Liberalisation of energy markets, climate policy and the promotion of renewable energy have changed the framework conditions of the formerly strictly regulated energy markets. Generating companies are mainly affected by these changing framework conditions as they are exposed to the different risks from liberalised energy markets in combination with huge and largely irreversible investments. Uncertainties facing generating companies include: the development of product prices for electricity as well as for primary energy carriers; technological developments; availability of power plants; the development of regulation and political context, as well as the behaviour of competitors. The need for decision support tools in the energy business, mainly based on operation research models, has therefore significantly increased. Especially to cope with different uncertain parameters, several stochastic modelling approaches have been developed in the last few years for liberalised energy markets. In this context, the present paper aims to give an overview and classification of stochastic models dealing with price risks in electricity markets. The focus is thereby placed on various stochastic methods developed in operation research with practical relevance and applicability, including the concepts of: stochastic processes for commodity prices (especially for electricity);scenario generation and reduction, which is important due to the need for a structured handling of large data amounts; as well asstochastic optimising models for investment decisions, short- and mid-term power production planning and long-term system optimisation. The approaches within the energy business are classified according to the above structure. The practical relevance of the different methods and their applicability to real markets is thereby of crucial importance. Shortcomings of existing approaches and open issues that should be addressed by operation research are also discussed.