Osmotic Power is a completely renewable and emission-free energy source, and Pressure Retarded Osmosis (PRO) is one of the possible conversion technologies. The development to the current state of PRO has been led by Norway's Statkraft S.A. and a demonstration plant was opened in Tofte, Norway in November 2009. The goal of this study is to examine the PRO technology in order to: ? Understand and describe the principles, processes components and preconditions, ?Determine the theoretical, technical and realisable potential and analyse the supporting and limiting factors for a PRO implementation in Croatia, ? Analyse and estimate the economic parameters for a business case scenario and possibly apply these to a potential site for a PRO pilot power plant. With information available from public sources (internet, scientific articles), and after direct communication with Statkraft and other experts from relevant industries, probable investment and production costs are calculated for various scenarios. Finally, a promising site for a PRO pilot plant implementation in Croatia is selected, based on favourable environmental and infrastructure conditions. Based on a site visit, some ROI calculation parameters are adapted and the specific investment case analysed. The calculations results are put in relation to suggested feed-in tariffs that would support the PRO dissemination within the Croatian subsidy framework for renewable energy. The calculated costs of energy (LCoE) for PRO are found in the range between the costs of wind and photo voltaic, although key technology components like membranes are still in an early stage of development. In order to reach the expectations stated by Statkraft, these calculated costs will have to half until 2020. The main future challenge will be to increase the specific power of membranes and to decrease their specific costs. Enormous production capacities for membranes will be required to build PRO power plants with capacities relevant for utilities. The necessary growth of the production capacity can in fact create the steep learning curve that is required for the predicted cost decrease.