Keeping a contingency reserve in power systems is necessary to preserve the security of real-time operations. This work studies two different approaches to the optimal allocation of energy and reserves in the day-ahead generation scheduling process. Part I presents a stochastic security-constrained unit commitment model to co-optimize energy and the locational reserves required to respond to a set of uncertain generation contingencies, using a novel state-based formulation. The model is applied in an offer-based electricity market to allocate contingency reserves throughout the power grid, in order to comply with the N-1 security criterion under transmission congestion. The objective is to minimize expected dispatch and reserve costs, together with post contingency corrective redispatch costs, modeling the probability of generation failure and associated post contingency states. The characteristics of the scheduling problem are exploited to formulate a computationally efficient method, consistent with established operational practices. We simulated the distribution of locational contingency reserves on the IEEE RTS96 system and compared the results with the conventional deterministic method. We found that assigning locational spinning reserves can guarantee an N-1 secure dispatch accounting for transmission congestion at a reasonable extra cost. The simulations also showed little value of allocating downward reserves but sizable operating savings from co-optimizing locational nonspinning reserves. Overall, the results indicate the computational tractability of the proposed method. Part II presents a distributed generation scheduling model to optimally allocate energy and spinning reserves among competing generators in a day-ahead market. The model is based on the coordination between individual generators and a market entity. The proposed method uses forecasting, augmented pricing and locational signals to induce efficient commitment of generators based on firm posted prices. It is price-based but does not rely on multiple iterations, minimizes information exchange and simplifies the market clearing process. Simulations of the distributed method performed on a six-bus test system showed that, using an appropriate set of prices, it is possible to emulate the results of a conventional centralized solution, without need of providing make-whole payments to generators. Likewise, they showed that the distributed method can accommodate transactions with different products and complex security constraints.

Operation of the electricity markets is subject to a number of strict and specific constraints such as continuous load-generation balance, security of supply, and generation technology related limitations. Contributions have been made to two important problems of the Electricity Markets, in the context of this study. In this study, Price Based Unit Commitment problem in the literature, which is a tool for the GENCO for operations planning, is extended considering the interdependencies between the Natural Gas (NG) and Electricity infrastructures and the uncertainty of Wind Power generation. The effect of the NG infrastructure physical limitations is considered via linearized NG transmission system equations, and the Wind energy sources and conventional generation resource uncertainties are simulated by Monte-Carlo simulations. The contribution of the forward energy Bilateral Contracts (BC), as a financial risk hedging tool is also included by modeling these in the proposed PBUC framework. In the case studies , it is observed that a GENCO could prevent its financial losses due to NG interruptions, by depositing only a portion of the midterm interrupted NG in the storage facilities. The Security Constrained Unit Commitment (SCUC) Problem is widely accepted tool in the industry which models the market clearing process. This study integrates two novelties to the SCUC problem / &bull / A discrete demand response model to consider active participation of the consumers, &bull / A hybrid deterministic/stochastic contingency model to represent the N-1 contingencies together with the uncertainties related with the wind power generation and system load. It is observed that the curtailment of available wind power capacity would enable the TSO to take corrective actions against occurrence of the contingencies and realization of the uncertainties in the most possible economical manner.