Penetration of renewable energy generators into energy systems is increasing. The intermittency and variability of these generators makes supplying energy reliably and cost effectively difficult. As a result, storage technologies are proposed as a means to increase the penetration of renewable energy, to minimize the amount of curtailed renewable energy, and to limit the amount of back-up supply. Therefore, methods for determining an energy system’s storage requirements are being developed. This thesis investigates and details four existing methods, proposes and develops a fifth method, and compares the results of all five methods. The results show that methods which incorporate cost, namely the Dynamic Optimization and the Abbey method, consistently yield the most cost effective solutions. Under excellent renewable energy conditions the results show that the cost-independent methods of Korpaas, Barton, and the Modified Barton method produce solutions that are nearly as cost effective but have greater reliability of energy supply than the Dynamic Optimization and Abbey solutions. This thesis recommends a new path of research for the Modified Barton method:​ the incorporation of cost through the confidence level. This thesis also recommends the development of new sizing methods from various aspects of the methods presented.