EndoWake: Modeling Wind by Linear Programming for Wind Farm Layout Optimization
The Wind Farm Layout Optimization problem consists of placing a given number of turbines within a given area so as to maximize energy production. One of its main challenges is the wake effect between turbines, which can strongly affect production and is a cumbersome function to evaluate and to embed in an optimization framework. In this paper we introduce a new endogenous wake model (EndoWake), based on the idea of treating the wind speed at every grid point as a decision variable, linked to its upwind neighbors through linear propagation constraints. This makes it possible to incorporate the wake equations directly into a Mixed-Integer Linear Programming (MILP) model. We then develop such a MILP model and, in particular, introduce a new class of valid "band" inequalities, showing that they substantially strengthen the root bound. Building on this, we design a branch-and-check scheme: the master problem retains only the site variables together with one hypograph variable per cell and scenario, integer solutions are evaluated by an exact linear-time oracle, and the optimality cuts are problem-specific "upwind cuts". Computational results are reported on a ladder of square grids under a four-sector axial wind rose, demonstrating the effectiveness of the proposed improvements. Preprint of a paper under review at an international journal (revised version of 11 September 2026); 30 pages plus 12 pages of supplementary material appended after the references. The replication package is at https://doi.org/10.5281/zenodo.22017424.
Authors
- Matteo Fischetti (ORCID: https://orcid.org/0000-0001-6601-0568)
- Martina Fischetti (ORCID: https://orcid.org/0000-0002-7673-6917)
Institutions
- University of Padua (IT)
- Universidad de Sevilla (ES)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-28
- DOI
- https://doi.org/10.5281/zenodo.23001174
- Primary Topic
- Wind Energy Research and Development
- Type
- preprint
Funders
- Agencia Estatal de Investigación