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 study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

In the case of partial shading conditions (PSCs), normal equations cannot be completely implemented. The mathematical model of the Photovoltaic (PV) array needs to be modified and re-established with the existence of bypass diodes connected to the PV module, which can alleviate the negative effects of the PSCs and generate several peaks on the PV output characteristics curve. The first aim of this study is to modify and re-establish the mathematical model of the PV array under PSCs. Second, it aims to improve and validate the reliable Cuckoo Search Algorithm (CSA) by integrating it with PID (hybrid CSA-PID) to diminish the impact of PSCs problems. The hybrid CSA-PID was proposed to both track the global maximum power point (GMPP) of PV systems and reduce the tracking time to eliminate the fluctuations around the GMPP. Further, the PID controller was used to eliminate the error percentage obtained by CSA under PSCs to generate the required duty cycle, which provides the required and desired maximum voltage accordingly. The proposed CSA-PID technique has been implemented using both Matlab/Simulink and Hardware-In-Loop experiments on the MT real-time control platform NI PXIE-1071. For validation, the Hybrid CSA-PID method is evaluated and compared with CSA, modified particle swarm optimization (MPSO), PSO, and modified perturb and observe (MP&O) methods under similar conditions. Finally, the obtained findings demonstrated the efficacy and superiority of the proposed hybrid CSA-PID technique, demonstrating its resilience, fast reaction, and good performance in terms of tracking time and GMPP tracking.