TY  - EJOU
AU  - Xiao, long 
AU  - Zhang, Linghao 
AU  - Guo, Ziran 
AU  - Lu, Xiaoxing 
AU  - Xie, Wenqiang 
AU  - Lv, Shukang 
AU  - Li, Peishuai 

TI  - Data-Driven Voltage Control for Distribution Networks with High Penetration of PVs via Improved DDPG
T2  - Energy Engineering

PY  - 
VL  - 
IS  - 
SN  - 1546-0118

AB  - The highly increasing integration of distributed photovoltaics (PVs) has brought significant challenges for distribution network voltage control, which struggles with fast and stochastic fluctuations. This paper proposes a data-driven voltage control method utilizing an improved deep deterministic policy gradient (DDPG) to minimize power losses while maintaining voltage within a safe operating range. The data-driven voltage control framework is established first, with the controlling center formulated as the agent. Based on the distribution network system and the voltage control original model, the data-driven voltage control model is formulated via the Markov decision process (MDP). A voltage sensitivity-guided DDPG algorithm, which incorporates the voltage-reactive power injection sensitivity matrix into the actor policy gradient, is developed to embed physical grid knowledge and reduce the actor’s reliance on critic action-gradient estimates, thus the practicality and generalization of data-driven voltage control is improved. Numerical simulations on the modified IEEE 33-node test system show the method maintains all nodal voltages within limits and reduces total system losses under varying PV generation, demonstrating its effectiveness and potential for future sustainable power systems.
KW  - Distributed photovoltaics (PVs); distribution network; voltage control; deep reinforcement learning (DRL); improved DDPG

DO  - 10.32604/ee.2026.076751