Mock catalogues are a crucial tool in the analysis of galaxy surveys data, both for the accurate computation of covariance matrices, and for the optimization of analysis methodology and validation of data sets. In this paper, we present a set of 1800 galaxy mock catalogues designed to match the Dark Energy Survey Year-1 BAO sample (Crocce et al. 2017) in abundance, observational volume, redshift distribution and uncertainty, and redshift-dependent clustering. The simulated samples were built upon HALOGEN (Avila et al. 2015) halo catalogues, based on a 2LPTdensity field with an empirical halo bias, For each of them, a light-cone is constructed by the superposition of snapshots in the redshift range 0.45 < z < 1.4. Uncertainties introduced by so-called photometric redshifts estimators were modelled with a double-skewed-Gaussian curve fitted to the data. We populate haloes with galaxies by introducing a hybrid halo occupation distribution-halo abundance matching model with two free parameters. These are adjusted to achieve a galaxy bias evolution b(z(ph)) that matches the data at the 1 sigma level in the range 0.6 < z(ph) < 1.0. We further analyse the galaxy mock catalogues and compare their clustering to the data using the angular correlation function w(theta), the comoving transverse separation clustering xi(mu < 0.8)(S-perpendicular to) and the angular power spectrum C-l, finding them in agreement. This is the first large set of three-dimensional {RA,Dec.,z} galaxy mock catalogues able to simultaneously accurately reproduce the photometric redshift uncertainties and the galaxy clustering.