The need to transition to renewable energy sources (RESs) including wind, solar photovoltaic (PV), and hydro is highlighted by the accelerated depletion of fossil fuel reserves and the increasing need for sustainable power generation. In this regard, the present study explores the use of a probabilistic optimal power flow (POPF) framework that addresses the inherent uncertainties related to the power outputs of various RES technologies. The artificial hummingbird algorithm (AHA), inspired by hummingbirds adaptive foraging behavior, is used to solve the ensuing highly nonlinear, multi-modal problem. Several IEEE 57-bus test systems are used to thoroughly validate the efficacy of the suggested AHA-based POPF model. Comparative studies indicate that the AHA consistently provides notable cost and emission reductions, outperforming advanced metaheuristic approaches in all cases. When compared to coral reef optimization and gray wolf optimizer, the AHA reduces emissions by 1.74%–3.08% and costs by 0.27%–0.33% in the traditional IEEE 57-bus system. Additional gains are shown with cost reductions of up to 0.148% and emission reductions of up to 2.40% when RES units are integrated. AHA achieves the greatest results in the most complicated Test System 3, with an ideal generating cost of 4949 $ / h and emissions of 1.0299 t/h. The development of a comprehensive POPF system that concurrently incorporates wind, solar, and hydro power with precise uncertainty modeling and related cost penalties is what makes the study new. The superiority of the suggested solution over sophisticated modern approaches is further shown by extensive statistical assessment of 24-hour dynamic load tests across both IEEE 57-bus system as well as IEEE 118-bus system.

The days of fossil fuels are rapidly coming to an end. The best course of action is to concentrate on renewable energy sources (RES), which are beginning to make sense as an alternative to the current situation. This article's goal is to utilize and leverage combined RESs, such as solar, wind, and electric vehicle (EV) charging, along with some appropriate FACTS devices, such as Thyristor controlled series compensator (TCSC) and Thyristor controlled phase shifter (TCPS), to solve probabilistic optimal power flow (POPF) problems. The challenges are solved by using quasi-oppositional-based learning (Q-OBL) in conjunction with the optimization strategy known as the artificial hummingbird algorithm (AHA). Furthermore, adding FACTS, specifically TCPS and TCSC, can enhance the outcomes even further. A reliable and effective meta-heuristic optimization technique for handling challenging power system issues is QOAHA. The research study attempts to reduce the overall cost of generating by satisfying all equality and inequity requirements. The non-linearity of the problem is attributed to the existence of valve point loading, the thermal unit's limited working zone, and the variations in wind and sun. Three test systems have been used to evaluate the efficacy of the suggested QOAHA approach: test system 1, which includes a conventional POPF with generators at bus 1 (swing), 2, 3, 6, 8, 9, and 12. Second, considering test system 2, which comprises supplementing the existing thermal generators with RESs such as wind, solar PV cells, and EV charging units. Last but not least, implementing test system 3, which keeps the thermal generators in their customary placements while also integrating solar PV, wind power generation, and EV deployment with the addition of FACTS devices to arrive at the best possible global solution. The computed findings clearly demonstrate that QOAHA is still a useful tool for treating POPF issues, even when RES is used in conjunction with conventional techniques. In terms of the best possible solution to the objective functions and the rate of convergence, the proposed method outperforms other optimization techniques. The robustness of the recommended optimization strategy has been evaluated by statistical analysis. An analysis of variance (ANOVA) test is used to conduct this inspection in a thorough way so that the suggested technique's robustness may be assessed more accurately. A comparison with well-established optimization techniques has been done in order to address the superiority of the desired strategy.

Incorporating electric vehicles (EVs) into the power grid significantly impacts its safe and reliable operation, while the unpredictable nature of wind power adds further complications. Solar power, though less efficient in converting sunlight to electricity compared to wind power, remains a popular renewable energy source. Combining wind and solar energy is advantageous because wind energy can be harnessed both day and night, unlike solar energy. Tidal energy also offers a reliable renewable option, although it has its own set of challenges. Consequently, the utilization of renewable energy sources (RESs) have become increasingly complex. Fossil fuels, on the other hand, are a major cause of severe pollution. This study addresses integration of wind, solar, tidal, and electric vehicles, using a unique moth-flame optimization technique, to solve the challenge of hydrothermal scheduling (HTS). The primary objective is to reduce power generation costs while adhering to various limitations, including transmission losses, thermal unit valve point effects, and RESs variability. In order to maximize energy management, several EVs are currently being built as virtual power plants (VPPs), utilizing sustainable energy sources. So, VPPs and combined renewable energy sources make the micro-grid more rigid. The objective is to minimize fuel expenditures by balancing load demand and transmission losses while satisfying all conditions. By evaluating the generation costs with MFO, this study demonstrates the effectiveness of the method and compares it with other advanced optimization techniques, highlighting its superior efficiency, utility and reliability. When the performance of normal HTS system, RES and EV based HTS system are observed, it is clearly observed that RESs based system has improved the results by 5.49% as compared to the conventional system using the suggested COMFO approach. The findings also show that EVs can effectively contribute to a hydro-thermal scheduling system with integrated renewable energy by using grid power.

