Journal of Resource Management and Decision Engineering In Press 2027 11 01 Designing a Flexibility Management Model Using Artificial Intelligence in Active Distribution Networks with Consumer and Producer Participation Designing a Flexibility Management Model Using Artificial Intelligence in Active Distribution Networks with Consumer and Producer Participation 1 18 EN Journal Article 2026 06 28 <p>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 &lt; 0.001) and intelligent decision-making (β = 0.214, p &lt; 0.001). Artificial intelligence capabilities significantly predicted forecasting and uncertainty management (β = 0.671, p &lt; 0.001) and intelligent decision-making (β = 0.486, p &lt; 0.001). Incentive and market mechanisms significantly influenced consumer and prosumer participation (β = 0.537, p &lt; 0.001), which significantly predicted flexible demand management (β = 0.458, p &lt; 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 &lt; 0.01). Network operational flexibility strongly predicted system-level flexibility outcomes (β = 0.713, p &lt; 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.</p> https://journalrmde.com/index.php/jrmde/article/download/445/415