No abstract

As coherency of generators decreases, the risk of rotor angle instability increases, especially under severe contingencies. The slow coherency as a network characteristic may be controlled by the locations of committed generators. Unit commitment (UC) problem is conventionally carried out regarding operational and network constraints. In this study, a two-step strategy is developed to promote the slow coherency via the network constrained UC (NCUC) model on a daily horizon. First, conventional NCUC is executed. The most important generators with both economic and coherency merits are then determined as representative generators. In the second step, the Slow Coherency Based Unit Commitment (SCBUC) is reoptimisedaccording to the results obtained from the first step, using a multi-objective function. The first part of the multi-objective function is devoted to the cost of generation, start-up, and shutdown of generators. The goal of the second part of the multiobjective function is to maximise the coherency between the committed generators to reach a transient stability margin. The proposed model is converted to a mixed integer linear programming model. The performance of the proposed method of promoting transient stability is investigated using the dynamic IEEE 118-bus test system. t binary variable used to specify the electrical distance of generator i from its representative generator IET Gener. Transm. Distrib.

Reconfiguration is one of the most important functions in the distribution network's automation system. Reconfiguration is formulated as an optimization problem with a large number of scenarios, which demands high central processing unit time to check all of them. Therefore, it is necessary to utilize a high-efficiency optimization method. In this paper, the minimization of active power losses, total voltage deviations of buses, and maximization of system loading margin are integrated as three objective functions of the proposed reconfiguration model. Also, to improve the voltage profile, reduce power losses, and increase system loading margin, shunt capacitors (SCs) and distributed generations (DGs) are located. Seasonal daily load curves are applied to better simulate networks' real conditions. This paper uses the nondominated sorting genetic algorithm II, which generates a set of nondominated solutions. This set includes a wide range of solutions with different weighting coefficients. The multicriteria decision-making (MCDM) algorithm, as a powerful and flexible decision-making tool, is utilized to select the best solution based on tuning parameters. Also, it is assumed that DGs are wind farms, thus the uncertainty of DGs' power output is taken into the account, and the threepoint estimate method (3PEM) is utilized to reduce the number of subscenarios generated by 3PEM. Finally, the subscenario aggregation method is utilized to extract the value of objective functions in subscenarios. The developed model determines the optimal location of SCs and DGs in conjunction with the optimal network reconfiguration. The proposed method is implemented on the typical 33 and 69 bus radial distribution systems.