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Smart grid integration by optimizing energy, carbon, and production in industrial systems with contextual reinforcement learning
One-line summary
A solar energy research paper on Smart grid integration by optimizing energy, carbon, and production in industrial systems with contextual reinforcement learning.
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Industrial manufacturers switching energy paradigms must make timely, context-aware smart grid judgments. Most reinforcement learning (RL) demand response algorithms ignore the contextual interaction between pricing signals, machine states, and environmental variability, flattening rich industrial realities into static state-action mappings. Dynamic grids' cost and carbon optimization are limited. This gap is filled by an integrated, five-stage analytical pipeline for intelligent plant-grid interaction sets, Reinforcement-Based Contextual Demand-Response Optimization (CReDO-5). The pipeline begins with the Contextual Multimodal Grid-Plant Graph (COM-G³) which integrates price, weather, and process data into a temporal graph. Causal Counterfactual Replay Engine (CaCoRE) generates feasibility-aware counterfactual trajectories enabling accurate off-policy evaluation from the output. The Distributionally-Robust Multi-Objective Policy Search (DR-MOPS) module balances energy costs and CO₂ emissions in uncertain scenarios, using trajectories in process. The Market-Consistent Participation Calibrator (MCPC) ensures policy compliance with grid settlement and baseline criteria sets. Conformal-Safe Online Learning Controller (CSOLC) guarantees action-level safety in real-time adaptations. Using the proposed framework can reduce energy expenses by 15%, CO₂ footprint by 12%, and peak demand by 20% while maintaining production integrity in process. By merging causal reasoning, risk-aware optimization, and market alignment into a single RL framework, our work presents a new analytical foundation for robust, carbon-efficient industrial plant engagement in future smart grids.
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