Open AccessA novel deep reinforcement learning enabled agent for pumped storage hydro‐wind‐solar systems voltage control
Author(s) -
Huang Qin,
Hu Weihao,
Zhang Guozhou,
Cao Di,
Liu Zhou,
Huang Qi,
Chen Zhe
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12311
Subject(s) - renewable energy , randomness , reinforcement learning , wind power , energy storage , computer science , electric power system , solar power , voltage , control theory (sociology) , automotive engineering , engineering , electrical engineering , power (physics) , control (management) , artificial intelligence , statistics , physics , mathematics , quantum mechanics
With the large‐scale penetration of wind and solar energies in the power system, the randomness of this renewable energy increases the non‐linear characteristics and uncertainty of the system, which causes a mismatch between renewable energy generation and load demand and it will badly affect the bus voltage control of distribution network. In this context, this study applies pumped storage hydroelectric (PSH) which tracks the load variation rapidly, operate flexibly and reliably to balance the power of the system to minimize the bus voltage deviation. Moreover, to obtain the optimal control policy of PSH, a deep‐reinforcement‐learning algorithm, that is, deep deterministic policy gradient, is utilized to train the agent to address the continuous transformation of the pumped storage hydro‐wind‐solar (PSHWS) system. The performance of a well‐trained agent was evaluated on the IEEE 30‐bus power system. Simulation results show that the proposed method achieves an improvement of 21.8% in cumulative deviation per month, which implies that it can keep the system operating in a safe voltage range more effectively.