Optimizing Smart Grid Demand Response: A Stackelberg Game Framework for Priority-Aware Dynamic Pricing and Load Scheduling

Abstract

Modern power systems face increasing complexity due to fluctuating demand and the intermittent nature of renewable energy sources. To address these challenges, this paper introduces a novel Stackelberg game-theoretic framework for intelligent demand response (DR) in smart grids. Our approach models hierarchical interaction between energy providers (leaders) and consumers (followers), incorporating priority-aware load scheduling and real-time feedback loops. Consumers are classified into priority and non-priority categories. A Markov chain-based behavior model captures stochastic user adaptation, enabling dynamic price adjustment. Simulations over 1, 7, and 30-day horizons in MATLAB demonstrate significant improvements: A 22% reduction in operational costs and a 15% decrease in peak-to-average ratio (PAR). The framework converges efficiently and ensures adherence to the grid capacity. These findings demonstrate the effectiveness of our adaptive and scalable solution. Unlike existing Stackelberg-based models, our approach uniquely integrates real-time feedback, priority-based user classification, and a stochastic Markov behavior model to enhance pricing responsiveness, grid reliability, and fairness across diverse consumer types.