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Distribution network reconfiguration is one of the well-known and effective strategies in the distribution networks which performs by the status management of the network switches in order to obtain a new optimal configuration for the feeders. This study formulates multi-objective distribution feeder reconfiguration along with optimal sizing of distributed generators and capacitors. The prevalent objective functions in the network reconfiguration studies comprise of power loss and voltage deviations that are considered as the main objectives for traditional distribution systems, however, less attention has been paid to the objective functions of reliability and network voltage security in the previous literatures. Therefore, the main objective of this study is to improve the reliability and network voltage security by solving the distribution network reconfiguration problem. To this end, the energy not supplied and voltage stability index are defined as the objective functions of reliability and voltage security. A modified gravitational search algorithm is suggested to solve the complex and non-convex optimization problem. Ultimately, to demonstrate the efficiency of the proposed method; it has been tested on two 33 and 70-bus test systems, and the results are compared with the results of using other evolutionary algorithms, such as particle swarm optimization and shuffled frog leaping. [ABSTRACT FROM AUTHOR]

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature. [ABSTRACT FROM AUTHOR]

This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard realparameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixedinteger RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results. [ABSTRACT FROM AUTHOR]