A Scoping Review of Flexibility Markets in the Power Sector: Models, Mechanisms, and Business Perspectives
Abstract
1. Introduction
2. Materials and Methods
2.1. Protocol and Registration
2.2. Eligibility Criteria
- Type of publication: Eligible documents included peer-reviewed journal articles and conference papers.
- Source quality and accessibility: Grey literature, technical reports, and non-peer-reviewed sources were excluded. Only articles with full-text access and a valid DOI were included.
- Thematic scope: Studies concentrated on energy flexibility marketplaces, flexibility methods, coordinating techniques, enabling technology, and business models.
- Language: Only studies published in English were considered.
2.3. Information Sources and Search Strategy
- In Web of Science (WoS), we employed the terms “Flexibility Markets” and “Peer to Peer review”, resulting in 319 records.
- In Scopus, we applied the query “TITLE-ABS (flexibility AND markets AND energy) AND KEY (flexibility AND market)”, limited to journals and conferences, which yielded 543 results.
- In IEEE Xplore, we used as search parameters “(“Document Title”: Flexibility markets) OR (“Publication Title”: Flexibility market) OR (“Author Keywords”: Flexibility market)”, and restricted to journals and conference proceedings, retrieving 501 documents.
2.4. Selection Process
- Identification: Duplicate records were removed based on DOI and title matching, reducing the dataset to 1302 articles.
- Screening: This stage involved two steps. First, a language filter was applied and titles were screened to retain only English-language publications and exclude clearly unrelated topics, resulting in 743 articles. Second, records containing irrelevant keywords or whose abstracts were misaligned with the research scope were excluded, yielding a final set of 243 articles.
2.5. Data Charting Process
- PRISMA-ScR principles to ensure replicability and methodological transparency.
- TEAM Framework (Technology–Economy–Actor–Market) to examine actor interactions, coordination models, and systemic architectures in flexibility markets. This framework integrates concepts such as Moore’s metaphor, coordination theory, and value modelling, allowing analysis across technical, economic, and institutional dimensions according to the Business Ecosystem Architecture Modelling [10]. For instance, it captures whether coordination is centralized or distributed, what level of market integration is pursued, and how actors relate within the system. Ref. [11] illustrates a comparable application to peer-to-peer energy markets, which, although governed differently, share key functional traits with flexibility markets (e.g., decentralized control and value exchange).
- Business Model Canvas to identify economic mechanisms and organizational aspects of flexibility markets. This includes value propositions, customer segments, revenue structures, and cost models. While prior reviews [3,12,13,14] have applied simplified or case-specific versions of the BMC, our approach uses a comprehensive and comparative structure across all included articles.
2.6. Critical Appraisal and Synthesis of Results
3. Results
3.1. Overview of Included Studies
3.2. Flexibility Conceptualization
3.2.1. Definition
3.2.2. Metrics
- Capacity and power availability: Quantifies the amount of flexible energy or power that can be traded. Common indicators include ramp-up/down rates, dispatchability, curtailed energy, and load shift volume. This is the most widely used category, especially in capacity planning and operational scheduling.
- Temporal responsiveness: Captures how quickly and accurately a resource reacts to control signals, using metrics like response time, ramp rate, delay, and activation window. This is the second prominent group, especially in short-term operations.
- Cost–benefit metrics: Relate flexibility provision to economic performance, using activation cost, market revenues, and profit margins. These metrics are central to techno-economic evaluations and business model assessments.
- Reliability and variability: Assess the likelihood that flexibility will be available when needed. Probabilistic indicators such as Lack of Ramp Probability (LORP) and Insufficient Ramping Resource Expectation (IRRE) are used under high shares of renewables.
- Market and clearing performance: Evaluate how flexibility affects system efficiency and price signals. Key indicators include Market Clearing Price (MCP), activation ratio, and convergence time. This category features prominently in market design and simulation studies.
- Capacity adequacy under uncertainty: Focuses on system robustness to operate securely under uncertainty. Metrics like System Capability Ramp (SCR) and Ramping Capability Shortage Expectations (RCSE) are used in stress-test scenarios and long-term planning, though only a few studies apply them.
- Technical and grid-level indicators: This category connects flexibility to grid performance and includes voltage deviations and RES hosting capacity. These are essential for distribution system operators (DSOs) and grid reinforcement planning.
