Sizing and Design of a PV-Wind-Fuel Cell Storage System Integrated into a Grid Considering the Uncertainty of Load Demand Using the Marine Predators Algorithm

Mahmoud, Fayza S.; Abdelhamid, Ashraf M.; Sumaiti, Ameena Al; El‐Sayed, Abou‐Hashema M.; Diab, Ahmed A. Zaki

doi:10.3390/math10193708

Cited by 19 publications

(5 citation statements)

References 53 publications

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“…The evaluation of the proposed energy system's reliability relies on the Loss of Power Supply Probability (LPSP). It is crucial to ensure that the LPSP does not exceed a certain threshold, as indicated by [23].…”

Section: − 2 Loss Of Power Supply Probabilitymentioning

confidence: 99%

“…The annual total cost of the hybrid system is the same as the total cost of the PV, wind turbine, FCs, electrolyzer, H2 tank, and converters components of the suggested system [23].…”

Section: Cost Of Energy (Coe)mentioning

confidence: 99%

“…These days, several studies describe and assess how to model, size, and simulate hybrid systems such as RE with FCs and use a suggested optimization technique to increase system reliability while lowering energy costs [18][19][20][21][22]. In the literature, F. S. Mahmoud et al (2022) [23] have several optimization techniques that aim to nd the best solution to HRES. For the best sizing of HRESs in combination with a grid under uncertainty, the marine predators' algorithm (MPA) and the seagull optimization algorithm (SOA) was integrated.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Said,

Gad,

El-Gaafary

et al. 2024

Preprint

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An optimum design of Photovoltaic-wind turbine-fuel cell hybrid energy systems (HRES) linked to a stand-alone micro-grid to meet the energy requirements of industrial and residential buildings in the Egyptian city of Siwa with anew algorithm introduced in this paper. The main photovoltaic (PV) and wind turbine (WT) hybrid renewable energy systems (HRESs) provide an affordable means of distributing power in these areas and then there is surplus power, it is efficiently channeled towards the electrolyzer to produce hydrogen. When the generated power cannot provide sufficient power, stored hydrogen is furnished to a proton exchange membrane fuel cell which, smoothly and without interruption, supplies the load. Fuel cells serve as supplemental sources intended to balance out power fluctuations and provide a steady supply of electricity to the load. The Zebra algorithm (ZOA) is used to determine the hybrid system's ideal size and compare results with Cuckoo Search algorithm (CSA). To match the load of the site with high operational reliability, a fitness function is employed loss of power supply probability (LPSP). To have a high-performance, dependable system, the optimization problem's choice variables such as the quantity of PV arrays and WTs are optimized. Based on the findings, 150 FCs, 113 wind turbines, and 82 PV arrays make up the ideal HRES system. The optimization performance has been excellent, even when the unmet load is zero with lowest COE.

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Section: − 2 Loss Of Power Supply Probabilitymentioning

confidence: 99%

“…The annual total cost of the hybrid system is the same as the total cost of the PV, wind turbine, FCs, electrolyzer, H2 tank, and converters components of the suggested system [23].…”

Section: Cost Of Energy (Coe)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Said,

Gad,

El-Gaafary

et al. 2024

Preprint

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“…𝐶 𝐴,𝐺𝑆 = 𝐶 𝑆 ∑ 𝑃 𝐺𝑆 (𝑡) 8760 𝑡=1 (13) In this context, the cost of purchasing one kWh of electricity from the electric grid is denoted as 𝐶 𝑃 , with a specific value of $0.08/kWh. On the other hand, the cost of supplying one kWh of electricity to the utility grid is represented as 𝐶 𝑆 , with a value of $0.2/kWh [24].…”

Section: Cost Of Energymentioning

confidence: 99%

Optimizing Grid-Dependent Hybrid Renewable Energy System with the African Vultures Optimization Algorithm

Abdelsattar,

Mesalam,

Fawzi

et al. 2024

SVU-International Journal of Engineering Sciences and Applicati

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Countries aiming to achieve their sustainable development goals recognize the significance of adopting hybrid power systems to ensure access to clean, dependable, and cost-effective energy. In this study, an african vultures optimization algorithm (AVOA) is introduced for the efficient design of a grid-tied hybrid renewable energy (HRE) system that incorporates wind turbines, photovoltaic (PV) panels, and energy storage through batteries. The system is meticulously designed to ensure the provision of clean, dependable, and cost-effective energy through the utilization of HRE systems. Because of the complex and non-linearity of the sizing problem, AVOA, being an efficient metaheuristic approach, presents a promising solution. An empirical case study is presented, focusing on an HRE system introduced in the Zafarana region of Egypt. This case study serves as an evaluation of the effectiveness of the proposed optimizer. This study will provide valuable insights for decision-makers in Egypt, offering a practical solution to enhance the integration of intermittent renewable systems and ensure a continuous and reliable energy supply. The results achieved through AVOA are compared with those obtained using the particle swarm optimization (PSO) algorithm for evaluation. Simulation results validate the superior of the AVOA over the PSO, showing its potential to deliver promising results.

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“…Therefore, its potential energy output must be predicted. Accurate wind power forecasting is essential for the smooth incorporation of wind energy into the utilities (Hamid and Alotaibi, 2022a;Cheng et al, 2022;Mahmoud et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Saeed¹,

Ibrahim²,

El-kenawy³

et al. 2023

Front. Energy Res.

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Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting.

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Sizing and Design of a PV-Wind-Fuel Cell Storage System Integrated into a Grid Considering the Uncertainty of Load Demand Using the Marine Predators Algorithm

Cited by 19 publications

References 53 publications

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Optimal Sizing and Design of a Photovoltaic-Wind-Fuel Cell Storage System Using Zebra Optimization Algorithm

Optimizing Grid-Dependent Hybrid Renewable Energy System with the African Vultures Optimization Algorithm

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

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