Solar energy paper index
Enhancing Virtual Inertia Control in Microgrid Clusters: A Novel Frequency Response Model Based on Deep Reinforcement Learning
One-line summary
A solar energy research paper on Enhancing Virtual Inertia Control in Microgrid Clusters: A Novel Frequency Response Model Based on Deep Reinforcement Learning.
Engineering notes
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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
Traditional control strategies, such as droop-frequency and PI controllers, often show limited adaptability when the system operates under highly variable renewable generation and load conditions. In order to overcome this limitation, this study proposes the design and simulation of a clustered microgrid supported by reinforcement learning (RL)-based virtual inertia control. Three continuous-control RL algorithms were evaluated: Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The SAC agent provided the most robust training performance, reaching stable convergence after approximately 400 episodes and a final reward close to 86 units after 600 episodes. DDPG presented the second-best behavior, whereas TD3 achieved the lowest final reward, approximately 43 units. The proposed Agent SAC-Reinforcement Learning control was tested on a two-area microgrid cluster, which demonstrated greater frequency stability. Results indicate a frequency nadir of 59.82 Hz and ROCOF of 0.1484 Hz/s, with 6.78% nadir deviation improvement and 37.23% ROCOF reduction compared to PID-based strategies.
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