• Energy Research

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 high-penetration 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.

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 high-penetration 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.

This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

Abstract Worldwide, electrostatic precipitators (ESP) have been extensively utilized to separate fine particles for diverse large-scale industrial applications. In this regard, this paper presents a novel approach for modeling the dust-loaded ESP on the fine computational domain where the need for a fast solver arises. Unlike the previously published numerical techniques, the finite difference method (FDM) integrated with a full multi-grid method (FMG), labeled FDM-FMG, is developed to resolve Poisson and continuity equations on one fine computational domain. For clean and dust-loaded ESP, the proposed FMG is checked versus successive over-relaxation (SOR) on fine domains where the proposed one is greatly transcendent in terms of convergence characteristics and hence the computational performance (CPU time). For the first time, two major issues are highlighted and solved: the first concerning issue is the chosen ion mobility as an important factor in the simulation results and the second one is choosing an optimal computational grid for dust loaded precipitators that grantees both low truncation and roundoff errors, results in well-matched with experimental measurements nominated in the previous publishing. The novel idea of working on various grid sizes and tracking the optimal ones gives the FDM-FMG an advantage of predicting a precise picture for the electrical situations in industrial ESP over the other numerical techniques. After all, the impact of changing the spacing between the different wires and the height of the ionized wires on the distributions of current, ion, and particle charge densities on the ground are deeply simulated and presented in dust-loaded ESP. The proposed FDM-FMG can be a promising tool for the designers and manufacturers of precipitators, thanks to its superior computational performance.

Abstract Worldwide, electrostatic precipitators (ESP) have been extensively utilized to separate fine particles for diverse large-scale industrial applications. In this regard, this paper presents a novel approach for modeling the dust-loaded ESP on the fine computational domain where the need for a fast solver arises. Unlike the previously published numerical techniques, the finite difference method (FDM) integrated with a full multi-grid method (FMG), labeled FDM-FMG, is developed to resolve Poisson and continuity equations on one fine computational domain. For clean and dust-loaded ESP, the proposed FMG is checked versus successive over-relaxation (SOR) on fine domains where the proposed one is greatly transcendent in terms of convergence characteristics and hence the computational performance (CPU time). For the first time, two major issues are highlighted and solved: the first concerning issue is the chosen ion mobility as an important factor in the simulation results and the second one is choosing an optimal computational grid for dust loaded precipitators that grantees both low truncation and roundoff errors, results in well-matched with experimental measurements nominated in the previous publishing. The novel idea of working on various grid sizes and tracking the optimal ones gives the FDM-FMG an advantage of predicting a precise picture for the electrical situations in industrial ESP over the other numerical techniques. After all, the impact of changing the spacing between the different wires and the height of the ionized wires on the distributions of current, ion, and particle charge densities on the ground are deeply simulated and presented in dust-loaded ESP. The proposed FDM-FMG can be a promising tool for the designers and manufacturers of precipitators, thanks to its superior computational performance.

Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

Economic dispatch aims to determine the optimal generated power from the generation units to meet the required load at the lowest fuel cost. In this paper, a stochastic whale optimization (SWO) method is proposed to solve the economic dispatch problem. Whale optimization algorithm is enhanced using mutation and crossover operators. To test the proposed method two systems (3 and 10 generating units) are tested. We compare the proposed SWO algorithm with whale optimization algorithm, artificial bee colony algorithm, dragonfly algorithm, ant lion algorithm, gray wolf optimization, and whale optimization algorithm with mutation only. The obtained results demonstrate the high efficiency of the proposed method compared with the other methods.

