Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet

The shift to decentralized smart grids requires dynamic pricing based on demand, supported by advanced technology to adapt to behavioral changes. However, current pricing models fail to capture spatio-temporal load behavior, consumer heterogeneity, and externalities like emissions. Privacy constraints also hinder granular data collection, causing revenue loss. To address these issues, this proposal introduces the Topo-Behavioral Hybrid Learning Model (TBHLM) for dynamic pricing in smart grids. TBHLM has five key modules, ST-GNN-PNet: Uses temporal graph convolutions to forecast loads, congestion, and locational marginal prices (LMPs) with <3.5% MAPE and <3s latency. FBEM-Net: Applies federated learning for privacy-preserving elasticity modeling, achieving ~92% behavioral prediction accuracy and a 15% increase in demand response participation. MAD-RL-StackelNet: Uses multi-agent reinforcement learning for equilibrium pricing, leading to 18-22% peak shaving and a 30% rise in pricing stability. RBEIO-Opt: Integrates carbon penalties into economic dispatch, reducing emissions by 12.6% and improving welfare by 6.1%. PIDE-Engine: Uses inverse optimization for utility estimation with a privacy breach probability of <0.01%. TBHLM provides an adaptive, secure, and consumer-centric framework for real-time pricing, enhancing efficiency, sustainability, and grid intelligence sets.