3.3. Modelling Assumptions and Interoperability
3.3.1. Rationality
- Perfect rationality or symmetry: Agents have full information, unlimited computational capacity, and act to maximize utility or profit.
- Bounded rationality: Agents operate under limited information or cognitive constraints, often following heuristics or behavioural rules.
- Unspecified: The model does not explicitly state how agent decisions are determined.
3.3.2. Perfect Information
3.3.3. Grid Constraints
- Detailed grid constraints: These studies incorporate full power flow models, typically using IEEE standard test systems (e.g., 13, 33, or 69-bus systems [98]) or advanced test cases like Simbench [99]. Formulations such as AC-OPF, DC-OPF [100], and LinDistFlow [101] are employed to integrate line limits, voltage constraints, and nodal balances directly into the optimization process. Flexibility is treated as a control variable constraint by network states, allowing spatial and temporal feasibility assessments.
- Post-optimization validation: Some studies exclude grid constraints during optimization, but validate results afterward through power flow simulations [103]. This ensures physical feasibility without embedding complexity in the main model.
- Narrative or qualitative consideration: These works acknowledge grid limitations, such as voltage, congestion, or capacity problems, but do not include mathematical representation. Often focused on qualitative insights, market design concepts, or regulatory or institutional frameworks, this group accounts for 61.3% (149) of the reviewed articles.
- No consideration of grid constraints: A total of 62 studies (25.5%) omit any reference to grid limitations, focusing on market mechanisms, actor behaviour, or economic outcomes. While useful for exploratory modelling, this omission reduces operational realism, especially when flexibility is intended for grid services like congestion management and voltage control.
3.3.4. Tariffs
3.3.5. Modelling Approaches and Algorithms
3.3.6. Interoperability
3.3.7. Technology Readiness Level
3.4. Market Participants and Enablers
3.4.1. Aggregation
3.4.2. Time Scale
3.4.3. Agent
3.5. Transactional and Coordination Aspects
3.5.1. Coordination Paradigms
3.5.2. Optimization Level of Coordination
- Global optimization: Global or system-wide optimization typically appears in centralized or bilevel frameworks, where a single authority (e.g., TSO or DSO) optimizes the activation and allocation of all available flexibility resources. These models aim to maximize social welfare, minimize system costs, and enforce network constraints holistically. They offer high efficiency and clear visibility over system dynamics but rely on complete information and pose scalability challenges in large, distributed systems.
- Distributed optimization: In contrast, distributed optimization leverages multi-agent systems, where each actor (aggregator, prosumer, DSO, etc.) solves its own local problem, potentially exchanging information through a coordination protocol. Methods include dual decomposition, Alternating Direction Method Of Multipliers (ADMM), and iterative pricing schemes. This approach reflects real-world decentralization and improves scalability, but requires convergence guarantees, robust communication, and often approximate solutions.
- Rule-based heuristics: Many pilot projects, regulatory sandboxes, and early-stage demonstrations rely on heuristic or rule-based methods for coordination. These may involve static priorities, time-of-use rules, and simple threshold-based activation. While not optimal in a mathematical sense, heuristics are pragmatic, transparent, and easily implementable, making them useful in uncertain or evolving regulatory contexts.
- Stackelberg and game-theoretic models: To capture strategic behaviour or sequential decision making, some studies use Stackelberg games, leader–follower models, and other game-theoretic frameworks. These allow one agent (e.g., a DSO) to anticipate responses from others (e.g., aggregators) and design coordination accordingly. Such models are valuable for studying incentives and market power but are computationally intensive and often limited to theoretical analysis or stylized case studies.
3.5.3. Strategic Behaviour
3.6. Business Model Analysis
3.6.1. Value Proposition
3.6.2. Customer Segments
3.6.3. Channels
3.6.4. Cost Structure
3.6.5. Revenue Streams
3.6.6. Actor-Specific Differentiation
4. Discussion
4.1. Main Findings and Emerging Patterns
4.2. Methodological Weaknesses
4.3. Thematic Gaps in the Literature
4.4. Implications for Practice and Policy
4.5. Future Research Directions and Priorities
- Integration of uncertainty in modelling frameworks: While several studies recognize the stochastic nature of flexibility provision, uncertainty is often abstracted away or addressed through simplified sensitivity analyses. Future work should advance the use of stochastic optimization, scenario-based modelling, and probabilistic simulation techniques to reflect real-world conditions such as renewable variability, price volatility, and user behaviour. As demonstrated in Section 3.3.5, only a minority of studies incorporate robust scenario generation or risk-adjusted planning. This limits the capacity of current models to support investment decisions and market design under high-renewables and decentralized scenarios.
