An analysis of photovoltaic/supercapacitor energy system for improving self-consumption and self-sufficiency

Hassan, Qusay; Jaszczur, Marek; Abdulateef, Ammar M.; Abdulateef, Jasim; Hasan, Ali N.; Mohamad, A. A.

doi:10.1016/j.egyr.2021.12.021

Cited by 41 publications

(16 citation statements)

References 41 publications

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“…When photons hit the surface of a metal, energy is absorbed by electrons, until the electrons escape from the metal surface and form photoelectrons. The theoretical output of photovoltaic power generation can be expressed as [35],…”

Section: Wind/photovoltaic/hydrogen Storage Power System Modelmentioning

confidence: 99%

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

Liu¹,

Liu²

2023

Energy Engineering

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The optimal allocation of integrated energy system capacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy, which has attracted many attentions. However, the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters. To solve the above problem, the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms. Firstly, an integrated energy system consisting of the photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and energy storage is established. Meanwhile, the minimum economic cost, the maximum wind and PV power consumption rate, and the minimum load shortage rate are considered to be the objective functions. Then, a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem. According to the optimal result, the economic cost is 6.3 million RMB, and the load shortage rate is 9.83%. Finally, four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method. The comparative results indicate that the proposed method possesses a stronger merit-seeking ability, resulting in a solution satisfaction rate of 87.37%, which is higher than that of the unimproved non-dominated sorting genetic algorithms-II.

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Section: Wind/photovoltaic/hydrogen Storage Power System Modelmentioning

confidence: 99%

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

Liu¹,

Liu²

2023

Energy Engineering

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“…Kichou et al [12] evaluated the effect of PV and battery systems on the self-sufficiency of two residential buildings in Prague, where the active discharge of the storage into the grid is foreclosed by design. Similarly, Hassan et al [13] analyse a PV-supercapacitor system for improving self-sufficiency, while Gudmunds et al [14] analyse the selfsufficiency of prosumers and prosumagers when introducing an EV. For reasons of simplicity, the entailed storage entities in above-mentioned studies are used exclusively for the storage of own local generation, and hence disallowed for active grid exchange.…”

Section: Related Literaturementioning

confidence: 99%

Re-Thinking the Definition of Self-Sufficiency in Systems with Energy Storage

Zepter

Engelhardt

Gabderakhmanova

et al. 2022

2022 International Conference on Smart Energy Systems and Technologies (SEST)

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Self-sufficiency is an important metric for various energy concepts, as it reflects what share of the local consumption is covered by local generation. However, the equation commonly used in literature cannot be applied to systems with an energy storage that actively exchanges energy with the grid. With more and more systems incorporating storage units it is therefore necessary to re-think the mathematical definition of self-sufficiency. The present paper addresses this issue by proposing an alternative equation that captures distinctive factors introduced by storage units: (i) Energy exported to the grid can originate from previously imported energy, (ii) initial and final storage energy in a given observation period can be different, (iii) the usage of storage systems entails energy losses. It is demonstrated that neglecting these factors leads to an over-or underestimation of self-sufficiency with values even reaching below 0 % or above 100 %. In contrast, the self-sufficiency calculated by the proposed definition considers the above mentioned factors and always stays within the defined range.

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“…Several studies have been conducted to improve the performance of solar power systems and use modern systems to benefit from the largest amount of solar energy [17][18][19][20]. The state estimation and prediction for the photovoltaic system are critical as it is very important in avoiding losses due to external influences on the system.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Al-Dulaimi¹,

Guneser²,

Hameed³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures. Therefore, it causes an insulating layer on solar panels and the inability to produce electrical energy. The detection of snow-covered solar panels is crucial, as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation. This paper presents five deep learning models, -16, -19, ESNET-18, ESNET-50, and ESNET-101, which are used for the recognition and classification of solar panel images. In this paper, two different cases were applied; the first case is performed on the original dataset without trying any kind of preprocessing, and the second case is extreme climate conditions and simulated by generating motion noise. Furthermore, the dataset was replicated using the upsampling technique in order to handle the unbalancing issue. The conducted dataset is divided into three different categories, namely; all_snow, no_snow, and partial snow. The five models are trained, validated, and tested on this dataset under the same conditions 60% training, 20% validation, and testing 20% for both cases. The accuracy of the models has been compared and verified to distinguish and classify the processed dataset. The accuracy results in the first case show that the compared models -16, -19, ESNET-18, and ESNET-50 give 0.9592, while ESNET-101 gives 0.9694. In the second case, the models outperformed their counterparts in the first case by evaluating performance, where the accuracy results reached 1.00, 0.9545, 0.9888, 1.00. and 1.00 for -16, -19, ESNET-18 and ESNET-50, respectively. Consequently, we conclude that the second case models outperformed their peers.

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An analysis of photovoltaic/supercapacitor energy system for improving self-consumption and self-sufficiency

Cited by 41 publications

References 41 publications

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II

Re-Thinking the Definition of Self-Sufficiency in Systems with Energy Storage

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

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