Due to the existence and building of an important number of combined cycle plants throughout electric power systems around the world, there exists the growing need to have a more accurate model to represent these type of power plants when solving the unit commitment problem. A commonly used optimization technique to solve the unit commitment problem is dual programming. This work focuses on solving the subproblem of scheduling a combined cycle plant using dynamic programming under a dual optimization scheme. The model used to represent the combined cycle plants is based on configurations; this new model takes into account such constraints as the feasible transitions between configurations, and the minimum and maximum time that a combined cycle plant must remain on a certain configuration. This model accurately represents the reallife characteristics of combined cycle plants like different startup sequences and different stopping conditions. One novelty of this model is that the representation of each one of the states and configurations is done with a single integer state index that consecutively sums the time that the combined cycle plant must remain on each state or configuration. The use of this integer state index simplifies the state-space diagrams and reduces the number of integer/binary variables in the model. Another novelty is the modeling of Hybrid Combined Cycle Plants; these are the ones that use an auxiliary boiler in order to increase the production of steam.

A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT) ,battery, Micro- Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios.