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

Xiuxing Yin; Meizhen Lei

doi:10.1186/s41601-023-00298-7

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

Original Research | 更新时间：2026-09-30

- 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 Vol. 8, Issue 2, Pages: 1-11(2023)
- 作者机构：
  
  1. The State Key Laboratory of Water Resources Engineering and Management, Wuhan University,Hubei,Wuhan,China
  2. The School of Information Science and Engineering, Zhejiang Sci-Tech University,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-023-00298-7
  CLC：
- Published：01 April 2023
- 稿件说明：
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Xiuxing Yin, Meizhen Lei. Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-11.
DOI：

Xiuxing Yin, Meizhen Lei. Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-11. DOI： 10.1186/s41601-023-00298-7.

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.

Abstract

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated ofshore wind and photovoltaic (PV) power system for improving power generation efciency while simultaneously damping oscillations. A variable-speed ofshore wind turbine (OWT) with electrical torque control is used in the integrated ofshore 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 efectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the infow wind conditions to maximize total power generation. Signifcant oscillations in the overall power output can also be well suppressed by regulating the generator torque

which further indicates that complementary operation of ofshore wind and PV power can be achieved.

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