An effective hybrid approach based on arithmetic optimization algorithm and sine cosine algorithm for integrating battery energy storage system into distribution networks

Abdel-Mawgoud, Hussein; Fathy, Ahmed; Kamel, Salah

doi:10.1016/j.est.2022.104154

Cited by 27 publications

(13 citation statements)

References 57 publications

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“…There are other algorithms that are a combination or modification of algorithms in these four categories. Some of them are: modified particle swarm optimization [36], [37], modified harmony search algorithm [38], modified gravitational search algorithm [39], [40], modified ant colony optimization [41], modified sine cosine algorithm [42], [43], modified wild horse optimization [44], modified slime mould algorithm [45], hybrid genetic algorithm and particle swarm optimization [46], hybrid firefly algorithm [47], hybrid sperm swarm optimization and gravitational search algorithm [48], hybrid tunicate swarm algorithm and pattern search [49], hybrid arithmetic optimization algorithm and sine cosine algorithm [50].…”

Section: Related Workmentioning

confidence: 99%

Golden Search Optimization Algorithm

et al. 2022

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This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon's rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.

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Section: Related Workmentioning

confidence: 99%

Golden Search Optimization Algorithm

et al. 2022

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“…ESS has a crucial role in ANDs' optimal operating scheduling where it can be useful from the economic and technical point of view, and they have been taken into consideration in many studies, up to now. The optimal location and size of ESS in a distribution network were obtained considering the system's reliability in [10]. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

“…𝜎[] is the standard deviation. Equation (10) indicates the active power demand in ith bus and Equation (11) denotes the reactive power demand in ith bus.…”

Section: Load Demands Uncertainty Modellingmentioning

confidence: 99%

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Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks

Ebrahimi

Rezaeian‐Marjani

Farhadi‐Kangarlu

et al. 2022

IET Generation Trans & Dist

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Harmonics originating from non-linear loads in distribution systems will cause undesired effects and malfunctions of equipment. It is important to use efficient techniques for eliminating the voltage and current harmonic components. However, the uncertain nature of the active distribution networks (ADN) makes it difficult to apply these techniques, efficiently. Renewable energy sources (RESs), such as wind turbines (WTs) besides load fluctuations are the most important uncertainty sources. This study employs energy storage systems (ESS) to reduce total harmonic distortion (THD) as well as the ADN operating costs. In addition to the ESS, network structure reconfiguration and reactive power scheduling is presented for a more efficient operation of ADN. Also, the uncertainty of the network is modelled by the K-means and LHS methods. This methodology is conducted under a multi-objective optimization problem, which is handled by a multi-objective particle swarm optimization (MOPSO) algorithm. Furthermore, a technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to obtain the best compromise between two objectives. The efficacy of the proposed methodology is investigated in the IEEE 33 bus test system. Also, for validating the results, a large-scale network, the IEEE 118 bus test system is surveyed, too.

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“…The Coot Bird Natural Life Model (COOT) algorithm is an innovative algorithm, which has not been used in hydrological problems; however, it has been used in other different fields with high accuracy. Mawgoud et al (2022) investigated an effective hybrid approach based on arithmetic optimization algorithm and sine cosine algorithm for the integration of battery energy storage system in distribution networks by the COOT, SCA, AOA, DP, and AOA-SCA models. Mostafa et al (2022) Since there is no research on the application of hybrid COOT-ANN model in water resources fields based on the best knowledge of the authors, further studies are needed to investigate its performance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid COOT–ANN: a novel optimization algorithm for prediction of daily reference evapotranspiration in Australia

Mirzania

Kashani

Golmohammadi

et al. 2022

Preprint

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The present study evaluates the capability of a novel optimization method in modeling daily reference evapotranspiration (ET0), a critical issue in water resource management. A hybrid predictive model based on the ANN Algorithm that is embedded within the COOT method (COOT bird natural life model- Artificial Neural Network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ET0 at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ET0 were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), Root Mean Square Error (RMSE), Nash-Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations; and so this study provides an innovative method for prediction in agricultural and water resources studies.

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An effective hybrid approach based on arithmetic optimization algorithm and sine cosine algorithm for integrating battery energy storage system into distribution networks

Cited by 27 publications

References 57 publications

Golden Search Optimization Algorithm

Golden Search Optimization Algorithm

Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks

Hybrid COOT–ANN: a novel optimization algorithm for prediction of daily reference evapotranspiration in Australia

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