Search

Microgrid can operate in both grid-connected and islanded modes. Islanding can be scheduled or unscheduled. In the event of unscheduled islanding, the microgrid should be able to maintain its sustainability. Therefore, it is necessary to determine and supply the amount of required spinning reserve for stable islanding. In this paper, a two-stage strategy for optimal scheduling of the microgrid is proposed while uncertainties of renewable generations and demand are considered. In the first stage, co-optimization of power and reserve is carried out with the presence of distributed generations and batteries. Therefore, in the second stage, the demand is also used as reserve to provide required reserve for stable islanding. In order to determine the optimal amount of reserve of demand, a novel objective function is proposed. The objective function minimizes the sum of the standby reserve cost, expected cost of called reserve, and expected cost of emergency curtailment. One of the salient features of the proposed model is its modularity. That is, it can be added easily to any scheduling model in which the demand is not considered as reserve. Mathematical analysis and numerical simulations show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In the single-mode resource-constrained project scheduling problem, it is assumed that each activity has a specified known execution time and resource consumption can be done only in one way. In practice, there are many cases in which the make span can be reduced by providing additional resources activities. In this case, each activity can be done in one of the procedures, which is called the multi-mode resource-constrained project scheduling. In this paper, the problem includes determination of the basic schedule for the project activities which may be done in several models and the precedence relations are met, While the project make-span, cost and resource fluctuations are minimal. In this research project scheduling problem network will model using ED simulation software and the results of the simulation and a meta-heuristic algorithm has been compared. Finally, management strategies to optimize the scheduling, i.e such minimize total time, cost and resource leveling, will be offered. [ABSTRACT FROM AUTHOR]