whether flexibility remains socially deployable [5,17]. Fairness-aware optimisation provides useful foundations [301,353], but benefit allocation must also address unequal asset ownership and inconvenience [105,108]. Consent should be revisable as operating conditions and member preferences change [97,109], and explanations should make activation, compensation, and contestability intelligible to non-expert participants [331]. Future work should convert these principles into enforceable scheduling rules, permissions, approval thresholds, and settlement mechanisms.

10.5. From Conceptual Architectures to Interoperable and Validated Deployment

The principal deployment gap is integration under realistic conditions [8,17]. Community VPPs must connect heterogeneous devices, BEMS platforms, DER controllers, market systems, DSO interfaces, preference services, and settlement functions through interoperable semantics and secure interfaces [193,314]. Cybersecurity controls must be evaluated across complete workflows rather than isolated components [192,341], and digital-twin evidence must progress towards software-in-the-loop, hardware-in-the-loop, living-lab, market-pilot, and longitudinal field validation [272,347]. Scalability must be evaluated at both infrastructure and orchestration levels: larger portfolios increase resource registries, concurrent data and tool interactions, communication load, conflict-resolution demand, and audit volume, and may require hierarchical or distributed orchestration rather than a single central agentic coordinator. The decisive research question is whether the integrated architecture remains reliable, governable, and scalable as portfolio size and operational authority increase.

10.6. Future Research Roadmap

A proportionate development path begins with human-supervised decision support, progresses to bounded execution of low-risk workflows, and only then moves towards broader field deployment. Early work should focus on flexibility discovery, context interpretation, explainable scheduling, digital-twin testing, market analysis, and member communication. Living laboratories and pilots can then evaluate bounded tool use, market and DSO interaction, fairness controls, cybersecurity, resilience exercises, escalation, and auditability. Long-term deployment should be permitted only where operational evidence shows that additional autonomy improves coordination without weakening safety, community legitimacy, accountability, or human control.

11. Conclusions

This study shows that community VPPs should be understood as governed coordination systems rather than as simple aggregations of installed distributed resources. Photovoltaics, storage, EVs, heat pumps, smart buildings, and controllable demand become deployable flexibility only when technical state is reconciled with user requirements, permissions, market commitments, grid constraints, community priorities, and resilience reserve. The resulting problem is inherently socio-technical because the same action that creates market or grid value can also affect comfort, mobility, fairness, autonomy, privacy, and local legitimacy.

The synthesis also clarifies the boundary between established multi-agent energy systems and the agentic AI perspective. Forecasting, optimisation, reinforcement learning, deterministic control, and classical MAS already provide mature and valuable functions, including distributed representation and coordination. Agentic AI is justified only where community VPP operation requires additional context interpretation, multi-step workflow orchestration, governed tool use, dynamic exception handling, grounded explanation, and bounded delegation across those established capabilities. Integrated agentic community VPPs should therefore be regarded as an emerging design direction rather than an established operational technology.

Operational flexibility intelligence is introduced as the integrating capability that connects potential flexibility to authorised, verifiable, and accountable action. The proposed conceptual reference architecture operationalises this idea by combining physical resources, interoperable data, digital twins, analytical AI, governed agentic orchestration, and market, grid, and resilience interfaces under cross-cutting community governance, cybersecurity, fairness, provenance, approval, fallback, and human-in-command controls. The architecture explicitly prevents the agentic layer from bypassing validated control services and places physical actuation behind governed interfaces and local safety functions.

The contribution remains conceptual and its value must be established through progressively realistic implementation and validation. Particular priorities are interoperable reference implementations, agent-specific safety testing, community-governance mechanisms that can be enforced operationally, and quantitative methods for allocating flexibility between immediate market value and retained resilience. The central research challenge is not how to maximise autonomous control, but how to use additional orchestration capability only where it improves coordination while preserving reliability, community legitimacy, and accountable human authority.