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 constrain...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01015.pdf |
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| author | Jalit S. A. Warkad S. B. Rane P. R. Bonde S. V. |
| author_facet | Jalit S. A. Warkad S. B. Rane P. R. Bonde S. V. |
| author_sort | Jalit S. A. |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ed5c8187d8745828b01944de008f790 |
| institution | DOAJ |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-0ed5c8187d8745828b01944de008f7902025-08-20T03:22:11ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280101510.1051/epjconf/202532801015epjconf_icetsf2025_01015Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNetJalit S. A.0Warkad S. B.1Rane P. R.2Bonde S. V.3Assistant Professor, Department of Electrical Engineering, P. R. Pote Patil College of Engineering & ManagementProfessor, Department of Electrical Engineering, P. R. Pote Patil College of Engineering & ManagementAssistant Professor, Department of Electrical Engineering, P. R. Pote Patil College of Engineering & ManagementAssistant Professor, Department of Electrical Engineering, P. R. Pote Patil College of Engineering & ManagementThe 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.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01015.pdf |
| spellingShingle | Jalit S. A. Warkad S. B. Rane P. R. Bonde S. V. Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet EPJ Web of Conferences |
| title | Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet |
| title_full | Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet |
| title_fullStr | Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet |
| title_full_unstemmed | Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet |
| title_short | Design of an Improved Model for Smart Grid Pricing Using ST-GNN-PNet and MAD-RL-StackelNet |
| title_sort | design of an improved model for smart grid pricing using st gnn pnet and mad rl stackelnet |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01015.pdf |
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