- Behavioural and socio-economic modelling of end-users: Many studies assume passive or price-responsive behaviour, overlooking the heterogeneity of preferences, routines, and barriers that affect user participation. The literature underrepresents aspects such as comfort boundaries, rebound effects, and behavioural inertia. Future research should incorporate agent-based simulations, behavioural economics, and co-creation methods to capture the dynamic interaction between incentives and actual user responses. This is particularly relevant in low-voltage grids and energy communities, where social acceptance and perceived fairness can make or break flexibility mechanisms.
- Realistic cost structures and economic viability: A key gap lies in the simplification of cost models, which often include only activation costs while ignoring transaction costs, ICT investments, user onboarding, coordination, and platform maintenance. As shown in Section 3.6.4, cost realism is especially poor in early-stage models (TRLs 1–3), limiting their use for scalability assessment. Future research must adopt more granular and comprehensive cost typologies, possibly aligned with techno-economic assessment (TEA) and total cost of ownership (TCO) frameworks. Exploring cost-efficiency trade-offs across flexibility types and deployment models (e.g., centralized vs. distributed) would be particularly valuable.
- Interoperability, standardization, and platform architecture: Despite the centrality of digital infrastructure, interoperability is addressed in less than 40% of the reviewed studies (Section 3.3.6). This hinders the replicability and integration of flexibility platforms across jurisdictions. Future research should investigate open standards, layered architectures, and interface protocols, such as USEF, OpenADR, and CIM, not only from a technical perspective, but also from a governance and business model perspective. Platform design should be evaluated not only for performance, but also for data ownership, user trust, and regulatory alignment.
- Equity, risk-sharing, and institutional feasibility: As discussed in Section 3.6 and Section 4.3, the distribution of costs, benefits, and control remains largely unexplored. Future studies should analyze how flexibility mechanisms impact different user groups, especially vulnerable consumers, SMEs, and non-participating actors. Mechanisms for risk-sharing, fair remuneration, and revenue stacking across DSOs, TSOs, and aggregators need to be designed and evaluated, considering both efficiency and fairness. This also includes assessing institutional arrangements, such as role allocation, accountability, and dispute resolution in multi-actor flexibility schemes.
- Empirical validation, experimentation, and international comparison: Only a small fraction of the reviewed studies rely on real-world data, pilot results, or cross-country comparisons. Model assumptions are often insufficiently validated, leading to a mismatch between simulated performance and observed dynamics. There is a need for greater emphasis on learning from demonstration projects, regulatory sandboxes, and real-world trials—especially for evaluating market design, user response, and platform operation. Furthermore, comparative studies between countries or regions can uncover contextual dependencies and transferability limits, helping to design flexibility mechanisms that are both scalable and locally adapted.
- Enhancing load restoration strategies through cross-sector flexibility. Recent developments suggest promising avenues in coordinating the flexibility of buildings and electric buses during restoration scenarios. Leveraging thermal inertia and the spatiotemporal mobility of electric vehicles could significantly enhance grid resilience. Future studies should investigate fair allocation mechanisms, integrate sustainability metrics, and assess the societal impacts of prolonged outages, particularly in the context of decentralized energy transportation networks.
