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Due to the non-linear nature of most of the loads, there is a greater demand for reactive power in the distribution network. Further, it wasted high energy on distribution and dropped the bus voltages. The integration of the distribution static synchronous compensator (DSTATCOM) mechanism into the power distribution sector increases load capacity, compensates reactive power, and reduces power losses while enhancing the voltage profile and stability. This research utilized the improved bacterial foraging search algorithm (IBFA) for the optimum sizing and placement of the DSTATCOM in the distribution sector to minimize power losses and improve voltage stability and profile. The power flow model is developed using the back-forward load flow technique. The outputs obtained are compared with those from the conventional bacterial foraging algorithm (BFA) and the base case scenario. The optimum size of DSTATCOM computed is 3697.3kVAr with the optimal placement at bus 19. The percentage drop in power losses obtained with the installation of DSTATCOM using an improved bacterial search algorithm (IBFA) is 49.021% as compared to the base case scenario. Similarly, the IBFA approach resulted in a 12.8% enhancement in voltage stability and a 3.32% improvement in voltage profile as compared to the base case scenario. The results obtained clearly indicate the efficiency and applicability of the applied IBFA technique to the optimum placing and sizing of DSTATCOM in the radial power distribution sector for minimizing power loss and improving the voltage profile and stability. [ABSTRACT FROM AUTHOR]

Summary: During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs. [ABSTRACT FROM AUTHOR]

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure. [ABSTRACT FROM AUTHOR]