1 research outputs found
Stochastic Dynamic Programming for Wind Farm Power Maximization
Wind farms can increase annual energy production (AEP) with advanced control
algorithms by coordinating the set points of individual turbine controllers
across the farm. However, it remains a significant challenge to achieve
performance improvements in practice because of the difficulty of utilizing
models that capture pertinent complex aerodynamic phenomena while remaining
amenable to control design. We formulate a multi-stage stochastic optimal
control problem for wind farm power maximization and show that it can be solved
analytically via dynamic programming. In particular, our model incorporates
state- and input-dependent multiplicative noise whose distributions capture
stochastic wind fluctuations. The optimal control policies and value functions
explicitly incorporate the moments of these distributions, establishing a
connection between wind flow data and optimal feedback control. We illustrate
the results with numerical experiments that demonstrate the advantages of our
approach over existing methods based on deterministic models