This thesis aims to develop an optimal control strategy for the DOT 500kW Pilot Reverse Osmosis
(DOT500PRO) turbine system. The system integrates a 500 kW wind turbine with a reverse
osmosis (RO) module to produce freshwater. The primary goal is to maximise revenue generation
by optimising the turbine’s state transitions based on wind predictions.

The thesis begins with an analysis of the DOT500PRO and its state machine, identifying operational states, transitions, and constraints. A Markov model is used to model and predict wind
speeds, which fits nicely with the Markov Decision Process (MDP) framework. The problem
is formulated as an MDP, and multiple control strategies, including Threshold Control, Model
Predictive Control (MPC), Stochastic Dynamic Programming (SDP), and Approximate Dynamic
Programming (ADP), are evaluated.

MPC is found to be computationally intensive, making it less feasible for real-time control. SDP
shows promising results, but is limited by the curse of dimensionality, restricting the use to higherorder models. ADP, which approximates SDP solutions, can offer a potential controller for higherorder models but requires further tuning and optimisation.

Simulations are conducted to compare the performance of these control strategies in several
scenarios. While SDP demonstrates slight improvements over threshold control on the training
dataset, its performance on different wind patterns is less consistent. The study concludes that
while proactive control strategies such as SDP and ADP can offer improvements over reactive
methods, their performance is dependent on the accuracy of wind predictions and the specific
operational conditions.

Future work suggestions include refining the turbine and wind models, exploring adaptive control
methods, and conducting real-life experiments to validate the control strategies, which are crucial
for practical implementation and optimisation.