Abstract
This paper presents a novel spatiotemporal optimization approach for
maximizing the output power of an ocean current turbine (OCT) under
uncertain ocean velocities. In order to determine output power, ocean
velocities and the power consumed and generated by an OCT system are
modeled. The stochastic behavior of ocean velocities is a function of
time and location, which is modeled as a Gaussian process. The power of
the OCT system is composed of three parts, including generated power,
power for maintaining the system at an operating depth, and power
consumed for changing the water depth to reach the maximum power. Two
different algorithms, including model predictive control (MPC) as a
model-based method and reinforcement learning (RL) as a learning-based
method, are proposed to design the optimization structure, and
comparative studies are presented. On one hand, the MPC based controller
is faster in finding the optimal water depth, while the RL is also
computationally feasible considering the required time for changing
operating depth. On the other hand, the cumulative energy production of
the RL algorithm is higher than the MPC method, which verifies that the
learning-based RL algorithm can provide a better solution to address the
uncertainties in renewable energy systems. Results verify the efficiency
of both presented methods in maximizing the total power of an OCT
system, where the total harnessed energy after 200 hours shows an over
18% increase compared to the baseline.