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Low-voltage (LV) and high-voltage (HV) DC distribution systems are being investigated as alternatives due to the growth of DC distribution energy resources (DER), DC loads such as solar and wind power systems, and energy storage sources (ESSs). Furthermore, an HV/LV DC distribution system offers various advantages, including lower conversion losses, an easier connecting strategy for DC DERs, and less complex power management techniques. As renewable energy sources are increasingly incorporated into the electrical grid, it is important to create novel, effective approaches for connecting such sources and loads. It would hence be effective to merge DC distribution with AC distribution to fulfill the energy demands of both DC and AC consumers. To this end, this study proposes a multizone design with four buses: low-voltage direct current (LVDC), high-voltage direct current (HVDC), low-voltage alternating current (LVAC), and an electrical grid. A model of this system that covers crucial elements, including power systems, DER systems, and power electronic devices, to serve as a foundation for the analysis and design of this architecture is proposed. MATLAB/Simulink is used to conduct a simulation study to verify the performance of the proposed design. In this study, a hybrid electrical grid with an LVDC, HVDC, and LVAC distribution network test is used and implemented. Additionally, a transient and steady-state characteristic analysis of the test system is performed. [ABSTRACT FROM AUTHOR]

Combined economic emission dispatch (CEED) problems are among the most crucial problems in electrical power systems. The purpose of the CEED is to plan the outputs of all production units available in the electrical power system in such a way that the cost of fuel and polluted emissions are minimized while respecting the equality and inequality constraints of the system and efficiently responding to the power load required. The rapid depletion of these sources causes limitation and increases the price of fuel. It is therefore very important that scientific research in the last few decades has been oriented toward the integration of renewable energy systems (RES) such as wind and PV as an alternative source. Furthermore, the CEED problem including RES is the most important problem with regard to electrical power field optimization. In this study, a classification of optimization techniques that are widely used, such as traditional methods, non-conventional methods, and hybrid methods, is summarized. Many optimization methods have been presented and each of them has its own advantages and disadvantages for solving this complex CEED problem, including renewable energy. A review of different optimization techniques for solving this CEED problem is explored in this present paper. This review will encourage researchers in the future to gain knowledge of the best approaches applicable to solve CEED problems for practical electrical systems. [ABSTRACT FROM AUTHOR]

This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. This approach is applied for the stabilization of a multimachine power system subject to disturbances. In this case, a state-feedback controller based on parallel distributed compensation (PDC) is applied for the stabilization of the fuzzy system, where the design of control laws is based on the Lyapunov function method and the stability conditions are solved using a linear matrix inequalities (LMI)-based framework. Due to the high number of system nonlinearities, two steps are followed to reduce the number of fuzzy rules. Firstly, the power network is subdivided into sub-systems using Thevenin's theorem. Actually, each sub-system corresponds to a generator which is in series with the Thevenin equivalent as seen from this generator. This means that the number of sub-systems is equal to the number of system generators. Secondly, the significances of the nonlinearities of the sub-systems are ranked based on their limits and range of variation. Then, nonlinearities with non-significant variations are assumed to be uncertainties. The proposed strategy is tested on the Western systems coordinating council (WSCC) integrated with a wind turbine. The disturbances are assumed to be sudden variations in wind power output. The effectiveness of the suggested fuzzy controller is compared with conventional regulators, such as an automatic voltage regulator (AVR) and power system stabilizers (PSS). [ABSTRACT FROM AUTHOR]

This study focused on the use of the non‐dominated sorting genetic algorithm (NSGA‐II) to achieve an optimal power system stabiliser (PSS) parameters for a given operating point with a renewable source of energy so as to increase the system damping and guarantee enough stability margin. The parameters tuning was formulated using an eigenvalue‐based multi‐objective function. In recent years, the changeability and fluctuation of the wind power injected into the network have led to new challenges in small signal stability (SSS). These wind speed change and load demand change perturbations take place routinely and cause the variation of the operating conditions. However, as the conventional PSS is conceived for a fixed operating point in order to obtain the linearised transfer function model, it cannot yield good results when the operating range is too wide. To this end, the adaptive neuro‐fuzzy inference system was proposed to estimate the stabiliser parameters in real time after a learning phase. The nine‐bus Western System Coordinating Council and the obtained simulations results were assessed using Matlab/Simulink package. The validity of the proposed methodology was checked through the PSS parameters evolution simulation for daily load forecast curves and monthly wind speed prediction curves. [ABSTRACT FROM AUTHOR]