Decentralized power generation from renewable energy sources (RES) is a long-term solution that addresses present environmental threats because of its widespread availability, sustainability, nonpolluting generation and eco-friendliness. The most widely used renewable distributed generation (RDG) are wind turbine (WT) and solar photovoltaic (PV) systems. But power generated from WT and solar PV systems is intermittent. Since wind speed and solar irradiance have random nonlinear generation patterns a suitable probabilistic method is adopted to model these uncertainties. A new hybrid grey wolf optimizer (HGWO) algorithm is proposed for optimal allocation of WT and solar PV systems in a distribution network considering the following constraints: discrete DG size limits, DG penetration limits, line loading capacity and bus voltage stability limits. The proposed method is tested on a 28-bus Indian distribution network which is located in Kakdwip, India. The results obtained by the proposed HGWO algorithm are presented and compared with the existing particle swarm optimization (PSO) algorithm and found to be better. © 2019 International Journal of Renewable Energy Research.