In this article, a new metaheuristic bioinspired technique oppositional artificial rabbit optimization (OARO) technique is enhanced and employed, for phasor measurement unit (PMU) placement in distribution system. Based on PMUs, this study emphasizes their advantages for real-time monitoring. Quite differently, the PMU serves as the system’s cornerstone. The goal of this kind of technology is to analyse data quickly and automatically arrive at a decision. Four scenarios are taken into consideration in the simulation study for each test system, and the goal is to minimize costs while taking into account fewer PMU. The first case focuses primarily on the number of PMU set as the objective function; the second case uses the wide area monitoring system (WAMS) data traffic index and installation cost to observe comprehensive observability. The third case focuses on how many PMU are used in conjunction with zero injection bus (ZIB). Finally, ZIB and WAMS are implemented together to reduce data traffic and, eventually, the fitness function. There is artificial rabbit optimization (ARO), inspired by the survival of rabbits, such as detour foraging and random hiding. The behaviour of the rabbits in random searching for food in other region neglecting its own region followed by random hiding amongst all borrowings to reduce the chances for the predators to search them to kill. In this study, an oppositional strategy of rabbits’ finding the tunnels is applied for finding the food search space. In addition, the rabbit energy shrink strategy is implemented to transmit rabbits from detour foraging and random hiding. The concept of oppositional-based learning applied to rabbit survival strategy has been mathematically modelled and tested in PMU placement problems in the radial distribution system. The methods are evaluated in radial distribution systems with 33, 69, 85, 118, and 141 buses. In each case, it is found that OARO provides better results on PMU placement for phasor measurement of voltage and current in radial distribution systems.

Due to the massive volume of data generated by the PMU implementation in the current power system during the data collection process, the data transmission system becomes overburdened. The trade-off between installation cost, communication congestion, and full system observability makes it difficult to decide where PMUs should be placed in large-scale transmission networks. Additionally, the placement of PMUs in the optimal placement has a significant impact on both installation costs and traffic congestion. The wide area monitoring system (WAMS) is a practical solution for this data system congestion. Additionally, the incorporation and integration of the zero injection bus (ZIB) into the current system may allow for a further decrease in the number of PMUs necessary to achieve full system observability. To achieve perfect observability in the PMU placement problem, the researchers in this study developed a hybrid quasi oppositional-based artificial rabbit optimization. In order to survive, rabbits use detour foraging, random hiding, and energy shrinkage. Rabbits imitate other foragers while disregarding their own strategies. This tactic helps with exploration. The rabbits can choose a random burrow from among their own borrows to hide in, lowering the likelihood that the predator would locate and capture them. These tactics assist in exploitation. A balance between exploration and exploitation is finally maintained by the energy shrink. In the current work, the authors used these special techniques to examine total observability, WAMS data traffic, ZIB, and cost installation index in the PMU placement problem. On the IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus, the proposed techniques have been tested. In order to demonstrate the superiority of the suggested technique in the white scenario, the computed results were compared with other published studies. The outcomes of the suggested methods also show a faster convergence and speedier data scenario.

Optimal Power Flow (OPF) is crucial for efficient and sustainable power system management, aiming to minimize operational costs and emissions while meeting system constraints. This paper introduces the artificial hummingbird algorithm (AHA) to solve the OPF problem, enhanced with Quasi-Oppositional Based Learning (QOBL) for improved convergence and solution accuracy. The proposed QOAHA is validated on the IEEE 57-bus system, demonstrating superior performance compared to existing optimization techniques in cost and emission reduction. By combining the exploration capability of AHA with QOBL’s accelerated search, the algorithm achieves robust and efficient results. This hybrid approach offers a promising direction for addressing complex power system challenges.

