TL;DR
A bi-level optimization framework coupling Bayesian optimization with deep reinforcement learning is proposed, which offers a practical path toward uncertainty-aware IES co-optimization.
Experimental results demonstrate that the proposed DRL-based framework provides an effective and scalable solution for intelligent microgrid energy management and outperforms conventional rule-based strategies and model-based optimization approaches in terms of operational cost reduction, energy utilization efficiency,...
An imitation-learning-based hierarchical proximal policy optimization strategy is developed to decompose the scheduling task into system-level energy coordination and device-level action execution, which achieves the fastest convergence compared with the three benchmark methods.
This paper proposes a novel Reinforcement Learning–Based Intelligent Control (RL-IC) algorithm for energy-efficient and emission-aware optimization in industrial power systems. Unlike conventional rule-based or model-predictive controllers that rely on fixed dynamics, RL-IC employs a hierarchical dual-objective learnin...
The storage utilization and power supply self‐sufficiency rates under the PPO strategy significantly outperform traditional methods, validating the efficacy of the proposed architecture in enhancing both the economic viability and operational performance of the HESS.
A Deep Reinforcement Learning–Enhanced Dynamic Optimization Framework (DRL-DOF) that integrates uncertainty-aware policy optimization, temporal-attention actor–critic networks, and digital twin simulation for precision investment management is presented.
This work investigates a coupling-aware, decentralized formulation of the on-policy SARSA (State–Action–Reward–State–Action) algorithm for real-time HVAC control in multi-zone open-plan offices and indicates that the approach is computationally compatible with resource-constrained building energy management system (BEM...
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