| 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. |
| Key words: Ofshore wind turbine, Ofshore photovoltaic power, Deep reinforcement learning, Deep deterministic
policy gradient, Multi-objective optimal control |
| DOI：10.1186/s41601-023-00298-7 |
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| Fund:Not applicable |
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