Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

Sharkawy, Abdel-Nasser; Ali, Mustafa M.; Mousa, Hossam H. H.; Ali, Ahmed S.; Abdel-Jaber, G. T.; Hussein, Hany S.; Farrag, Mohammed; Ismeil, Mohamed A.

doi:10.1109/access.2023.3249108

Cited by 8 publications

(5 citation statements)

References 74 publications

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“…To fulfil the obligations of the ADNs and overcome the drawbacks of the previous approaches (model-based approaches), the applications of AI algorithms are extensively applied in MPSs [214]. Hence, the data-driven approaches are utilized to process the historical dataset or measured data from smart meters, to develop models for real-time estimation and forecasting of the HC of the ADNs with reliable operation [10].…”

Section: Datadriven Approachesmentioning

confidence: 99%

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

Mousa,

Mahmoud,

Lehtonen

2024

IEEE Access

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Recently, several types of distributed energy resources (DERs) have been developed to reduce the environmental impact and support the global demand for electrical energy. However, the continuous penetration of the DERs into modern power systems (MPSs) may cause several adverse impacts in terms of operation performance indices (PIs) and power quality issues, especially in low-voltage distribution systems (LVDSs). To cope with these serious impacts and achieve optimal control, the hosting capacity (HC) of DERs must be accurately estimated and optimally optimized. However, it requires an extensive communication infrastructure, which is hardly offered without clear financial benefits. In this regard, this article investigates the historical developments of HC definitions as well as the recent developments in terms of operational performance indices, and estimation methods for active distribution networks (ADNs) with the highpenetration level existence of the DERs, energy storage systems (ESSs), electric vehicle (EV) charging systems, sector coupling, hydrogen technologies, and multi-carrier energy systems (MESs) to deal with electrical, thermal, and cooling demands. In this regard, this review article is intended to exhibit an appropriate reference for comprehensive research trends in HC estimation and optimization based on ADNs. Additionally, it involves and covers most current research HC topics in detail compared to other published review articles. Moreover, the authors deliberate the recent approaches for evaluating and improving the HC, especially concerning data-driven methods, with the aid of various software for simulating real systems. Moreover, modern research trends and main factors of MPS operations are deliberated with current energy market developments. Also, prominent challenges, current status, and future aspects are discussed.

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Section: Datadriven Approachesmentioning

confidence: 99%

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

Mousa,

Mahmoud,

Lehtonen

2024

IEEE Access

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“…The suggested NN types were contrasted with other relevant earlier methods that are discussed in references [7,[58][59][60]. This comparison aimed to clarify the influence of the NN in estimating output power under different conditions.…”

Section: Comparison With Earlier Researchsmentioning

confidence: 99%

“…This indicated that employing our suggested structures improved both the accuracy and the closeness between estimated and true power. While it was not stated and was not explored with others, the generalization ability under various scenarios and conditions was evaluated with our suggested approach by Abdel-Nasser, et al [7]. Figure 15 shows the difference between suggested methods and the other approaches mentioned in Table 10 by displaying the accuracy percentage (%) that could be calculated by the difference between 100 and MSE% (100-MSE%).…”

Section: Comparison With Earlier Researchsmentioning

confidence: 99%

“…These conditions are also critical for the control of the grid operator that must prioritize wind turbine substations, improve the entrance of electrical power into the grid, and foresee future energy requirements and issues [5,6]. Two options are used in various studies to preserve the utility grid's performance indices within permitted limitations, depending on changes in the climate and violent interruptions of RESs in grid-connected or islanded modes [7]. At first glance, using large-capacity energy storage devices, such as batteries, escalates the initial cost of deployment and negatively affects the utility grid's power quality.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Sharkawy,

Ameen,

Mohamed

et al. 2024

Automation

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The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.

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“…Moreover, it is used for scheduling day-ahead balancing between supply and demand associated with energy markets [83,84]. • Long-medium term (weekly or seasonal): it refers to the ability of power systems to respond to changes in demand and supply within a week or over a season which can be used in hydro units, thermal units or RESs scheduling [85,86]. • Long term (years): for planning the power system generation and consumption for years regarding the energy costs, a longterm timescale is applied [87,88].…”

Section: Flexibility Timescalesmentioning

confidence: 99%

Recent developments of demand‐side management towards flexible DER‐rich power systems: A systematic review

Mousa,

Mahmoud,

Lehtonen

2024

IET Generation Trans & Dist

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Recently, various distributed energy resources are significantly integrated into the modern power systems. This introduction of distributed energy resource‐rich systems can cause various power quality issues, due to their uncertainties and capacity variations. Therefore, it is crucial to establish energy balance between generation and demand to improve power system's reliability and stability and to minimize energy costs without sacrificing customers’ comfort or utility. In this regard, power system flexibility concept is highlighted as a robust and cost‐effective energy management system, especially on the demand side, to provide consumers’ demands with an acceptable level of power quality. Accordingly, here, a comprehensive review of recent developments in the power system flexibility and demand‐side management strategies and demand response programs are provided to include mainly classifications, estimation methods, distributed energy resource modelling approaches, infrastructure requirements, and applications. In addition, current research topics for applying power system flexibility solutions and demand‐side management strategies based on modern power system operation are deliberated. Also, prominent challenges, research trends, and future perspectives are discussed. Finally, this review article aims to be an appropriate reference for comprehensive research trends in the power system flexibility concept in general and in demand‐side management strategies and demand response programs, specifically.

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Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

Cited by 8 publications

References 74 publications

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Recent developments of demand‐side management towards flexible DER‐rich power systems: A systematic review

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