Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.

In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).

Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.

Optimization of Reactive Power Dispatch Considering DG Units Uncertainty By Dandelion Optimizer Algorithm

This article proposes a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) for solving optimization problems of optimal power flow (OPF) utilizing the renewable energy sources (RES) for minimizing different single-objective and multi-objective functions based on minimization of fuel cost, power losses of transmission lines, emission and voltage profile improvement. Also, mathematical formulation of (OPF) is introduced by converting the function with multiple objectives based on price and weighting parameters into a single objective function. Also, the effect of optimal RES is merged into the OPF problem. Notably, optimal RES placement yields even more effective solution. AOA was inspired by an intriguing physical law known as Archimedes' Principle. To prove the effectiveness of the AOA proposed algorithm, it compared with different recent algorithms for solving the optimal power flow problems and testing them to one standard system of the IEEE30-bus test system. The superiority of the proposed AOA algorithm is proven also by applying them on the IEEE30-bus modified system with optimal allocation of renewable energy source (RES). The results demonstrate that the proposed algorithm is more successful and efficient than the other optimization methods in the title of resolving OPF problems.

The interaction between the wind farm and the series‐compensated transmission line may lead to the sub‐synchronous oscillation (SSO) phenomenon which affects the system stability and equipment safety. Among the presented methods for damping the SSO phenomenon, the supplementary damping controller (SDC) is the most prevalent method because of its low cost, effectiveness, and simplicity. In all previous studies, one input control signal is fed to the SDC for mitigating the SSO. Here, two input control signals (active power and reactive power deviation) are applied to the SDC for enhancing the system stability and damping the SSO quickly. The ability of the proposed method for damping the SSO has not been investigated before. The proposed SDC is embedded into the q‐axis of the rotor‐side converter (RSC) inner current loop. The modified IEEE first benchmark model (FBM) is used to analyze the performance of the proposed method under different compensation levels, variable wind speeds, and sub‐synchronous control interaction (SSCI). Small‐signal stability using the eigenvalue approach is carried out, where the impact of the proposed method on sub‐synchronous mode, and super‐synchronous mode is investigated. The results successfully prove the superiority of the proposed method for damping the SSO under various operating conditions compared to some previous methods, where the least overshoot and faster convergence have been achieved by the proposed method in all studied cases.