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Resource optimization is a major factor in the assessment of the effectiveness of renewable energy systems. Various methods have been utilized by different researchers in planning and sizing the grid-connected PV systems. This paper analyzes the optimal photovoltaic (PV) array and inverter sizes for a grid-connected PV system. Unmet load, excess electricity, fraction of renewable electricity, net present cost (NPC) and carbon dioxide (CO2) emissions percentage are considered in order to obtain optimal sizing of the grid-connected PV system. An optimum result, with unmet load and excess electricity of 0%, for serving electricity in Makkah, Saudi Arabia is achieved with the PV inverter size ratio of R = 1 with minimized CO2 emissions. However, inverter size can be downsized to 68% of the PV nominal power to reduce the inverter cost, and hence decrease the total NPC of the system.

La stabilité est une exigence principale du système d'alimentation électrique pour son fonctionnement impeccable, sûr et économique. Les oscillations à basse fréquence (LFO), couramment observées dans les systèmes d'alimentation interconnectés, initient la possibilité d'instabilité et, par conséquent, nécessitent des soins sophistiqués. Cet article propose une approche originale pour ajuster les paramètres du stabilisateur du système d'alimentation (PSS), qui joue un rôle crucial dans les réseaux du système d'alimentation pour amortir les oscillations indésirables. Le procédé d'ensemble combine de multiples techniques d'apprentissage automatique et a été utilisé pour régler les paramètres PSS en temps réel pour deux réseaux de système d'alimentation connectés au PSS. Le premier système est un système d'alimentation à bus infini mono-machine, tandis que le second est un dispositif de contrôleur de flux de puissance unifié (UPFC). Le modèle d'ensemble proposé basé sur l'algorithme de recherche de retour en arrière (BSA) est formé en combinant trois techniques d'apprentissage automatique (ML), à savoir la machine d'apprentissage extrême (ELM), le système neurogénétique (NG) et la programmation génétique multigénique (MGGP). Pour valider la stabilité du réseau, des valeurs propres, des paramètres statistiques bien reconnus et des rapports d'amortissement minimaux ont été analysés, en plus des résultats de simulation dans le domaine temporel. En outre, des résultats pour diverses conditions de chargement ont été préparés pour vérifier la robustesse du modèle proposé. Une étude comparative de l'approche proposée avec les modèles NG, ELM, MGGP et deux cas de référence ainsi que la méthode conventionnelle valideront la supériorité de l'approche ML employée. La estabilidad es un requisito principal del sistema de energía eléctrica para su funcionamiento impecable, seguro y económico. Las oscilaciones de baja frecuencia (LFO), comúnmente observadas en los sistemas de energía interconectados, inician la posibilidad de inestabilidad y, por lo tanto, requieren un cuidado sofisticado para tratarlas. Este documento propone un enfoque original para ajustar los parámetros del estabilizador del sistema de energía (PSS), que desempeña un papel crucial en las redes del sistema de energía para amortiguar las oscilaciones no deseadas. El método Ensemble combina múltiples técnicas de aprendizaje automático y se ha utilizado para ajustar los parámetros PSS en tiempo real para dos redes de sistemas de energía conectadas a PSS. El primer sistema es un sistema de alimentación de bus infinito de una sola máquina, mientras que el segundo es un dispositivo controlador de flujo de energía unificado (UPFC). El modelo de conjunto propuesto basado en el algoritmo de búsqueda de retroceso (BSA) se forma combinando tres técnicas de aprendizaje automático (ML), a saber, la máquina de aprendizaje extremo (ELM), el sistema neurogenético (NG) y la programación genética multigénica (MGGP). Para validar la estabilidad de la red, se analizaron los valores propios, los parámetros estadísticos bien reconocidos y las relaciones mínimas de amortiguación, además de los resultados de la simulación en el dominio del tiempo. Además, se prepararon resultados para diversas condiciones de carga para comprobar la robustez del modelo propuesto. Un estudio comparativo del enfoque propuesto con los modelos NG, ELM, MGGP y dos casos de referencia junto con el método convencional validará la superioridad del enfoque de ML empleado. Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach. الاستقرار هو مطلب أساسي لنظام الطاقة الكهربائية لتشغيله الخالي من العيوب والآمن والاقتصادي. تؤدي التذبذبات منخفضة التردد (LFOs)، التي تشاهد عادة في أنظمة الطاقة المترابطة، إلى احتمال عدم الاستقرار، وبالتالي تتطلب عناية متطورة للتعامل معها. تقترح هذه الورقة نهجًا أصليًا لضبط معلمات مثبت نظام الطاقة (PSS)، والذي يلعب دورًا حاسمًا في شبكات نظام الطاقة لتخفيف التذبذبات غير المرغوب فيها. تجمع طريقة المجموعة بين العديد من تقنيات التعلم الآلي وقد تم استخدامها لضبط معلمات الدعم النفسي الاجتماعي في الوقت الفعلي لشبكتين من شبكات نظام الطاقة المتصلة بالدعم النفسي الاجتماعي. النظام الأول هو نظام طاقة ناقل لانهائي لآلة واحدة، في حين أن الثاني هو جهاز تحكم موحد في تدفق الطاقة (UPFC). يتم تشكيل نموذج المجموعة المقترح القائم على خوارزمية البحث التراجعي (BSA) من خلال الجمع بين ثلاث تقنيات للتعلم الآلي (ML)، وهي آلة التعلم المتطرف (ELM)، ونظام الجينات العصبية (NG)، والبرمجة الجينية متعددة الجينات (MGGP). للتحقق من استقرار الشبكة، تم تحليل القيم الذاتية، والمعلمات الإحصائية المعترف بها جيدًا، والحد الأدنى من نسب التخميد، إلى جانب نتائج محاكاة النطاق الزمني. علاوة على ذلك، تم إعداد نتائج لظروف التحميل المختلفة للتحقق من متانة النموذج المقترح. ستؤدي الدراسة المقارنة للنهج المقترح مع نماذج NG و ELM و MGGP وحالتين مرجعيتين جنبًا إلى جنب مع الطريقة التقليدية إلى التحقق من تفوق نهج غسل الأموال المستخدم.

