Over the last decade, significant advancements have occurred in global electricity networks due to the widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation of solar photovoltaic and wind power stations and reduction of environmental hazards related to traditional power sources, they have also introduced various challenges to power network scheduling and operation. The traditional optimal power flow (OPF) problem, which is inherently complex, has become even more intricate with the integration of RES alongside traditional thermal power generators. This complexity arises from the unpredictable and intermittent nature of those resources. To tackle the intricacies of incorporating RES into conventional electric power systems, this study utilizes a pair of probability distribution functions to predict the power generation of wind and solar PV systems, respectively. The comprehensive OPF, which includes RES components, is expressed as a singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, and voltage deviations. To tackle this challenge, a novel hybrid metaheuristic optimization algorithm (ACGO) is introduced. The ACGO algorithm combines Chaos game optimization (CGO) with the artificial ecosystem-based optimization (AEO) method to obtain the optimum solution for the OPF problem considering stochastic RES. This technique aims to enhance solution precision by increasing solution diversity through an optimization process. The modified optimizer’s validation begins by examining its performance using well-known benchmark optimization functions, demonstrating its superiority over CGO, AEO, and other competitive algorithms. Subsequently, the modified optimizer is applied to a combined model of a wind and PV-incorporated IEEE 30-bus system. The ACGO technique proves to be highly effective, yielding the lowest fitness values of 781.1675 $/h and 808.4109 $/h in their respective scenarios for the modified IEEE 30-bus system. Additionally, the proposed ACGO method achieves the optimal total cost of 31623.5 $/h and 31601.55 $/h for the modified IEEE 57-bus system. These results emphasize the accuracy and robustness of ACGO in effectively addressing various instances of the OPF problem. The performance of ACGO in solving the OPF issue is verified through statistical boxplot comparisons, non-parametric tests, and robustness analyses. The evaluations indicate that the ACGO technique outperforms other well-known optimization algorithms in achieving the optimum values for the OPF problem involving stochastic PV and wind power systems. Additionally, the results show that ACGO offers faster convergence rates and higher precision in convergence compared to conventional artificial ecosystem-based optimization, Chaos game optimization, and other recent heuristic, metaheuristic, and hybrid optimization algorithms. The effectiveness of the ACGO technique has been proven to be robust and efficient, making it suitable for multidisciplinary problems and engineering optimization challenges.

Over the last twin decades, significant advancements have occurred in global electricity grids due to the widespread adoption of renewable energy resources (RES). While these sources play an essential role in total generation cost reduction, transmission power loss minimization, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a primary challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses’ in the nature is introduced. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. The obtained results demonstrate that the DMOA realized exceptional performance for both the test networks, with total generation cost minimized values of 780.982 ${\$}$ /h and 8283.942 US ${\$}$ /h, respectively. These results highlight the precision and robustness of DMOA in effectively addressing various instances of the OPF problem Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.

This paper presents a new approach for coordinated design of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controller. For this purpose, the design problem is considered as an optimization problem whose decision variables are the controllers’ parameters. Due to nonlinearities of large, interconnected power systems, methods capable of handling any nonlinearity of power networks are mostly preferable. In this regard, a nonlinear time domain based objective function is used. Then, the coyote optimization algorithm (COA) is employed for solving this optimization problem. In order to ensure the robustness and performance of the proposed controller (COA-PSS&SVC), the objective function is evaluated for various extreme loading conditions and system configurations. To show the contribution of the coordinated controllers on the improvement of the system stability, PSSs and SVC are optimally designed in individual and coordinated manners. Moreover, the effectiveness of the COA-PSS&SVC is assessed through comparison with other controllers. Nonlinear time domain simulation shows the superiority of the proposed controller and its ability in providing efficient damping of electromechanical oscillations.

The energy produced from various sources in modern power systems needs to be optimally planned for scheduling and managing the power system under specific conditions. In recent times, the world's growing population, increasing energy needs, technological advancements, environmental concerns, and global climate change have led to a rise in the demand for electric energy. One significant solution to meeting this energy demand is the use of renewable energy sources (RESs) in power systems. However, integrating RESs has made the construction of power systems more complex. Moreover, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The optimal power flow (OPF) is a challenging problem when dealing with integrating renewable energy sources (RESs) and plug-in electric vehicles (PEVs) into modern power systems. In this paper, we address the OPF problem that arises from power systems with a high penetration of controllable renewable sources and PEVs. We simulate uncertain solar irradiance, wind speed, and PEV behavior using Weibull, lognormal, and normal probability distribution functions (PDFs) respectively. The proposed stochastic OPF problem, which incorporates controllable renewable sources and Vehicle-to-Grid (V2G) technology, is addressed using the hybrid metaheuristic optimization algorithm (AHMRFO). The AHMRFO hybrid algorithm combines the exploratory power of the manta ray foraging optimization (MRFO) with the exploiting strength of artificial hummingbird algorithm (AHA) resulting in a more balanced, robust, and efficient optimization tool. It can effectively handle complex problems, avoid premature convergence, and provide high-quality solutions across a wide range of optimization challenges. Initial evaluations for the proposed algorithm are executed on 7 Congress on Evolutionary Computation (CEC) test functions and its obtained results was compared with recent optimization algorithms such as original MRFO, AHA, supply demand-based optimization (SDO), hunter prey optimization (HPO), weighted mean of vectors (INFO), and northern goshawk optimization (NGO). Moreover, simulations carried out on different test systems demonstrate the effectiveness and efficiency of the proposed AHMRFO algorithm when compared to other algorithms such as the MRFO and AHA. The AHMRFO technique proves to be highly effective, yielding the lowest fitness values of 781.5836 $/h and 808.3611 $/h in the first two scenarios for the modified IEEE 30-bus system with RES and it has the minimum fitness values of 790.2783 $/h and 819.6573 $/h in their respective scenarios for the modified IEEE 30-bus system with stochastic Vehicle-to-grid and RES. These results highlight the precision and strength of AHMRFO technique in effectively addressing various instances of the OPF problem. Additionally, the Wilcoxon signed-rank test has been employed to demonstrate the superiority, effectiveness, and robustness of the proposed AHMRFO algorithm.