Smart solar power plant

Jose, Sam; Itagi, Rajeshwari L.

doi:10.1109/iccsp.2015.7322615

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Intelligent renewable energy generation has received extensive attention recently, and the concept of the smart power plant has also been mentioned more frequently [59], [60]. With big data, deep learning, industrial cloud platform, and other information technologies, a lot of functions and applications can be improved in power generation such as renewable energy output monitoring, equipment performance diagnosis, early warning, information security, and operation cost reduction [6].…”

Section: A Ec-cc System For Power Generationmentioning

confidence: 99%

Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications

Xiang

et al. 2022

Journal of Modern Power Systems and Clean Energy

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The quantity and heterogeneity of intelligent energy generation and consumption terminals in the smart grid are increasing drastically over the years. These edge devices have created significant pressures on cloud computing (CC) system and centralised control for data storage and processing in realtime operation and control. The integration of edge computing (EC) can effectively alleviate the pressure and conduct real-time processing while ensuring data security. This paper conducts an extensive review of the EC-CC computing system and its application to the smart grid, which will integrate a vast number of dispersed devices. It first comprehensively describes the relationship among CC, fog computing (FC), and EC to provide a theoretical basis for the differentiation. It then introduces the architecture of the EC-CC computing system in the smart grid, where the architecture consists of both hardware structure and software platforms, and key technologies are introduced to support functionalities. Thereafter, the application to the smart grid is discussed across the whole supply chain, including energy generation, transportation (transmission and distribution networks), and consumption. Finally, future research opportunities and challenges of EC-CC while being applied to the smart grid are outlined. This paper can inform future research and industrial exploitations of these new technologies to enable a highly efficient smart grid under decarbonisation, digitalisation, and decentralisation transitions.

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Section: A Ec-cc System For Power Generationmentioning

confidence: 99%

Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications

Xiang

et al. 2022

Journal of Modern Power Systems and Clean Energy

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“…The characteristics and limitations are discussed for two types of available solar power plants. The modifications required to make the solar plant into a smart or intelligent solar plant [4]. The study is based on a new strategy of frequency control and also in the virtual inertia control based on the virtual synchronous generator, which simulates the behavior of conventional generators in large power systems.…”

Section: Literature Surveymentioning

confidence: 99%

Carbon Footprint Study on Renewable Power Plants: Case Study on Egypt's Benban Solar Park

Metwally

Magdy²,

Elbaset³

et al. 2022

International Journal of Applied Energy Systems

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Some recent human activities have negatively affected the environment through the production of toxic substances. Carbon footprint (CFP) refers to the total amount of greenhouse gases generated, i.e., disaggregated by categories of equivalent carbon dioxide compounds. It plays a major role in global environmental degradation, causing global warming. The direct emission of the CFP is determined by the fossil fuels used to generate electricity traditionally and the use of different means of transportation. The use of renewable energy sources (RESs) is a long-term investment aimed at obtaining clean energy, reducing CFP, and replacing fossil fuels for electricity production, thus contributing to the conservation of energy resources for future generations. This paper discusses the most important ways to reduce the CFP by treating toxic gases in the atmosphere and switching to the use of RESs. The results obtained show that the more use of RESs, the less carbon it is because it is clean energy. Furthermore, this paper has studied the case of the Benban solar power plant, Aswan, Egypt which is considered one of the world's biggest solar photovoltaic stations. It was found that this plant reduces carbon emissions by 2 million tons of heat emissions, the equivalent of 400,000 cars.

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“…Solar power is a prominent renewable energy source that transforms sunlight into electricity [1], emitting less pollution than traditional fossil fuels [2]. However, solar power generation faces challenges in energy efficiency, long-term adoption, cost reduction, and efficient battery utilization.…”

Section: Introductionmentioning

confidence: 99%

Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants

Thonglek

Ichikawa

Takahashi

et al. 2023

IEICE Trans. Commun.

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Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.

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Smart solar power plant

Cited by 4 publications

References 14 publications

Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications

Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications

Carbon Footprint Study on Renewable Power Plants: Case Study on Egypt's Benban Solar Park

Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants

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