- Integrating cooperative game-theoretic approaches and multi-agent coordination. Emerging research highlights the potential of cooperative strategies based on asymmetric negotiation to optimize interactions among heterogeneous agents—such as renewable generators, storage systems, and building loads. These approaches enable fair profit allocation, preserve agent privacy, and improve solving efficiency through advanced distributed algorithms. Exploring such models may provide more realistic, privacy-preserving, and computationally scalable coordination mechanisms for future flexibility markets.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ABM | Agent-Based Models |
ADMM | Alternating Direction Method of Multipliers |
BMC | Business Model Canvas |
BSUoS | Balancing Services Use-of-System |
CBA | Cost–Benefit Analysis |
CIM | Common Information Model |
DA | Day-ahead |
DER | Distributed Energy Resources |
DSO | Distribution System Operator |
DUoS | Distribution Use-of-System |
EV | Electric Vehicle |
FMP | Flexibility Market Platform |
FSP | Flexibility Service Provider |
HEMS | Home Energy Management System |
IoT | Internet of Things |
IRRE | Insufficient Ramping Resource Expectation |
LFM | Local Flexibility Market |
LMP | Locational Marginal Pricing |
LORP | Lack of Ramp Probability |
LP | Linear Programming |
MCP | Market Clearing Price |
MILP | Mixed Integer Linear Programming |
NPV | Net Present Value |
OPF | Optimal Power Flow |
OpenADR | Open Automated Demand Response |
PRISMA-ScR | Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews |
P2P | Peer-to-Peer |
QoS | Quality of Service |
RES | Renewable Energy Sources |
RT | Real-time |
SCR | System Capability Ramp |
SGAM | Smart Grid Architecture Model |
SO | System Operator |
TEAM | Technological, Economic, Actor and Market |
ToU | Time-of-Use |
TNUoS | Transmission Network Use-of-System |
TRL | Technology Readiness Level |
TSO | Transmission System Operator |
USEF | Universal Smart Energy Framework |
VPP | Virtual Power Plant |
WoS | Web of Science |
Appendix A
Section | Column | Type | Description |
---|---|---|---|
General Data | Authors | Free text | List of all authors |
Title | Free text | Full paper title | |
Year | Numeric | Year of publication | |
Source title | Free text | Journal or conference proceedings | |
DOI | Text | Digital Object Identifier (if available) | |
Objective and research question | Paper type | Choice | Review, Modelling, Empirical, Experimental |
Research question | Free text | What problem or research gap does the paper aim to address? | |
Future work | Free text | Open questions or future work proposed by the authors | |
Flexibility | Definition | Free text | How is flexibility defined? |
Type | Choice | Explicit/Implicit | |
Metrics | Free text | e.g., ramp rate, time response | |
Modelling assumptions | Rationality | Choice | Perfect/Bounded/Not specified |
Information symmetry | Boolean | Do all agents share the same information? | |
Grid constraints | Free text | Are physical grid limits included? | |
Tariffs | Free text | e.g., Static, dynamic, ToU, real-time tariffs. | |
Algorithms | Free text | e.g., LP, MILP, AI, heuristic, agent-based, game theory. | |
Technology Readiness Level | Numeric | TRL 1 (basic research) to TRL 9 (commercial deployment) | |
Interoperability | Standard used | Free text | e.g., OpenADR, IEC 61850, Modbus, OpenFMB. |
Market participants and enablers | Aggregation level | Choice | Household, SME, Industry, Community, Aggregator, VPP |
Time scale addressed | Free text | e.g., Intra-day, Day-ahead, Weekly, Seasonal. | |
Agent | Choice | Consumer, Prosumer, Aggregator, DSO, TSO, Grid Operator | |
Flexibility role | Choice | Provider, Requester, Coordinator, Enabler | |
Coordination paradigms | Choice | Centralized, Decentralized, Hierarchical, Peer-to-peer | |
Level of coordination | Choice | Local, Regional, System-wide; Distributed vs. Global | |
Strategic behaviour | Free text | Any modelling of gaming, bidding strategy, selfish behaviour | |
Business Model | Value proposition | Free text | What value or benefit is provided, and to whom? |
Customer segments | Free text | e.g., Individuals, DSOs, communities, etc. | |
Channel | Free text | e.g., Platforms, aggregators, etc. | |
Cost structure | Free text | e.g., CAPEX, OPEX, cost- vs. value-driven. | |
Revenue streams | Free text | Fixed/dynamic pricing, incentives, savings | |
Results, conclusions and recommendations | Main findings | Free text | What are the key insights, results or takeaways from the study? |
Limitations | Free text | What are the limitations, caveats or uncertainties? | |
Benefits and risks | Free text | How are risks/rewards distributed among actors? | |
