Psychological insights for electric vehicles

The integration of electric vehicles (EVs) into modern power systems has introduced unprecedented opportunities for enhancing grid flexibility, integrating renewable energy, and reducing operational costs. However, managing the uncertainties associated with user behavior, renewable energy generation, and dynamic grid demand poses significant challenges to achieving optimal vehicle-to-grid (V2G) system performance. This paper presents a novel interdisciplinary framework that combines Self-Determination Theory (SDT) with Differentiable Distributionally Robust Optimization (DRO) to address these challenges. By embedding user-centric psychological insights into a robust optimization model, the proposed framework prioritizes user satisfaction and engagement while ensuring technical efficiency and system resilience. The mathematical modeling employs a multi-objective optimization approach to minimize total operational costs, maximize user satisfaction, and enhance system robustness. Constraints reflect real-world operational limits, including energy balance, grid dependency, and renewable curtailment. The methodology incorporates advanced neural network-based energy forecasting, gamification-driven user participation strategies, and dynamic clustering to foster community-based V2G collaboration. The differentiable nature of the DRO model enables real-time adaptability, making it scalable for large-scale V2G networks. Case studies on a simulated urban V2G network of 10,000 EVs demonstrate the framework’s efficacy. Results indicate that integrating user engagement metrics into energy dispatch decisions can increase participation rates by up to 20% while reducing peak grid dependency by 25%. Furthermore, the system effectively mitigates renewable energy intermittency, achieving a 15% reduction in curtailment and ensuring robust performance under worst-case uncertainty scenarios. These findings underscore the transformative potential of combining psychological theories with advanced optimization techniques in energy management. This study makes four key contributions: (1) a user-centric V2G optimization framework leveraging SDT principles to enhance engagement and satisfaction; (2) a differentiable DRO approach for real-time robust energy management under uncertainty; (3) the integration of gamification and community-based clustering to promote sustained participation; and (4) a scalable methodology applicable to large-scale V2G networks. This interdisciplinary approach sets a new benchmark for addressing the technical and behavioral complexities of V2G systems, paving the way for more sustainable and resilient energy solutions. • Integrates Self-Determination Theory into V2G systems for enhanced user engagement. • Employs Differentiable Distributionally Robust Optimization for real-time adaptability. • Proposes gamification and clustering to sustain community-based V2G participation. • Achieves a 25% reduction in peak grid dependency and 15% renewable curtailment. • Demonstrates scalability in a large-scale V2G network with 10,000 EVs.