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As the demand for renewable energy continues to increase, wind power has emerged as a prominent source of clean energy. However, incorporating wind energy into the power generation system at a high level can significantly impact the dynamic performance of the power system, resulting in increased uncertainties during operation. This study investigated the effectiveness of the Multi-Band Power System Stabilizer (MBPSS), a new power system stabilizer, in suppressing dynamic oscillations in a multi-machine power system connected to a wind farm. This research focused on analyzing the transient stability of a nine-bus network, commonly known as the Western System Coordinating Council (WSCC), integrated with a Doubly Fed Induction Generator (DFIG) using MATLAB/Simulink. The study evaluated the dynamic performance of the proposed system under fault conditions, including Line-to-Line-to-Line-to-Ground (LLLG) faults. Simulation results showed that MBPSS effectively dampened oscillations and improved the stability of the power system, even in the presence of severe faults and high-level integration of wind farms. [ABSTRACT FROM AUTHOR]

The distribution system acts as a conduit between the consumer and the bulk power grid. Due to characteristics such as a high resistance/reactance ratio, distribution networks cannot be solved using conventional methods, such as the Gauss-Seidel and Newton-Raphson. This research proposes a method for the calculation of the power flow in radial networks that considers their wide range of resistance and reactance values, PV generator characteristics, and radial structure. An iterative methodology is employed, with each iteration beginning with the branch that has the highest accurate power flow solution. The procedure is reliable and effective over various workloads and network configurations. To confirm the effectiveness of the suggested technique on the simple and IEEE 33-bus radial distribution system, simulations were carried out in MATLAB. The implications of including a renewable energy source, such as a PV generator, in the network under consideration are investigated by simulation result comparison. The optimal location of the PV generator was also determined using an Artificial Neural Network (ANN) controller. The results of the identification process improve the already exceptional efficacy and performance of the ANN controller. [ABSTRACT FROM AUTHOR]

One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine–cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques. [ABSTRACT FROM AUTHOR]

In this paper, a new method was proposed with the objective of maximizing the rate of wind power integration into the electric grid. This method was based on the optimization of the parameters of the turbine governors (TGs) by means of a genetic algorithm. The tuning of TGs' parameters was formulated relying on an objective function aiming at reaching the maximum wind power penetration rate. The dynamic grid modeling consisted of synchronous machines, regulators, and wind turbines. The IEEE 14-bus modified test system was adopted to test the grid using the Power System Analysis Toolbox. The simulation results revealed that, with the optimized TGs' parameters, the rate of wind power integration improved considerably. [ABSTRACT FROM AUTHOR]