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In today's transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as the imbalance between generation and demand-side (consumption) leading to increasing the cost of operating the power system. On the other hand, distributed generators (DGs) in distribution networks (DNs) can be a relatively good alternative to-the-disconnected GUs with their ability to react quickly. Therefore, robust cooperation-for integrated TN&DN is essential. However, it is impossible to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators' independence, and the information privacy. Thus, in this paper, a linearized ACOPF model for the robust cooperation of TN&DN problem is presented along with an efficient hierarchical decentralized solution method to solve the robust cooperation of integrated TN&DN in which the information-privacy-is-maintained-for both network operators. In this paper, the numerical simulations are analyzed for standard IEEE 30/118-bus transmission and IEEE 33/69-bus distribution networks. Finally, the paper validates through simulations the accuracy and computational-efficiency-of the proposed robust optimization problem and hierarchical decentralized method. [ABSTRACT FROM AUTHOR]

In this paper, to solve the multi-objective problem of distribution feeder reconfiguration (DFR) in the presence of distributed generation (DG), the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been proposed, which is a combination of particle optimization (PSO) and gravitational (GSA) optimization algorithm. In this field, the power losses and operating costs are the two most used objective functions in the literature. In addition to the mentioned objective functions, this paper also considers the optimal generation capacity of DG resources and energy not supplied (ENS), which is one of the basic reliability indexes of distribution networks. In this paper, the values of different objective functions are normalized by the fuzzy method, and also the Fuzzy decision-maker is used to determine the most optimal solution among the Pareto-optimal solutions. The proposed algorithm is implemented on IEEE 70-bus and 119-bus test systems. The simulation results show the efficiency of the proposed PSOGSA in improving the considered objective functions. The proposed method, by establishing a suitable fit between different objective functions has introduced a more efficient structure with lower losses and operating costs, as well as greater reliability, compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]