With increasing penetration of renewable energy sources, it is necessary to analyze its impact on the allocation of optimal power generation schedule. In this work, the pumped storage hydrothermal (PSHT) system incorporating solar units has been undertaken. The novel integrated heuristic approach named as time varying acceleration coefficient particle swarm optimization with mutation strategies (TVAC-PSO-MS) has been proposed. In this approach, an initial solution has been updated by the TVAC-PSO approach and then local best solutions are updated by using the successive mutation strategies namely Cauchy, Gaussian, and opposition based mutations. The Cauchy mutation strategy is applied to enhance the search capability and the Gaussian, as well as the opposition based mutation strategies are used to improve the exploitation capability of the algorithm. In order to validate the proposed approach, a standard test system of hydrothermal generation scheduling has been undertaken and the results have been compared with other state of art algorithms. Further, the proposed approach has been applied to optimize the cost of the PSHT system incorporating solar units and validate it through statistical test. (C) 2017 Elsevier Ltd. All rights reserved.