Integrating Psychological Motivation and Adversarial Resilience in Microgrid Operations: A Tri-Level Robust Optimization Approach

Abstract

The increasing complexity of modern microgrid operations, driven by high renewable energy integration and growing prosumer participation, demands innovative optimization approaches. This paper proposes a novel behaviorally informed tri-level optimization framework that integrates goal-setting theory (GST) with distributionally robust optimization (DRO) and reinforcement learning (RL) to enhance prosumer engagement, system efficiency, and adversarial resilience. The upper level employs GST to motivate prosumers with structured energy-saving and trading goals, the middle level optimizes cost, emissions, and resource allocation, while the lower level ensures robustness against adversarial uncertainties using Wasserstein-based DRO. A multi-agent RL approach is incorporated for adaptive decision-making under uncertainty. Extensive simulations on a community-scale microgrid reveal a 25% cost reduction, 30% increase in prosumer engagement, and improved resilience against adversarial scenarios. This research bridges behavioral psychology and energy optimization, introducing a human-centric paradigm that improves microgrid sustainability, participation, and robustness.