The rapid integration of distributed renewable energy and flexible loads significantly intensifies supply and demand uncertainty in active distribution networks (ADNs), threatening economic and secure grid operations. Existing deep reinforcement learning (DRL) dispatch methods fail to extract spatial features properly,...
Hu-Cheng Li, Li-Fei Sun, Haifeng Fan et al.· Electronics· 0 citations
In the context of large-scale integration and long-distance power export of new energy bases, the strong volatility and uncertainty of wind and solar generation intensify the operational risks of the power grid under extreme contingencies. To address the increasing requirements for regulation capability and resilience...
Ling Ji, Ling Hao, Heng Chi et al.· Processes· 0 citations
Against the backdrop of the “dual-carbon” goals, the penetration of distributed photovoltaics (PV) in distribution networks continues to increase. Under the combined effects of stochastic PV generation and load variability, assessing PV hosting capacity in distribution networks under fault conditions has become increas...
Constructing wind and solar energy bases is an effective way to promote the green transformation, and the optimal dispatch of renewable energy bases is essential to their high-quality development. However, wind and solar generation are characterized by intermittency, fluctuations, and unpredictability, which pose new c...
