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    Moth Swarm Algorithm Based Approach for the ACOPF Considering Wind and Tidal Energy
    (Springer International Publishing Ag, 2020) Duman, Serhat; Wu, Lei; Li, Jie
    In the last decades, optimal power flow (OPF) problem is becoming one of the most important nonlinear and non-convex problems for the planning and operation of large-scale modern electrical power grids. In this study, the OPF problem with the consideration of two types of renewable energy sources (RES), tidal and wind energy, is studied. The Gumbel PDF is used to calculate power output of tidal energy. The proposed OPF problem is solved by Moth Swarm Algorithm (MSA), and tested on an IEEE 30-bus test system via two different cases with and without the consideration of prohibited operating zones. The simulation results show the effectiveness of the MSA based OPF approach.
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    Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization
    (Wiley, 2020) Duman, Serhat; Rivera, Sergio; Li, Jie; Wu, Lei
    The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back-up unit (storage). Uncertain solar irradiance and wind speed are simulated via log-normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed-rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.
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    Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach
    (Springer London, 2019) Duman, Serhat; Li, Jie; Wu, Lei; Güvenç, Uğur
    Nowadays, the increasing usage of renewable energy sources (RES) in modern power systems introduces new challenges in power system planning and operation. Specifically, a high penetration of RESs introduces additional complexity into the optimal power flow (OPF) problem, which has a highly nonlinear complex structure. Under this environment, this paper discusses a modified hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) integrated with chaotic maps (CPSOGSA) to apply the composite benchmark test functions and to solve the OPF problem with stochastic wind power and flexible alternating current transmission system (FACTS) devices. Numerical studies are used to illustrate effectiveness of the proposed CPSOGSA approach against other approaches such as moth swarm algorithm, grey wolf optimizer, and whale optimization algorithm. Additionally, to demonstrate the superiority and robustness of CPSOGSA algorithm, Wilcoxon signed-rank test is applied for all case studies. Case studies indicate the potential of CPSOGSA method in effectively solving OPF problem with stochastic wind power and FACTS devices. © 2019, Springer-Verlag London Ltd., part of Springer Nature.