Advanced Methodological Approaches to Multienergy Virtual Power Plant Operations: Integrating Behavioral Dynamics, Habit Formation, and Robust Splitting Algorithms

The increasing complexity of modern energy systems necessitates innovative frameworks for optimizing the operation and management of Multi-Energy Virtual Power Plants (MEVPPs). These systems integrate electricity, gas, and heat networks while incorporating renewable energy sources (RES) and energy storage systems (ESS) to achieve sustainability and resilience. This paper proposes a novel behavior-driven optimization framework that integrates Habit Formation Theory with Distributionally Robust Optimization (DRO) to address the multifaceted challenges of MEVPP operations. The framework uniquely models user behavior to capture the long-term effects of incentives on energy consumption, fostering sustainable demand response through dynamic adjustments. A multi-objective optimization model is developed to balance competing goals, including cost minimization, carbon emissions reduction, renewable energy utilization maximization, and grid stability. The model incorporates practical constraints, such as energy balance, thermal and gas network flows, and cybersecurity requirements. To solve the high-dimensional problem efficiently, a splitting algorithm tailored for DRO is proposed, enabling the decomposition of the optimization problem into computationally manageable subproblems. The methodology ensures scalability and robustness under uncertainties in renewable generation, market participation, and user behavior.