Designing a Flexibility Management Model Using Artificial Intelligence in Active Distribution Networks with Consumer and Producer Participation

Mahmoud Fereydouni

Authors

Mahmoud Fereydouni Department of Electrical Engineering, Technical and Vocational University (TVU),Tehran, Iran https://orcid.org/0000-0002-4700-1225

Keywords:

Artificial Intelligence, Flexibility Management, Active Distribution Networks, Consumer Participation, Prosumers, Distributed Generation

Abstract

This study aimed to design and validate an artificial intelligence-based flexibility management model for active distribution networks with the participation of consumers and producers. This applied developmental study employed an exploratory sequential mixed-method design. In the qualitative phase, 18 experts in electricity distribution, smart grids, distributed generation, energy management, and artificial intelligence were purposively selected in Tehran and interviewed using a semi-structured protocol until theoretical saturation was achieved. The qualitative findings were used to develop a 52-item questionnaire covering 11 model constructs. In the quantitative phase, 300 managers, engineers, technical specialists, and energy-sector experts completed the questionnaire. Data were analyzed using SPSS 29 and SmartPLS 4. Measurement-model evaluation included indicator loadings, Cronbach’s alpha, composite reliability, average variance extracted, heterotrait-monotrait ratios, and variance inflation factors. The structural model was assessed using partial least squares structural equation modeling with 5,000 bootstrap resamples, path coefficients, R², Q², f², SRMR, and NFI. Intelligent data and communication infrastructure significantly predicted artificial intelligence capabilities (β = 0.624, p < 0.001) and intelligent decision-making (β = 0.214, p < 0.001). Artificial intelligence capabilities significantly predicted forecasting and uncertainty management (β = 0.671, p < 0.001) and intelligent decision-making (β = 0.486, p < 0.001). Incentive and market mechanisms significantly influenced consumer and prosumer participation (β = 0.537, p < 0.001), which significantly predicted flexible demand management (β = 0.458, p < 0.001). Forecasting and uncertainty management (β = 0.219), flexible demand management (β = 0.187), distributed-generation coordination (β = 0.231), energy-storage management (β = 0.143), and intelligent decision-making (β = 0.302) all significantly predicted network operational flexibility (all p < 0.01). Network operational flexibility strongly predicted system-level flexibility outcomes (β = 0.713, p < 0.001). The model explained 68.4% of the variance in network operational flexibility and 50.8% of the variance in system-level flexibility outcomes. The findings indicate that effective flexibility management in active distribution networks requires the integrated development of intelligent infrastructure, AI capabilities, forecasting, distributed-resource coordination, demand-side flexibility, market incentives, and consumer and producer participation.

References

Abdelmotteleb, I., Fumagalli, E., & Gibescu, M. (2022). Assessing customer engagement in electricity distribution-level flexibility product provision: The Norwegian case. Sustainable Energy, Grids and Networks, 29, 100564. https://doi.org/10.1016/j.segan.2021.100564

Aggarwal, D., Sharma, D., & Saxena, A. B. (2024). Enhancing the Online Shopping Experience of Consumers Through Artificial Intelligence. International Journal of Information Technology and Computer Engineering(42), 1-5. https://doi.org/10.55529/ijitc.42.1.5

Amin, A. (2025). Artificial Intelligence in Social Media: A Catalyst for Impulse Buying Behavior? Young Consumers Insight and Ideas for Responsible Marketers. https://doi.org/10.1108/yc-10-2024-2297

Bahrini, M., Nikpei, M., & Baharvand, B. (2015). The impact of external environment on performance management in Lorestan Province Electricity Distribution Company. 3rd International Conference on Modern Research in Management, Economics, and Accounting,

Fallahi, A., Fallahi, F. A. U. S. H., Ghaderi, S. F., & Ebrahimi, R. (2019). Application of a robust data envelopment analysis model for performance evaluation of electricity distribution companies. International Journal of Energy Sector Management, 15(4), 724-742. https://doi.org/10.1108/IJESM-08-2018-0008

