A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

Weng, Xuemeng; Xuan, Ping; Heidari, Ali Asghar; Cai, Zhennao; Chen, Huiling; Mansour, Romany F.; Ragab, Mahmoud

doi:10.1016/j.energy.2023.127000

Cited by 11 publications

(5 citation statements)

References 86 publications

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“…In this particular scenario, the influence of FACTS devices on decreasing power losses within the electrical transmission network is evidently more significant than in other instances. The power loss attained by CGO amounts to 1.761859 MW, which is lower than the values of 2.2278212 MW in RIME, 2.8777 MW in OOA, 1.9416 MW in SMA, 2.482 MW in SCA [21], 1.880 MW in IMO [21], 1.7898 MW in GWO [22], 1.9736 MW in FPA [22], and 2.0420 MW in SCA [22]. As illustrated in Table 12, all algorithms exhibit reduced power losses compared to previous cases.…”

Section: B Casementioning

confidence: 65%

“…Specifically, in the CGO algorithm, the total generation cost decreased to $807.0393/h, which is lower than the costs observed in the RIME algorithm ($808.5072/h), OOA algorithm ($817.0351/h), and SMA algorithm ($807.496018/h). Additionally, the CGO algorithm's cost was lower compared to other algorithms studied, such as MVO [21] ($808.030/h), ALO [21] ($809.449/h), SCA [21] ($818.654/h), CBA [22] ($810.5056/h), FPA [22] ($808.0864/h), and SCA [22] ($815.4325/h), as shown in Figure 12. This indicates that the CGO approach yielded a lower cost compared to the other algorithms.…”

Section: A Casementioning

confidence: 88%

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Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

Mohamed,

Kamel,

Hassan

et al. 2024

IEEE Access

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This study addresses the optimal power flow (OPF) problem incorporating renewable energy sources (RES) and flexible alternating current transmission systems (FACTS) using the Chaos Game Optimization (CGO) algorithm. Five objective functions are considered, which include minimizing generation costs, emissions, active power loss, voltage deviation, and enhancing voltage profiles. The OPF formulation considers the anticipated electricity production from wind turbines (WT) and photovoltaic (PV) units as dependent variables, while the voltage magnitude at WT and PV buses is treated as a control variable. Probabilistic models based on wind speed and solar irradiance are used to forecast the electrical output of WT and PV units. The proposed OPF methodology and solution method are validated on the IEEE 30-bus test network. This paper introduces and applies four new optimization techniques inspired by biological and natural phenomena, namely CGO, Osprey Optimization Algorithm (OOA), RIME Algorithm, and Slime Mould Algorithm (SMA), to address both single-OPF and multi-OPF objective problems in electric power networks. The suggested optimization approaches are tested under different operational scenarios, considering various combinations of FACTS, renewable energy sources (solar PV and wind), and their locations in the network. To predict wind and solar PV power generation, Weibull and lognormal probability density functions are utilized, respectively. The objective function accounts for reserve cost due to overestimation and penalty cost due to underestimation of intermittent solar and wind power. The results demonstrate that the CGO technique is more efficient than other methods in solving OPF instances.INDEX TERMS renewable energy source; wind power generation; solar photovoltaic; FACTS devices; load flow; CGO algorithm.

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Section: B Casementioning

confidence: 65%

Section: A Casementioning

confidence: 88%

Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

Mohamed,

Kamel,

Hassan

et al. 2024

IEEE Access

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“…Given these limitations, there is a need to employ straightforward concepts and easily implementable optimization techniques that do not require gradient definitions. Benchmark methods such as Salp Swarm and Sine Cosine Optimization have been verified in different power system applications, including power flow analysis [21,22], parameter estimation [23,24], parameter extraction of solar photovoltaic models using rat swarm optimization [25], power system stabilizer [26], stability improvement [27], and optimization of retaining walls [28,29]. Popular optimization techniques such as Whale Optimization Algorithm (WOA) [30], Grey Wolf Optimization (GWO) [31], Student Psychology-Based Optimization (SPBO) [32], Symbiotic Organisms Search (SOS) [33], and Firefly algorithm (FFO) [34] have been introduced, offering alternative approaches to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

Optimizing transmission line parameter estimation with hybrid evolutionary techniques

Shaikh,

Raj,

Abdul Latif

et al. 2024

IET Generation Trans & Dist

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Power flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology‐based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability.

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“…In [25], an effective hybrid technique using the Whale algorithm and Moth-Flame method is designed and applied to solve several power management problems. In [26], a vertical and horizontal crossover sine cosine algorithm combined with a pattern search algorithm is designed (CPSCA-PS) to enhance the performance of modern electric systems by integrating various types of FACTS devices. Various objective functions related to the OPF are optimized considering the integration of three types of FACTS devices: TCSC, TCPS, SVC, and two Wind sources.…”

Section: Introductionmentioning

confidence: 99%

Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

Mahdad

2024

Eng. Res. Express

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this paper focuses on solving with accuracy the multi objective optimal power flow of large-scale power systems under critical loading margin stability using a novel improved mountain gazelle optimizer (IMGO)-based flexible distributed strategy. Multi-shunt compensator-based flexible AC transmission systems (FACTS), such as SVC and STATCOM devices, are integrated at specified locations to exchange reactive power with the network. It is well known that several metaheuristic methods are able to solve the standard OPF related to small and medium test systems with success. However, by considering large-scale electric systems based on FACTS devices and renewable energy and by considering the operation under loading margin stability, the majority of these techniques fail to achieve the near-global solution due to the high dimension and nonlinearity of the problem to be solved. In this study, a new planning strategy, namely the Multi-Objective OPF-Based Distributed Strategy (MO-OPFDS), is proposed to optimize individually and simultaneously various objective functions, in particular the total power loss (T∆P), and the total voltage deviation (T∆V). The mechanism search of the standard MGO is improved by dynamically adjusting exploration and exploitation during the search process. The robustness of the proposed variant was validated on the large electric test system, the IEEE 118-Bus, and on the Algerian Network 114-Bus under normal conditions and at critical loading margin stability. The obtained results compared to several recent techniques clearly confirm the high performance aspect of the proposed method in terms of solution accuracy and convergence behaviors.

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A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

Cited by 11 publications

References 86 publications

Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

Optimal Power Flow Incorporating Renewable Energy Sources and FACTS Devices: A Chaos Game Optimization Approach

Optimizing transmission line parameter estimation with hybrid evolutionary techniques

Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

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