Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

Protection and Control of Modern Power Systems

Original Research

Meizhen Lei¹  Xiuxing Yin² 

[1] The School of Information Science and Engineering, Zhejiang Sci-Tech University, 310018, Hangzhou, China;The State Key Laboratory of Water Resources Engineering and Management, Wuhan University, 430072, Wuhan, Hubei, China;

关键词: Offshore wind turbine;    Offshore photovoltaic power;    Deep reinforcement learning;    Deep deterministic policy gradient;    Multi-objective optimal control;   

DOI  :  10.1186/s41601-023-00298-7

 received in 2022-07-04, accepted in 2023-05-15,  发布年份 2023

来源: Springer

【 摘 要 】

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated offshore wind and photovoltaic (PV) power system for improving power generation efficiency while simultaneously damping oscillations. A variable-speed offshore wind turbine (OWT) with electrical torque control is used in the integrated offshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and effectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the inflow wind conditions to maximize total power generation. Significant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of offshore wind and PV power can be achieved.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

41116_2023_36_Article_IEq483.gif

41116_2023_36_Article_IEq482.gif

Fig. 3

Fig. 2

41116_2023_36_Article_IEq479.gif

41116_2023_36_Article_IEq478.gif

Fig. 3

41116_2023_36_Article_IEq476.gif

41116_2023_36_Article_IEq475.gif

Fig. 3

Fig. 3

41116_2023_36_Article_IEq473.gif

Fig. 2

41116_2023_36_Article_IEq471.gif

41116_2023_36_Article_IEq470.gif

Fig. 1

41116_2023_36_Article_IEq468.gif

41116_2023_36_Article_IEq467.gif

41116_2023_36_Article_IEq466.gif

Fig. 4

41116_2023_36_Article_IEq465.gif

41116_2023_36_Article_IEq464.gif

Fig. 2

41116_2023_36_Article_IEq461.gif

Fig. 1

41116_2023_36_Article_IEq459.gif

Fig. 3

41116_2023_36_Article_IEq438.gif

41116_2023_36_Article_IEq410.gif

41116_2023_36_Article_IEq407.gif

41116_2023_36_Article_IEq385.gif

41116_2023_36_Article_IEq359.gif

41116_2023_36_Article_IEq341.gif

Fig. 2

41116_2023_36_Article_IEq307.gif

41116_2023_36_Article_IEq294.gif

41116_2023_36_Article_IEq276.gif

41116_2023_36_Article_IEq255.gif