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The necessity of power is growing in response to today's growing population. In order to reduce both real and reactive power losses, three devices have been combined in this research. The distributed generations (DGs) and static var compensator (SVC) with static synchronous compensator (STATCOM) play important role for reduction of power losses (both real and reactive power) and improvement of voltage profile. The research outcome presented here outlines that presents genetic algorithm (GA) may be utilised to improve power system performance and voltage profile (VP) of distribution networks by integrating DG, SVC and STATCOM planning properly. On a 37-bus distribution network, the proposed methodology has been applied. The results of this study are helpful to those focusing on the functionality of viable and upcoming electrical grids, as well as those working in the disciplines of DG, SVC and STATCOM integration for improved system performance. [ABSTRACT FROM AUTHOR]

This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive compensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm's capability to solve these nonlinear optimization problems with high decision-variable numbers. [ABSTRACT FROM AUTHOR]

The problems of increasing electricity consumption and atmospheric pollutants emissions are targeted as critical challenges for power transfer in modern distribution networks. In order to tackle this issue, many researchers have proposed the incorporation of Distributed Generator (DG) units, as well as different types of reactive power compensation devices, which will contribute to the increase of networks efficiency through loss minimization, voltage profile improvement, or even increasing transmission line's capacity. This paper addresses the optimal allocation/coordination of DG, Distributed Static VAR Compensator (D-SVC), and Distributed Thyristor-Controlled Series Compensator (D-TCSC) to reduce total power loss in the distribution network. The allocation problem is formulated as a mixed-integer nonlinear programming (MINLP) and is solved using a basic open-source MINLP (BONMIN) solver embedded in GAMS. The proposed method was tested in IEEE 16-bus and 33-bus distribution test systems, where two cases were considered. Firstly, BONMIN solver is used for allocation of D-SVC, and D-TCSC, respectively, where the results showed that incorporation of these devices leads to reduction of the active power losses, while the minimal losses and highest efficiency increase for both test networks occurred in the case where 3 D-TCSCs allocated. Secondly, a coordinated allocation of DGs and D-SVCs, as well as DGs and D-TCSCs, is presented, where it was shown that coordination of these devices leads to even greater loss reduction, i.e. increased efficiency. Also, it was concluded that DG and D-TCSC coordination is better suited for loss reduction in the 16-bus system, while DG and D-SVC coordination leads to greater loss reduction for the 33-bus system. Furthermore, the comparison between BONMIN and metaheuristic methods was performed for DG allocation and DG and D-TCSC coordination in IEEE 33-bus system, where it was concluded that BONMIN performs better in terms of minimal power loss, compared to the results which were presented in the available literature. Finally, the BONMIN solver's performance is evaluated using three parameters: number of nodes, number of iterations, and total execution time, where the numerical analysis showed that BONMIN is efficient in solving the addressed problems. [ABSTRACT FROM AUTHOR]

One of the prominent problems in wind farms is voltage flicker emission. To prevent flicker emission or mitigate the impact as best as possible, a static VAr compensator (SVC) is a great candidate both economically and technically. However, SVCs cannot completely compensate the fast-changing reactive power due to delays caused by the reactive power calculation unit and the triggering fire angle of the SVC. This paper proposes a predictive control system for SVCs, by merging an additional predictive control block into the conventional control system. It is constructed based on deep neural networks, namely adaptive one-dimensional convolutional neural network (1D-CNN). The training process is conducted based on the adaptive learning weights process to enhance the prediction accuracy and training computational complexity of the 1D-CNN. Numerical results on the actual dataset in a wind farm in Manjil, Iran, have verified the forecasting accuracy and flicker mitigation of the proposed controller. [ABSTRACT FROM AUTHOR]