Rating | Choice | High/Medium/Low | |
Notes | Free text | Any extra comments |
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Category | Metrics | References |
---|---|---|
Capacity and power availability | Flexibility Ramp-Up | [4,17,19] |
Flexibility Ramp-Down | [4,17,19] | |
Load shift volume | [1,20] | |
Curtailed energy | [21] | |
Energy capacity | [22] | |
Dispatchability | [4,17] | |
Power | [2,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41] | |
Temporal responsiveness | Response Time | [6,20,22,28,31,42,43,44,45,46,47,48,49,50,51,52,53] |
Ramp Rate | [15,42,50,54,55,56,57] | |
Delay | [8,14,18,58,59,60] | |
Start-up Time | [17] | |
Activation Window | [17] | |
Cost–benefit metrics | Activation cost | [21,42,48,50,51,61,62] |
Market revenue | [9,63,64] | |
Profit margin | [3,21,63,65] | |
Reliability and variability | IRRE | [4,19] |
LORP | [4,19] | |
Market and Clearing Performance | MCP | [66] |
Activation ratio | [5,67] | |
Social welfare | [19,34,68,69,70,71,72,73,74,75,76,77,78] | |
Efficiency | [5,20,24,28,51,57,66,73,79,80,81,82,83,84,85,86,87,88,89,90,91] | |
Convergence time | [92,93] | |
Capacity adequacy under uncertainty | SCR | [17,94] |
RCSE | [17] | |
Technical and grid-level indicators | Voltage deviation | [37,38,95,96,97] |
Hosting capacity | [78,97,98] |
Category | References |
---|---|
ToU and dynamic tariffs | [2,9,16,18,20,25,27,32,50,68,104,105,106,107,108,109] |
Wholesale electricity market prices | [8,14,31,43,46,56,78,81,84,102,110,111,112,113,114,115,116,117,118] |
Network access tariffs | [3,15,28,33,41,53,55,66,72,74,89,91,95,119,120,121,122,123,124,125,126,127,128] |
Hybrid remuneration schemes | [6,45,58,62,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143] |
Tariffs as operational signals | [4,7,12,48,59,79,144,145,146,147,148,149,150,151,152,153,154,155,156] |
Category | References |
---|---|
Conventional optimization algorithms | [12,15,18,19,21,25,29,30,33,34,37,38,44,48,51,54,61,62,66,67,69,71,73,74,76,77,79,80,84,89,91,98,103,105,106,111,117,119,125,131,132,133,134,136,137,140,141,143,147,149,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180] |
Game-theoretic and equilibrium models | [42,83,159,161,181] |
Agent-based models | [27,63,83,89,103,167] |
Machine learning and heuristic methods | [18,40,96,103,182,183] |
Distributed optimization techniques | [41,76,93,126] |
Stochastic optimization methods | [64,66,93,117,118,121,172,184,185,186] |
Timescale | Purpose | Applications |
---|---|---|
Long term | Strategic planning and system adequacy | Annual system planning, capacity remuneration mechanisms, regulatory tariffs, and incentive schemes |
Medium term | Anticipatory scheduling to address seasonal variability | Seasonal flexibility assessments, long-term market structuring |
Short term | Short-term market operation and adaptability to forecast uncertainties | Day-ahead and intraday market participation, transition from zonal to nodal pricing, reduction in gate closure intervals |
Very short term | Real-time operational responsiveness and frequency stability | Real-time balancing services, frequency containment and restoration reserves (FCRs, FRRs), fast frequency response (FFR), inertia support |
Paradigm | Key Coordinator | Information Flow | Pros | Cons |
---|---|---|---|---|
Centralized | TSO/DSO | One-way | Efficiency, control | Scalability, limited local autonomy |
Decentralized | No single entity | Peer-to-peer | Scalability, autonomy | Coordination, system integration |
Hierarchical | Multi-layer | Sequential | Modularity, coordination | Interface complexity, latency |
Hybrid | Mixed | Multi-directional | Flexibility, realism | Governance, standardization need |
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Cano-Martínez, J.; Quijano-López, A.; Fuster-Roig, V. A Scoping Review of Flexibility Markets in the Power Sector: Models, Mechanisms, and Business Perspectives. Energies 2025, 18, 5213. https://doi.org/10.3390/en18195213
Cano-Martínez J, Quijano-López A, Fuster-Roig V. A Scoping Review of Flexibility Markets in the Power Sector: Models, Mechanisms, and Business Perspectives. Energies. 2025; 18(19):5213. https://doi.org/10.3390/en18195213
Chicago/Turabian StyleCano-Martínez, Jorge, Alfredo Quijano-López, and Vicente Fuster-Roig. 2025. "A Scoping Review of Flexibility Markets in the Power Sector: Models, Mechanisms, and Business Perspectives" Energies 18, no. 19: 5213. https://doi.org/10.3390/en18195213
APA StyleCano-Martínez, J., Quijano-López, A., & Fuster-Roig, V. (2025). A Scoping Review of Flexibility Markets in the Power Sector: Models, Mechanisms, and Business Perspectives. Energies, 18(19), 5213. https://doi.org/10.3390/en18195213