Browsing by Author "Abdel-Nasser, Mohamed"

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    HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-10-26) Abdel-Nasser, Mohamed; Mahmoud, Karar; Lehtonen, Matti; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Aswan University
    The rapid employment of photovoltaic (PV) has highlighted the importance of accurate solar irradiance forecasting in grid operation. However, the intermittent nature of solar irradiance represents a big challenge and degrades the accuracy of forecasting techniques, posing towards developing ensemble-based approaches. Most ensemble approaches generate weights based on the performance of individual forecasting models (IFMs) where linear operations are often used to aggregate them. The generalization of such weights could not be practically guaranteed due to the high variability among predictions obtained by IFMs. To tackle these issues, a novel heterogeneous solar irradiance forecasting approach, so-called HIFA, is proposed in this article. Specifically, we propose an effective aggregation strategy based on kernel mapping for aggregating the predictions of accurate deep learning based IFMs. The proposed aggregation strategy can properly map the predictions of IFMs onto a consensus prediction. HIFA utilizes efficient deep recurrent neural networks, which can exploit long-term information from previous computations to model the fluctuated solar irradiance, for building the IFMs. The results reveal that HIFA substantially improves the accuracy of solar irradiance forecasting when compared to ensemble-based approaches, thanks to the generalization capability of the proposed aggregation strategy and the high accuracy of deep IFMs.
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    Improved Salp-Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020-01-12) Mahmoud, Karar; Abdel-Nasser, Mohamed; Mustafa, Eman; Ali, Ziad M.; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Aswan University; Prince Sattam Bin Abdulaziz University
    Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp–swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.
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    Link quality prediction in wireless community networks using deep recurrent neural networks
    (Alexandria University, 2020-10) Abdel-Nasser, Mohamed; Mahmoud, Karar; A. Omer, Osama; Lehtonen, Matti; Puig, Domenec; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Universidad Rovira i Virgili; Aswan University
    Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.
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    Low-Computational Voltage-Assessment Approach Considering Fine-Resolution Simulations for Distribution Systems with Photovoltaics
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-12-01) Mahmoud, Karar; Abdel-Nasser, Mohamed; Lehtonen, Matti; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; University of Rovira i Virgili
    The proliferation of photovoltaic (PV) has been increased in distribution systems worldwide. The intermittent PV generation can cause diverse operational problems in the grid, especially voltage deviations and violations. As a result, voltage assessment in distribution systems interconnected with PV, which has a heavy computational burden, is privileged to assist power utilities in decision-making. In this article, a low-computational and accurate voltage assessment approach with PV considering fine-resolution simulations (i.e., time-step of 1 s) is proposed. Specifically, the proposed approach can rapidly compute the voltage deviation in the whole distribution system and terminal voltages of PV units based on a data-driven model. This model is built using machine learning considering various scenarios of PV and load profiles. The proposed approach has the following features. 1) Its computational burden is very low compared to the widely used iterative-based methods. 2) It can handle the full data with the finest available resolution, yielding accurate voltage assessment. The proposed method has been applied for voltage assessment considering daily and annual simulations of different distribution systems interconnected with PV units. The simulation results manifest the high accuracy and computational speed of the proposed approach, especially for fine-resolution simulations.
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    Optimal Voltage Control in Distribution Systems With Intermittent PV Using Multiobjective Grey-Wolf-Lévy Optimizer
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-03) Mahmoud, Karar; Hussein, Mahmoud M.; Abdel-Nasser, Mohamed; Lehtonen, Matti; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Aswan University
    The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf optimizer integrated with a Lévy mutation operator (GWO-Lévy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Lévy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV.
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    Optimal Voltage Regulation Scheme for PV-Rich Distribution Systems Interconnected with D-STATCOM
    (TAYLOR & FRANCIS, 2021-06-15) Mahmoud, Karar; Abdel-Nasser, Mohamed; Lehtonen, Matti; Hussein, Mahmoud; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Aswan University
    This paper proposes an optimal voltage regulation scheme (OVRS) for distribution systems with rich photovoltaic (PV). Various regulation devices are optimally controlled in a coordinated manner: PV inverter, D-STATCOM, step voltage regulator (SVR), and on-load-tap-changer (OLTC). A data structure algorithm is proposed to split the distribution system into layered zones considering the radial structure of the system. The solution process of the proposed scheme is accomplished by a meta-heuristic optimizer. OVRS addresses the voltage violations while yielding a coordinated operation of the various control devices. The proposed OVRS involves three control levels to completely prevent voltage violations. In the first control level, the PV inverter and D-STATCOM mitigate rapidly the local voltage deviation through injecting/absorbing optimized reactive power. The second control level is a decentralized-based control scheme that utilizes the voltage control devices in each zone to handle the voltage violations if any. For each zone, the control devices in the upper-stream zones (parent zones) are managed by the third control level to ensure cooperative control actions. The simulation results on the 119-bus distribution system, with clear, low fluctuation, and high fluctuation of solar radiation profiles, demonstrate the effectiveness of the proposed OVRS.
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    Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-03) Abdel-Nasser, Mohamed; Mahmoud, Karar; Lehtonen, Matti; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Universidad Rovira i Virgili
    The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.