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This paper presents an optimal active power dispatch (OAPD) problem that, unlike common economic dispatch problems, precludes unwanted mismatches on realistic power systems. The OAPD is formulated by considering the unified power flow controller (UPFC), a versatile device from the flexible AC transmission systems. However, the resultant turns into a highly nonlinear and complex optimization problem, which requires a powerful evolutionary algorithm to determine the optimal solutions. Toward this end, this paper explores the use of comprehensive learning particle swarm optimization and differential evolution as a hybrid configuration in a fuzzy framework, called hybrid fuzzy-based improved comprehensive learning particle swarm optimization-differential evolution, to address the proposed problem. To demonstrate the performance of the proposed algorithm, a set of benchmark problems, including real-world constrained optimization problems as well as a profound analysis of Schwefel problem 2.26 are provided. Moreover, to authenticate its effectiveness in solving power and energy-related problems with quite a few decision variables, four different power systems, 3-unit, 6-unit IEEE 30-bus, 10-unit, and 40-unit systems, are implemented. The IEEE 30-bus system is opted for profoundly analyzing the performance of the proposed algorithm in handling the optimal power dispatch problem considering security constraints and UPFC device, where an enhancement, at least $74,000 saving in a 365-day horizon, in total generation cost is obtained. Simulation results also validate that evolutionary algorithms need to be improved/hybridized to achieve better equilibrium between exploration and exploitation processes in a timely manner while solving power and energy-related problems.

In this manuscript, a new quantum computing-based optimization algorithm is proposed to solve multiple-objective mixed cost-effective emission dispatch (MEED) problem of electrical power system. The MEED problem aims at maintaining proper balance between emission of pollutants and generation of power. The problem has been formulated here using cubic equation to reduce the nonlinearities of the system. It is transformed to single-objective problem by considering max to max penalty factor. The proposed optimization technique is inspired by the concept of quantum mechanics, gravitational force and firefly algorithm (FA) and is termed as quantum-inspired tidal FA (QITFA). The proposed QITFA is tested on IEEE 14-bus and IEEE 30-bus test system for four different load conditions. The obtained results are compared with the results yielded by some other state-of-the-art methods like Lagrangian relaxation method, particle swarm optimization (PSO), simulated annealing, quantum-behaved bat algorithm and quantum PSO. This paper proves the superiority of the proposed QITFA over all these methods. Further, the obtained results also suggest its effective and efficient implementation in MEED problem.

In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated, mixed-integer nonlinear optimization problem, comprising control variables which are discrete and continuous. The CTFWO algorithm is used to minimize voltage deviation (VD) and real power loss (P_loss) for IEEE 30-bus and IEEE 57-bus power systems. These goals can be achieved by obtaining the optimized voltage values of the generator, the transformer tap changing positions, and the reactive compensation. In order to evaluate the ability of the proposed algorithm to obtain ORPD problem solutions, the results of the proposed CTFWO algorithm are compared with different algorithms, including artificial ecosystem-based optimization (AEO), the equilibrium optimizer (EO), the gradient-based optimizer (GBO), and the original turbulent flow of water-based optimization (TFWO) algorithm. These are also compared with the results of the evaluated performance of various methods that are used in many recent papers. The experimental results show that the proposed CTFWO algorithm has superior performance, and is competitive with many state-of-the-art algorithms outlined in some of the recent studies in terms of solution accuracy, convergence rate, and stability.

[...]it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems. [...]in the improved method, we propose (13) should be cancelled and another better one is employed. The obtained result comparisons between the two methods have indicated that the proposed method has found better solutions and its search stabilization has been superior to SFSOA. [...]it can conclude that the proposed modification on diffusion process was highly effective. [...]the proposed method can be recommended to be an effective method for ORPD problem and it can be used for other problems in other engineering fields.

Developing a low-carbon power system is critical and fundamental to cope with the challenges of global warming, in which the carbon capture and storage (CCS) technology will play a key role. In this study, the characteristics of energy flow and operation of carbon capture plants (CCPs) are clarified, while the mutual constraint between total generation output of CCPs and operation power consumption of carbon capture system is analysed. Then a generation output model and the optimal dispatch principle of CCPs is established, which can identify how the amount of carbon captured can represent a premium payment that can offset the increase in costs caused by the reduction on power output due to the CCS. On this basis, what with the low-carbon economy factors, a economic power dispatch model under low-carbon economy with CCPs considered is proposed. With the generation fuel cost and carbon emission cost incorporated in the objective function, the model proposed can effectively evaluate the power dispatch problem under low-carbon economy. Studies of the economic power dispatch of the 3-unit, 26-unit and 54-unit test systems show that the model proposed is effective and practical.

