The reserve is a service traded in the market to counteract unpredictable changes in system conditions. The efficient market-clearing procedure relies on economic criteria while maintaining the system security by means of a proper reserve management program. In this respect, there are several issues related to reserves that need to be assessed: (i) scheduling and allocation of reserves (ii) deployment or usage of reserves. In this dissertation, we formulate a market-clearing procedure that is in- tended to determine the reserve services required in an interconnected multi- area power system. First, the required reserves are determined implicitly using probabilistic criteria in an uncertain multi-area power system which is operated by a central system operator. Up to second order outages of generating units in a given area, double outages of generating units in different areas, and tie-line outages between interconnected areas are accounted for as possible uncertainties in the proposed model. Next, we provide a market-clearing procedure so that it is capable of de- termining the required reserves explicitly. Under this approach, the need for specifying any a priori reserve requirement is removed. Such proposed model is formulated as a two-stage stochastic programming problem. We use Bender’s decomposition approach to tackle the computational burden of the stochastic programming approach in the case with many scenarios. We then extend the model of electricity market-clearing, operated centrally, by proposing a decentralized market-clearing formulation capable of accounting for the system uncertainties, in particular wind production and equipment failures. The proposed decentralized algorithm relies on the decoupling of the first-order KKT optimality conditions of the original problem in such a way that the combination of the KKT conditions of all area sub-problems are identical to the KKT conditions of the original problem. The proposed model allows optimally dispatching the energy and reserve of each area of a multi-area system in a decentralized manner. Such model is relevant to the operation of the interconnected European electricity markets. Finally, we provide a decentralized procedure for clearing multi-regional electricity markets like electricity markets in U.S. where the unit commit- ment problem is solved for pool clearing. The aim of this methodology is to optimally schedule generating units while simultaneously determining the geographical allocation of the required reserve in the presence of wind power uncertainty. The proposed decentralized procedure relies on an augmented Lagrangian algorithm that require no central operator intervention but just moderate interchanges of information among neighboring regions. Numerical simulations and realistic case studies illustrate the performance of proposed market-clearing models.