Aiming to reduce operating expenses and emissions while satisfying system restrictions, optimal power flow, or OPF, is essential for effective and sustainable power system management. Quasi-Oppositional Based Learning (QOBL) is added to the butterfly optimization algorithm in this research to improve convergence and solution accuracy when solving the OPF issue. When tested on the IEEE 57-bus system, the suggested QOBOA outperforms current optimization methods in terms of transmission loss and voltage profile improvement. Combining renewable energy sources is crucial for efficient electricity generation because fossil fuel sources are becoming more and more improved every day. The suggested solutions integrate renewable energy sources, such as tidal and electric vehicles, to reduce the demand for fossil fuels in the generation of electricity. Furthermore, the suggested approach has a great deal of promise for improving the adaptability and resilience of contemporary power grids, particularly in light of the growing integration of decentralized energy resources (DERs) and renewable energy sources. This strategy can help future smart grid systems operate more effectively, sustainably, and dependably by facilitating quicker decision-making and enhancing the coordination of dispersed assets.

The methodology of Oppositional Artificial Rabbit Optimization (OARO) has been effectively applied in this study to a single input power system stabilizer for the most effective tuning to lessen volatility at low frequencies. A single machine infinite bus (SMIB) system has been used to test the suggested algorithm’s efficacy using the Heffron-Phillips framework. To assert the relevance of OARO in an adaptable situation, the implementation of the suggested method is tested for a wide loading environment. The novelity and superiority of the present work has been validated by comparing the result with other work.

In this research work, the concept of the Chaotic Quasi-Oppositional Differential Search Algorithm (CQODSA) has been successfully implemented on a single input power system stabilizer for the optimum tuning so as to damp low-frequency oscillations. The Heffron-Phillips model has been considered to evaluate the efficiency of the proposed algorithm by incorporating it in a device with a separate infinite bus. The enactment in terms of the prototype algorithm is tested for wide-loading scenarios to claim the applicability of CQODSA under flexible scenarios. By contrasting the findings with those of other well-known algorithms, the superiority of the established method has been proven.

This research aimed to reconfigure radial distribution networks in the presence of distributed generators (DGs) using the Chaotic Quasi-Oppositional Moth Flame Optimization (CQOMFO) method so as to minimize power losses in the power system network and keep the voltage profile consistent throughout the power system network, which will aid in increasing system efficiency. The primary goal is to demonstrate the proper placement of Distributed Generators (DGs) in the radial distribution network, as well as the reconfiguration and installation of DGs in the radial distribution network. The main advantage of this algorithm is continuous guiding search with changing goals, which can be used for real-time applications with only minor adjustments because the power from distributed generation is constantly changing. This algorithm's efficiency and suitability for real-time applications have been determined by testing for loss minimization on typical 33- and 69-bus radial distribution systems.

In this research work, the concept of Chaotic Quasi-Oppositional Differential Search Algorithm (CQODSA) has been successfully applied to address the transient stability constraint optimal power flow problem. The effectiveness of the suggested algorithm has been evaluated on WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system. The recommended algorithm’s implementation has been evaluated for different fault conditions with the purpose of demonstrating CQODSA’s applicability in this versatile scenario. By contrasting the findings with those of other well-known algorithms, the superiority of the established method has been proven.

Transient stability constraint based optimal power flow (TSC-OPF) problem is a major area of research for the research community. A relatively new optimization approach namely, grey wolf optimization (GWO) has been proposed in this novel study to explain the TSC-OPF problem efficiently. Here authors tries his best to explain philosophy of this technique. In this analysis work, here the TSC-OPF problem has solved by comparing other techniques with GWO. The superiority of the proposed GWO technique of solving TSC-OPF problem being validated by considering case studies of New England 10- machine, 39-bus system and WSCC 3-machine, 9-bus system. The allusive after-effects announce that the proposed GWO access is abundant added computationally able than added accepted techniques and is able for solving TSC-OPF job.

Low frequency oscillation has been a major threat in large interconnected power systems. These low frequency oscillation curtains the power transfer capability of the line, thereby affecting the small signal analysis of the system and hence the performance of the system comes to a stake. Power system stabilizer (PSS) helps in diminishing these low frequency oscillations by providing auxiliary control signal to the generator excitation input. In this chapter, the authors have incorporated the concept of quasi-oppositional based learning (OBL) in butterfly optimization algorithm (BOA) to solve PSS problem. The proposed technique has been implemented on SMIB system and the supremacy of the suggested QOBOA accept has been accurated by different loading conditions to show the flexibility of QOBOA. The computed results thus obtained by the proposed techniques have been verified by comparing the results with those obtained by well published algorithms. The convergence characteristics as well authenticate the sovereignty of the considered algorithms.