In planning and operation processes of power systems, the most critical and outstanding problem is the optimal scheduling of reactive power resources. The current research study considered real power loss as well as the deviation of voltage magnitude as objective functions since these two play important roles in a power system’s operations and control. Due to the above-mentioned considerations, bi-objective optimization takes a form here. In the recent times, lot of meta-heuristic optimization techniques was implemented to elucidate ORPD problem. One such recently advanced algorithm named Interior Search Algorithm is utilized to find a solution for challenges in power system. It is observed that it is not producing accurate solution and convergence characteristic curve is also not smooth. In order to enhance the searching ability of ISA a new method called Levy Interior Search Algorithm (LISA) was proposed in this paper. In this two different strategies of LISA were proposed. In order to validate the proposed algorithm, LISA is implemented on five various standard test systems comprising IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus and IEEE 354-bus test systems. To conclude, application results of LISA are compared with the results of other optimization techniques reported in literature. The comparison reveals that the LISA Strategy-II outperformed all other optimization techniques in terms of robustness, accuracy and convergence speed.

This paper proposes an improved social spider optimization (ISSO) for achieving different objectives of optimal reactive power dispatch (ORPD). The proposed ISSO method is developed by applying two modifications on new solution generation process. The proposed method uses only one modified equation for producing the first new solution generation and the second new solution generation while the standard SSO uses two equations for each process. The improvement in the proposed method is confirmed by solving benchmark optimization functions, IEEE 30-bus system and IEEE 118-bus system. Obtained results from ISSO are compared to those from other existing methods available in other studies together with other popular and state-of-the-art methods, which are implemented in the work. As compared to standard SSO for application to ORPD problem, ISSO can reduce the number of computation steps and one control parameter, and shorten simulation time. About the result comparisons with SSO and other remaining methods, ISSO can find more favorable solutions with higher quality and ISSO can stabilize solution search function with approximately all trial runs finding lower value of fitness. Furthermore, the strong search ability of ISSO is also indicated because it uses less value for control parameters. As a result, the proposed ISSO method can be a very effective optimization tool for dealing with the ORPD problem.

Artificial Bee Colony algorithm (ABC) is inspired by behavior of food foraging of honeybees to solve the NP-Hard problems using optimization model which is one among the swarm intelligence algorithms. ABC is a widespread optimization algorithm to obtain the best solution from feasible solutions in the search space and strive harder than other existing population-based algorithms. However, in diversification process ABC algorithm shows good performance but lacks in intensification process and slows to convergence towards an optimal solution because of its search equations. In this work, the authors proposed an improvised solution search strategy at employed bee phase and onlooker bee phase by considering the advantages of the local-best, neighbor-best, and iteration-best solutions. Thus, the obtained candidate solutions are closer to the best solution by providing directional information to ABC algorithms. The search radius for new candidate solutions is adjusted in scout bee phase which facilitates to move towards global convergence. Thus, the process of diversification and intensification is balanced in this work. Finally, to assess the performance of the proposed algorithm, 20 numerical benchmarks functions are used. To show the significance of the proposed methodology it has been tested with Combined Heat and Economic Power Dispatch (CHPED) problem. The empirical result exhibits that the proposed algorithm provides higher quality solutions and outperform with original ABC algorithm for solving numerical optimization problems.

In power grid modernisation, optimal network use is essential to preserving acceptable voltage profiles, boosting voltage stability, reducing power losses, and strengthening system security and dependability. This can be accomplished by strategically placing reactive power compensation devices within transmission and distribution networks, such as capacitor banks, synchronous condensers, flexible alternating current transmission system (FACTS) devices and custom power devices. The optimal location and size of these devices are essential for effective investment, but previous research has mostly concentrated on a variety of methods for this goal, using different indices to evaluate power loss, voltage stability, voltage profile and line loadability. Despite these initiatives, there is still a lack of a thorough analysis of how current indices and methodologies are applied to all varieties of reactive power compensation devices. This paper offers a detailed literature review on the ideal placement and sizing of these devices, encompassing analytical, conventional and hybrid-based techniques. It discusses key objectives such as power loss reduction, voltage deviation (VD) mitigation, voltage stability enhancement and improvements in system reliability and security. Additionally, the paper examines the relevance of reactive power for stakeholders, including transmission system operators (TSOs), distribution system operators (DSOs) and power generating companies, and explores the mathematical modelling of optimal reactive power dispatch (ORPD), considering the impact of renewable energy sources (RESs) and the role of FACTS devices.

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems).