The presented work addresses the design and optimization under uncertainty of power generation systems using renewable energy sources and hydrogen storage. A systematic design approach is proposed that enables the simultaneous consideration of synergies developed among numerous sub-systems within an integrated power generation system and the uncertainty involved in the system operation. The Stochastic Annealing optimization algorithm is utilized to handle the increased combinatorial complexity and to enable the consideration of different types of uncertainty in the performed optimization. A parallel adaptation of this algorithm is proposed to address the associated computational requirements through execution in a Grid computing environment. The proposed developments are implemented in a system that consists of photovoltaic panels, wind generators, accumulators, an electrolyzer, storage tanks, a compressor, a fuel cell and a diesel generator. Numerous design and operating parameters are considered as decision variables, while uncertain parameters are associated with weather fluctuations and operating efficiency of the employed sub-systems. The obtained results indicate robust performance under realizable system designs, in response to external or internal operating variations. (C) 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.