TY - JOUR
AB - Oceanic tides have the potential to yield a vast amount of renewable energy. Tidal stream generators are one of the key technologies for extracting and harnessing this potential. In order to extract an economically useful amount of power, hundreds of tidal turbines must typically be deployed in an array. This naturally leads to the question of how these turbines should be configured to extract the maximum possible power: the positioning and the individual tuning of the turbines could significantly influence the extracted power, and hence is of major economic interest. However, manual optimisation is difficult due to legal site constraints, nonlinear interactions of the turbine wakes, and the cubic dependence of the power on the flow speed. The novel contribution of this paper is the formulation of this problem as an optimisation problem constrained by a physical model, which is then solved using an efficient gradient-based optimisation algorithm. In each optimisation iteration, a two-dimensional finite element shallow water model predicts the flow and the performance of the current array configuration. The gradient of the power extracted with respect to the turbine positions and their tuning parameters is then computed in a fraction of the time taken for a flow solution by solving the associated adjoint equations. These equations propagate causality backwards through the computation, from the power extracted back to the turbine positions and the tuning parameters. This yields the gradient at a cost almost independent of the number of turbines, which is crucial for any practical application. The utility of the approach is demonstrated by optimising turbine arrays in four idealised scenarios and a more realistic case with up to 256 turbines in the Inner Sound of the Pentland Firth, Scotland.
AU - Funke,SW
AU - Farrell,PE
AU - Piggott,MD
DO - 10.1016/j.renene.2013.09.031
EP - 673
PY - 2014///
SN - 0960-1481
SP - 658
TI - Tidal turbine array optimisation using the adjoint approach
T2 - Renewable Energy
UR - http://dx.doi.org/10.1016/j.renene.2013.09.031
UR - http://www3.imperial.ac.uk/people/s.funke09
UR - https://www.sciencedirect.com/science/article/pii/S0960148113004989
VL - 63
ER -