Multi-objective optimization of gamified demand response for PV-integrated microgrids: a novel NSGA-III framework with behavioral adaptation modeling.

The increasing proliferation of residential photovoltaic (PV) systems in microgrids offers significant potential for enhancing renewable energy self-consumption and reducing dependency on external grid power. However, the inherent intermittency of solar generation and the mismatch between peak generation and household demand patterns require effective demand-side flexibility. Traditional demand response programs, often based solely on financial incentives or dynamic pricing, have demonstrated limited success in sustaining user engagement. To address these challenges, this paper proposes a novel gamification-driven demand response framework for PV-integrated microgrids, designed to simultaneously optimize operational cost, renewable energy utilization, user participation, and load-shifting comfort. By integrating behavioral adaptation modeling directly into the optimization process, the proposed framework captures the nonlinear and dynamic responses of households to gamification incentives, allowing for a more realistic and behaviorally-grounded approach to microgrid scheduling. The optimization problem is formulated as a multi-objective model and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which efficiently explores the trade-offs between cost minimization, PV self-consumption maximization, gamification-driven participation enhancement, and household comfort preservation. Compared to conventional demand response mechanisms, the proposed method explicitly incorporates evolving user behavior, dynamic incentive distribution, and social influence propagation, ensuring that demand-side flexibility is unlocked through both financial and psychological mechanisms.