Optimisation of wind farm location using mean variance portfolio theory and time series clustering
The variability associated with wind energy generation can be mitigated by applying optimisation strategies to determine the geographical locations and sizes of wind power plants. Mean variance portfolio theory has been proposed as in the literature for optimising wind farm locations, but applying it to a large geographic area with many wind speed datasets yields a solution that may not be practically realisable. This paper presents the results of a study to optimise the location of wind farms with view to reduce the variability of the available power profile, using a mean variance portfolio optimisation strategy in conjunction with a pre processing clustering algorithm, in order to generate practicable solutions. The results show that using clustered datasets yields near optimal solutions in terms of minimised variability, while also highlighting the scope for an increased energy yield from an optimally sized and located wind farm distribution.
2016 IEEE International Conference on Power and Energy (PECon)