Fan, X. (2024). Artificial Intelligence's Impact on Employment: Challenges, Potential Consumers, and Policy Responses Through Automation and Workforce Rehabilitating. Lecture Notes in Education Psychology and Public Media, 73(1), 50-55. https://doi.org/10.54254/2753-7048/73/20241040

Fernández-González, R., Puime-Guillén, F., & Vila-Biglieri, J. E. (2023). Environmental strategy and the petroleum industry: a sustainability balanced scorecard approach. Journal of Petroleum Exploration and Production Technology, 13(2), 763-774. https://doi.org/10.1007/s13202-022-01543-9

Gupta, A. S., & Mukherjee, J. (2022). Long-term changes in consumers' shopping behavior post-pandemic: An exploratory study. International Journal of Retail and Distribution Management, 50(12), 1518-1534. https://doi.org/10.1108/IJRDM-04-2022-0111

Ho, S. P. S., & Chow, M. Y. C. (2023). The Role of Artificial Intelligence in Consumers’ Brand Preference for Retail Banks in Hong Kong. Journal of Financial Services Marketing, 29(2), 292-305. https://doi.org/10.1057/s41264-022-00207-3

Jayasingh, S., Choy, S. L., & Lee, S. (2022). Consumers' adoption of online shopping technologies: An integrated perspective. International Journal of Retail & Distribution Management, 50(4), 401-422.

Karami, M. A., & Separi, F. (2016). Durability and Resiliency Improvement in Mazandaran Electricity Distribution Company Against Storm. 91 (94 .)-91 (94 .). https://doi.org/10.1049/cp.2016.0691

Manikandan, G., & Bhuvaneswari, G. (2024). Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-in Case of Indian Consumers. International Journal of Scientific Research in Science Engineering and Technology, 250-259. https://doi.org/10.32628/ijsrset2411122

Moghaddas Nodeh, M. (2019). Investigating challenges of university-industry collaboration: Case study of Tehran Province Electricity Distribution Company. Innovation Management, 8(1), 8-106.

Pourabadolahankooch, M., Nowbahar, E., Sajoodi, S., & Khalafi, R. (2021). Efficiency and scale: Evidence from electricity distribution companies in Iran. Iranian Journal of Energy Economics, 10(39), 71-97. https://jiee.atu.ac.ir/article_14274.html

Rather, R. A. (2025). Does Consumers' Reveal Engagement Behaviours in Artificial Intelligence (AI)-Based Technologies? The Dynamics of Perceived Value and Self-Congruence. International Journal of Hospitality Management, 126, 103989. https://doi.org/10.1016/j.ijhm.2025.103989

Rezaei Dizgah, M., Ajali, M., & Homayounfar, M. (2022). The effect of organizational culture on the safety performance of employees of Guilan Province Electricity Distribution Organization. Journal of Occupational Health and Health Promotion, 6(2), 98-110. https://doi.org/https://doi.org/10.18502/ohhp.v6i2.10298

Samavi, H. (2021). Strategic Factors of Efficient Human Resource Management in Iranian Electricity Distribution Companies. The First International Conference on the Leap of Management Sciences, Economics, and Accounting, Sari,

Valizadeh, M., & Hosseini, S. H. (2010). A project-based model for the growth of organizational risk management in electricity distribution companies. In Fifteenth National Conference on Electricity Distribution Networks,

Yahya Pour, M., Tabari, M., Mehrara, A., Bagherzadeh, M., & Baloei Jamkhaneh, E. (2022). Designing a Green Human Resource Management Excellence Model in Northern Iran's Electricity Distribution Companies. Ethical Research Quarterly, 12(4), 278-304.

Zhang, Y., Ai, Q., & Li, Z. (2021). Grouping of Dynamic Electricity Consumption Behaviour: An f ‐divergence Based Hierarchical Clustering Model. Iet Generation Transmission & Distribution, 15(22), 3164-3175. https://doi.org/10.1049/gtd2.12248