Single- and Multiobjective Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Moth Flame Optimization Algorithm

Pandya, Sundaram B.; Jariwala, Hitesh R.

doi:10.1080/23080477.2021.1964692

Cited by 20 publications

(5 citation statements)

References 58 publications

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“…In order to get the space disparity, we need to first measure the distance that separates each of the test suites. Next, we need to calculate the path disparity by working backwards from the branch condition through the control flow graph [22], [23]. Because product testing must take into account both the long term and the cost-benefit analysis, extensive testing may not be carried out.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Ryalat¹,

Fakhouri²,

Zraqou³

et al. 2023

IJACSA

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Data testing is a vital part of the software development process, and there are various approaches available to improve the exploration of all possible software code paths. This study introduces two contributions. Firstly, an improved version of the Multi-verse Optimizer called Testing Multi-Verse Optimizer (TMVO) is proposed, which takes into account the movement of the swarm and the mean of the two best solutions in the universe. The particles move towards the optimal solution by using a mean-based algorithm model, which guarantees efficient exploration and exploitation. Secondly, TMVO is applied to automatically develop test cases for structural data testing, particularly path testing. Instead of automating the entire testing process, the focus is on centralizing automated procedures for collecting testing data. Automation for generating testing data is becoming increasingly popular due to the high cost of manual data generation. To evaluate the effectiveness of TMVO, it was tested on various well-known functions as well as five programs that presented unique challenges in testing. The test results indicated that TMVO performed better than the original MVO algorithm on the majority of the tested functions.

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Section: Related Workmentioning

confidence: 99%

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Ryalat¹,

Fakhouri²,

Zraqou³

et al. 2023

IJACSA

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show abstract

“…Also, in Khan et al, 9 multiobjective multiverse optimization algorithm was developed to explore the optimal allocation of SVC and TCSC in an IEEE 57-bus system for voltage profile enhancement and power losses minimization. Furthermore, many studies [10][11][12][13][14][15][16] introduce other optimization problems and problemsolving approaches for various systems.…”

Section: Motivationmentioning

confidence: 99%

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

Awad,

Kamel,

Hassan

et al. 2023

Energy Science & Engineering

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The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.

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“…The researchers [13] proposed a robust multi-objective method to determine the Pareto optimal solutions of a multi-objective optimal power flow framework which was inclusive of thermal, wind, and solar PV energy sources in a hybrid power system. Both single and multi-objective optimal power flow was validated with the integration of solar and wind power sources using moth flame optimization algorithm [14]. In the study conducted earlier [15], multi-objective optimal power flow problem, encountered with the integration of thermal, wind, and PV systems, was handled using a multi-objective evolutionary algorithm based on decomposition and the summation of normalized objectives with an improved diversified selection method was proposed.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

Nagarajan,

Reddy,

Rajagopalan

et al. 2024

IJEER

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Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power. The current study outcomes provide knowledgeable insights towards power flow optimization in power systems with high penetration of renewable energy. The application results reveal that the improved mayfly algorithm achieved better efficacy compared to the classical mayfly algorithm and the rest of the optimization algorithms.

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Single- and Multiobjective Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Moth Flame Optimization Algorithm

Cited by 20 publications

References 58 publications

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Enhanced Multi-Verse Optimizer (TMVO) and Applying it in Test Data Generation for Path Testing

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy

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