Substituting a single large power grid into various manageable microgrids is the emerging form for maintaining power systems. A microgrid is usually comprised of small units of renewable energy sources, battery storage, combined heat and power (CHP) plants and most importantly, an energy management system (EMS). An EMS is responsible for the core functioning of a microgrid, which includes establishing continuous and reliable communication among all distributed generation (DG) units and ensuring well-coordinated activities. This research focuses on improving the performance of EMS. The problem at hand is the optimal scheduling of the generation units and battery storage in a microgrid. Therefore, EMS should ensure that the power is shared among different sources following an imposed scenario to meet the load requirements, while the operational costs of the microgrid are kept as low as possible. This problem is formulated as an optimization problem. To solve this problem, this research proposes an enhanced version of the most valuable player algorithm (MVPA) which is a new metaheuristic optimization algorithm, inspired by actual sporting events. The obtained results are compared with numerous well-known optimization algorithms to validate the efficiency of the proposed EMS.

This work proposes a modified differential evolution (MDE) based maximum power point tracker (MPPT) for photovoltaic (PV) system under partial shading condition. The proposed MDE does not involve any random numbers; hence, consistency of MPP tracking always prevails. Besides, it only contains one tuning parameter, i.e., mutation factor, which significantly simplifies the implementation strategy and therefore a low-cost micro-controller can be used for its realization. Despite the simpler MPPT structure, for each tested shaded curve, MDE always converges toward the global MPP within 12 perturbations. Performance wise, it outperforms another evolutionary algorithm, namely particle swarm optimization (PSO), which frequently traps at local MPP in shading conditions. The proposed MDE also works accurately for the measured data profile of a tropical country during 9.00 am to 5.00 pm, where it attains 99.5% average tracking efficiency.

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Abstract The tilt angle of photovoltaic (PV) panels is a major factor affecting the amount of solar radiation falling on the panel surface. The optimum tilt angle depends on the position of the sun, latitude and local geographical characteristics. This paper investigates PV panel optimum tilt angles for various cities in the Kingdom of Saudi Arabia. For simulation purposes, horizontal solar radiation data for the study cities was obtained from NASA. MATLAB software was used to optimize tilt angle by maximizing solar radiation. Experiments were conducted to validate the theoretical requirement for negative tilt angles during summer. Results demonstrate that adjusting tilt angles six times per year harvests 99.5% of the solar radiation that could be attained with daily PV panel adjustment.

Abstract This research contributes to the ongoing discussions about the grid-connected solar photovoltaic (PV) systems and draws attention to the optimal design by considering various PV array tracking systems towards enhancing the power generation. The PV tracking system configurations considered in this study include horizontal-axis (monthly adjustment, weekly adjustment, daily adjustment, continuous adjustment), vertical-axis (continuous adjustment), and a two-axis tracking system. HOMER (Hybrid Optimization of Multiple Energy Resources) software is employed whereas the actual data required by the model have been collected in Makkah, Saudi Arabia. The results show that the two-axis tracker can produce 34% more power than the fixed system, while the vertical axis tracker with continuous adjustment was able to generate up to 20% more power than the fixed system. Horizontal tracker with continuous adjustment shows the highest net present cost (NPC) and the highest levelized cost of energy (LCOE), with a high penetration of solar energy to the grid. For the case of Makkah, the vertical axis tracker with continuous adjustment is the best option as it has low LCOE and NPC values with a positive return on investment (ROI) as well as high renewable energy penetration to the grid, which enhances its viability for a utility-size solar PV grid-connected system.