Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent
Abstract This study presents a process-centric hybrid energy management framework tailored for large-scale smart mining operations. The framework addresses three major challenges: (i) multi-source uncertainty propagation, (ii) cross-process energy coupling, and (iii) time-varying, safety-critical op...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11013-x |
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| author | Dawei Wang Yifei Li Cheng Gong Tianle Li Fang Wang Shanna Luo Jun Li |
| author_facet | Dawei Wang Yifei Li Cheng Gong Tianle Li Fang Wang Shanna Luo Jun Li |
| author_sort | Dawei Wang |
| collection | DOAJ |
| description | Abstract This study presents a process-centric hybrid energy management framework tailored for large-scale smart mining operations. The framework addresses three major challenges: (i) multi-source uncertainty propagation, (ii) cross-process energy coupling, and (iii) time-varying, safety-critical operational constraints. The energy scheduling problem is formulated as a process-constrained, multi-period optimization under uncertainty, explicitly modeling the spatio-temporal correlations among renewable power generation, ventilation loads, dewatering demands, and blasting energy requirements. To tackle high-dimensional uncertainties with non-Gaussian distributions, a Wasserstein metric-based distributionally robust optimization (DRO) model is constructed. The ambiguity set is dynamically refined through adaptive scenario generation and clustering, capturing worst-case energy supply-demand mismatches. The objective function jointly minimizes total energy cost, carbon emissions, and process-specific operational risks, subject to nonlinear thermodynamic process constraints, piecewise convex ventilation characteristics, and interdependent hydraulic-ventilation-thermal (HVT) processes. Mining safety regulations are integrated via chance constraints, embedding safety-critical margins related to pressure, airflow, and gas concentration. To alleviate the computational burden caused by nested risk formulations, a Primal-Dual Reformulated Distributionally Robust Process Scheduling (PDR-DRPS) algorithm is proposed. This method recursively updates process-coupled dual variables, enabling fast convergence within joint physical-energy feasible subspaces. The proposed framework is validated using a synthetic open-pit mining benchmark incorporating real-world meteorological data, empirical process dynamics, and regulatory thresholds. Numerical results indicate a 25.4% reduction in operational costs, a 31.2% cut in carbon emissions, and consistent adherence to safety constraints within a 3% tolerance under all uncertainty scenarios. Sensitivity analysis further highlights that process inertia and time delays significantly amplify uncertainty propagation, underscoring the necessity of process-aware robust energy scheduling in safety-critical industrial systems. The framework offers a generalizable paradigm applicable to smart mining, tunnel construction, and underground industrial infrastructures. |
| format | Article |
| id | doaj-art-32f9cd4aa1e44f059e43c9cc470660c7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-32f9cd4aa1e44f059e43c9cc470660c72025-08-20T03:04:31ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-11013-xDistributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descentDawei Wang0Yifei Li1Cheng Gong2Tianle Li3Fang Wang4Shanna Luo5Jun Li6State Grid Beijing Electric Power Research InstituteState Grid Beijing Electric Power Research InstituteState Grid Beijing Electric Power Research InstituteState Grid Beijing Electric Power Research InstituteState Grid Beijing Electric Power Research InstituteSchool of Electrical and Control Engineering, North China University of TechnologySchool of Electrical and Control Engineering, North China University of TechnologyAbstract This study presents a process-centric hybrid energy management framework tailored for large-scale smart mining operations. The framework addresses three major challenges: (i) multi-source uncertainty propagation, (ii) cross-process energy coupling, and (iii) time-varying, safety-critical operational constraints. The energy scheduling problem is formulated as a process-constrained, multi-period optimization under uncertainty, explicitly modeling the spatio-temporal correlations among renewable power generation, ventilation loads, dewatering demands, and blasting energy requirements. To tackle high-dimensional uncertainties with non-Gaussian distributions, a Wasserstein metric-based distributionally robust optimization (DRO) model is constructed. The ambiguity set is dynamically refined through adaptive scenario generation and clustering, capturing worst-case energy supply-demand mismatches. The objective function jointly minimizes total energy cost, carbon emissions, and process-specific operational risks, subject to nonlinear thermodynamic process constraints, piecewise convex ventilation characteristics, and interdependent hydraulic-ventilation-thermal (HVT) processes. Mining safety regulations are integrated via chance constraints, embedding safety-critical margins related to pressure, airflow, and gas concentration. To alleviate the computational burden caused by nested risk formulations, a Primal-Dual Reformulated Distributionally Robust Process Scheduling (PDR-DRPS) algorithm is proposed. This method recursively updates process-coupled dual variables, enabling fast convergence within joint physical-energy feasible subspaces. The proposed framework is validated using a synthetic open-pit mining benchmark incorporating real-world meteorological data, empirical process dynamics, and regulatory thresholds. Numerical results indicate a 25.4% reduction in operational costs, a 31.2% cut in carbon emissions, and consistent adherence to safety constraints within a 3% tolerance under all uncertainty scenarios. Sensitivity analysis further highlights that process inertia and time delays significantly amplify uncertainty propagation, underscoring the necessity of process-aware robust energy scheduling in safety-critical industrial systems. The framework offers a generalizable paradigm applicable to smart mining, tunnel construction, and underground industrial infrastructures.https://doi.org/10.1038/s41598-025-11013-xHybrid energy managementSmart mining systemsDistributionally robust optimizationProcess couplingRenewable energy integrationBattery energy storage |
| spellingShingle | Dawei Wang Yifei Li Cheng Gong Tianle Li Fang Wang Shanna Luo Jun Li Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent Scientific Reports Hybrid energy management Smart mining systems Distributionally robust optimization Process coupling Renewable energy integration Battery energy storage |
| title | Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent |
| title_full | Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent |
| title_fullStr | Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent |
| title_full_unstemmed | Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent |
| title_short | Distributionally robust hybrid energy management in smart mining using process-coupled primal-dual mirror descent |
| title_sort | distributionally robust hybrid energy management in smart mining using process coupled primal dual mirror descent |
| topic | Hybrid energy management Smart mining systems Distributionally robust optimization Process coupling Renewable energy integration Battery energy storage |
| url | https://doi.org/10.1038/s41598-025-11013-x |
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