Récemment, l'Internet des objets (IoT) a joué un rôle important dans la croissance et le développement des centrales électriques numérisées tout en offrant des opportunités ambitieuses, en particulier la surveillance en temps réel et la cybersécurité. À cet égard, cet article présente une nouvelle architecture IoT pour la surveillance en ligne de l'état de l'appareillage de commutation à isolation gazeuse (SIG) au lieu des méthodes d'observation traditionnelles. L'architecture IoT proposée est dérivée du concept du système cyber-physique (CPS) dans l'Industrie 4.0. Cependant, les cyber-attaques et la classification des défauts d'isolation SIG représentent les principaux défis contre la mise en œuvre de la topologie IoT pour la surveillance et le suivi en ligne de l'état SIG. À cette fin, des techniques avancées d'apprentissage automatique sont utilisées pour détecter les cyberattaques afin de mener le paradigme et la vérification. Différents scénarios de test sur divers défauts dans les SIG sont effectués pour démontrer l'efficacité de l'architecture IoT proposée. Les caractéristiques de séquence d'impulsions de décharge partielle sont extraites pour chaque défaut afin de représenter les entrées de l'architecture IoT. Les résultats confirment que l'architecture IoT proposée basée sur la technique d'apprentissage automatique, c'est-à-dire le boost de gradient extrême (XGBoost), peut visualiser tous les défauts du SIG avec différentes alarmes, en plus de montrer efficacement les cyber-attaques sur les réseaux. En outre, les défauts du SIG et les fausses données dues aux cyber-attaques sont reconnus et présentés sur le tableau de bord de la plate-forme IoT proposée avec une grande précision et une visualisation plus claire pour améliorer la prise de décision sur l'état du SIG. Recientemente, el Internet de las cosas (IoT) tiene un papel importante en el crecimiento y desarrollo de las centrales eléctricas digitalizadas, al tiempo que ofrece oportunidades ambiciosas, específicamente el monitoreo en tiempo real y la ciberseguridad. En este sentido, este documento presenta una novedosa arquitectura IoT para el monitoreo en línea del estado de los interruptores aislados por gas (GIS) en lugar de los métodos de observación tradicionales. La arquitectura IoT propuesta se deriva del concepto del sistema ciberfísico (CPS) en la Industria 4.0. Sin embargo, los ciberataques y la clasificación de los defectos de aislamiento GIS representan los principales desafíos contra la implementación de la topología IoT para el monitoreo y seguimiento en línea del estado GIS. Para este propósito, se utilizan técnicas avanzadas de aprendizaje automático para detectar ciberataques para llevar a cabo el paradigma y la verificación. Se realizan diferentes escenarios de prueba sobre diversos defectos en GIS para demostrar la efectividad de la arquitectura IoT propuesta. Las características de la secuencia de pulso de descarga parcial se extraen para cada defecto para representar las entradas para la arquitectura IoT. Los resultados confirman que la arquitectura IoT propuesta basada en la técnica de aprendizaje automático, es decir, el impulso de gradiente extremo (XGBoost), puede visualizar todos los defectos en el SIG con diferentes alarmas, además de mostrar los ciberataques en las redes de manera efectiva. Además, los defectos de los SIG y los datos falsos debidos a los ciberataques se reconocen y presentan en el panel de control de la plataforma IoT propuesta con alta precisión y visualización más aclarada para mejorar la toma de decisiones sobre el estado de los SIG. Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision-making about the GIS status. في الآونة الأخيرة، لعب إنترنت الأشياء (IoT) دورًا مهمًا في نمو وتطوير محطات الطاقة الكهربائية الرقمية مع توفير فرص طموحة، وتحديدًا المراقبة في الوقت الفعلي والأمن السيبراني. في هذا الصدد، تقدم هذه الورقة بنية جديدة لإنترنت الأشياء للمراقبة عبر الإنترنت لحالة المفاتيح الكهربائية المعزولة بالغاز (GIS) بدلاً من طرق المراقبة التقليدية. تُشتق بنية إنترنت الأشياء المقترحة من مفهوم النظام الفيزيائي السيبراني (CPS) في الصناعة 4.0. ومع ذلك، تمثل الهجمات الإلكترونية وتصنيف عيوب عزل نظم المعلومات الجغرافية التحديات الرئيسية أمام تنفيذ طوبولوجيا إنترنت الأشياء لرصد وتتبع حالة نظم المعلومات الجغرافية عبر الإنترنت. لهذا الغرض، يتم استخدام تقنيات التعلم الآلي المتقدمة للكشف عن الهجمات الإلكترونية لإجراء النموذج والتحقق. يتم إجراء سيناريوهات اختبار مختلفة على العيوب المختلفة في نظم المعلومات الجغرافية لإثبات فعالية بنية إنترنت الأشياء المقترحة. يتم استخراج ميزات تسلسل نبضة التفريغ الجزئي لكل عيب لتمثيل مدخلات بنية إنترنت الأشياء. تؤكد النتائج أن بنية إنترنت الأشياء المقترحة بناءً على تقنية التعلم الآلي، أي تعزيز التدرج الشديد (XGBoost)، يمكنها تصور جميع العيوب في نظم المعلومات الجغرافية بإنذارات مختلفة، إلى جانب إظهار الهجمات الإلكترونية على الشبكات بشكل فعال. علاوة على ذلك، يتم التعرف على عيوب نظم المعلومات الجغرافية والبيانات المزيفة بسبب الهجمات الإلكترونية وعرضها على لوحة معلومات منصة إنترنت الأشياء المقترحة بدقة عالية وتصور أكثر وضوحًا لتعزيز عملية صنع القرار بشأن حالة نظم المعلومات الجغرافية.