In this research work, the concept of Chaotic Quasi-Oppositional Chemical Reaction Optimization (CQOCRO) has been successfully implemented on single input power system stabilizer for the optimum tuning so to damp low frequency oscillations. Heffron-Phillips model has been considered to check the effectiveness of the proposed algorithm by incorporating it in a single machine infinite bus (SMIB) system. The enactment of the proposed algorithm is tested for wide loading scenario to claim the applicability of CQOCRO under flexible scenario. By contrasting the findings with those of other well-known algorithms, the superiority of the established method has been proven.

This research aims to reconfigure radial distribution networks in presence of distributed generators (DGs) using the quasi-oppositional moth flame optimization (QOMFO) method in order to minimize power losses in the power system network and maintain a constant voltage profile throughout the power system network, which will aid in increasing system efficiency. The primary goal is to demonstrate the proper placement of distributed generators (DGs) in the radial distribution network, as well as the reconfiguration and installation of DGs in the radial distribution network. The primary benefit of this algorithm is continuous guiding search with changing goal, which can be used for real-time applications with only minor adjustments because the power from distributed generation is constantly changing. This algorithm has been tested for loss minimization on a standard 33 and 69 bus radial distribution systems, and the results show that it is efficient and suitable for real-time applications.

Optimal reconfiguration and reliability enhancement are the most significant objectives in the radial distribution systems.Because, enhancing the performance of power systems at different distribution level requires the rearrangement of network.For this purpose, this paper employs a Quasi-oppositional Moth flame Optimization (QOMFO) for improving the performance of radial distribution systems.The key factors of this work are to obtain the loss minimization.Also, to check feasibility proposed method has applied on 33-bus and 69-bus radial distribution systems.In addition to that, the obtained results are compared with some other conventional optimization techniques for proving the betterment of the proposed algorithm.

Many scientists are still concerned about power quality and minimising system losses. By lowering distribution losses, distributed generation (DG) increases overall electricity efficiency and quality. The approach used in this work employs chaotic quasi-oppositional moth flame optimisation (CQOMFO) to determine the appropriate scale of DG in the radial distribution system, hence minimising losses, lowering voltage deviance, and improving the voltage stability index. The aforementioned technique is put to the test on three separate test systems, which include buses of 33, 69, and 118. The multi-objective function has been significantly fine-tuned in order to gain a thorough technical understanding of the CQOMFO algorithm. The results of the computer simulations produced with the assistance of the scheduled approach are contrasted with the earlier optimisation methods put forth by several authors.

Optimization of system losses and quality power is still a major concern for many researchers. DG, i.e., Distributed Generation is a newly developed effective technology, when placed optimally in power system helps in raising overall efficiency and quality of power by minimizing distribution losses. This paper presents a methodology based on quasi-oppositional whale optimization algorithm (QOWOA) to locate DG with appropriate size in distribution system for reducing losses along with minimization of voltage deviance and enhancement of voltage stability index. The above-mentioned methodology is tested on three different test systems consisting of 33, 69 and 118 buses. The computer simulation results obtained using projected methodology is compared with the earlier optimization techniques proposed by various author.

This paper develops an improved version of the chemical reaction optimization (CRO) algorithm based on the opposition-based learning (OBL) strategy named quasi-oppositional CRO (QOCRO) for optimal reconfiguration of a power system to minimize power loss of the network. Furthermore, to avoid suboptimal solutions and to increase the convergence rate, chaotic behavior is mapped with QOCRO, which results in chaotic QOCRO (CQOCRO). The reconfiguration technique can minimize power loss up to a certain level. Further power loss reduction may be accomplished by locating the capacitor in the optimal location. To investigate the performance of the proposed CQOCRO, QOCRO, and CRO approaches, they are successfully implemented on two test systems, namely 33-bus and 69-bus radial distribution systems. Moreover, the numerical results are compared with other population-based optimization techniques like krill herd (KH) algorithm, oppositional krill herd (OKH) algorithm, and fuzzy approach. The computational results reveal that CQOCRO is superior to QOCRO, CRO, and other algorithms available in the literature in this domain. Finally, a convergence graph is given to identify the convergence superiority of CQOCRO.

This paper presents, an efficient optimization technique, namely chemical reaction optimization (CRO) algorithm is developed for power loss minimization in radial distribution system by optimal reconfiguration of the network. To check the feasibility and effectiveness the proposed methodology is successfully implemented on two test systems like 33-bus and 69-bus radial distribution systems. Moreover the numerical results are compared with other population based optimization technique like krill herd (KH) algorithm, oppositional krill herd (OKH) algorithm, fuzzy approach show that CRO could find better quality solutions. Finally, convergence graph is given to identify the robustness of above mentioned systems.