• Energy Research

Las plantas solares fotovoltaicas (PV) a gran escala desempeñan un papel esencial para satisfacer la creciente demanda de energía en los últimos tiempos. Por lo tanto, con el propósito de lograr la mayor potencia cosechada en las condiciones de sombreado parcial, así como de proteger la matriz fotovoltaica de la calamidad del punto caliente, la estrategia de reconfiguración fotovoltaica se establece como un procedimiento eficiente. Esto se realiza mediante la redistribución de los módulos fotovoltaicos de acuerdo con sus niveles de sombreado. Motivados por esto, los autores de este artículo han introducido un nuevo algoritmo basado en la población que se conoce como algoritmo de depredadores marinos (MPA) para reestructurar dinámicamente la matriz fotovoltaica. Además, se introduce una función objetivo novedosa para mejorar el rendimiento del algoritmo en lugar de utilizar la función objetivo ponderada regular en la literatura. La efectividad de los algoritmos propuestos basados en la función objetivo novedosa se evalúa utilizando varias métricas como el factor de relleno, las pérdidas por desajuste, el porcentaje de pérdida de potencia y el porcentaje de mejora de potencia. Además, los resultados obtenidos se comparan con una conexión regular de enlace cruzado total (TCT), optimización de forrajeo de mantarrayas (MRFO), optimizador de halcón de Harris (HHO) y técnicas de reconfiguración basadas en optimizador de enjambre de partículas (PSO). Además, para demostrar la idoneidad de los métodos propuestos, se consideran y evalúan matrices fotovoltaicas a gran escala de $ 16\ times16 $ y $ 25\ times25 $. Los resultados revelan que el MPA mejoró la potencia de la matriz fotovoltaica en un porcentaje del 28,6 %, 2,7 % y 5,7 % en los casos de matrices fotovoltaicas de $ 9\ times9 $, $ 16\ times16 $ y $ 25\ times25 $, respectivamente. Las comparaciones exhaustivas respaldan que el MPA muestra una dispersión de sombra exitosa; por lo tanto, el número de picos múltiples en las características fotovoltaicas se ha reducido, y se han cosechado altos valores de potencia con el menor tiempo medio de ejecución en comparación con PSO, HHO y MRFO. Además, se ha realizado la prueba de rango con signo de Wilcoxon para confirmar la fiabilidad y aplicabilidad del enfoque propuesto también para las matrices fotovoltaicas a gran escala. Les centrales solaires photovoltaïques (PV) à grande échelle jouent un rôle essentiel pour répondre à la demande croissante d'énergie ces derniers temps. Par conséquent, dans le but d'atteindre la puissance récoltée la plus élevée dans les conditions d'ombrage partiel ainsi que de protéger le réseau photovoltaïque contre la calamité du point chaud, la stratégie de reconfiguration du PV est établie comme une procédure efficace. Ceci est effectué par redistribution des modules PV en fonction de leurs niveaux d'ombrage. Motivés par cela, les auteurs de cet article ont introduit un nouvel algorithme basé sur la population qui est connu sous le nom d'algorithme des prédateurs marins (MPA) pour restructurer dynamiquement le réseau PV. De plus, une nouvelle fonction objective est introduite pour améliorer les performances de l'algorithme plutôt que d'utiliser la fonction objective pondérée régulière dans la littérature. L'efficacité des algorithmes proposés basés sur la nouvelle fonction objective est évaluée à l'aide de plusieurs paramètres tels que le facteur de remplissage, les pertes de désadaptation, le pourcentage de perte de puissance et le pourcentage d'amélioration de puissance. En outre, les résultats obtenus sont comparés à une connexion totale croisée (TCT) régulière, à une optimisation de recherche de nourriture par raie manta (MRFO), à un optimiseur harris hawk (HHO) et à des techniques de reconfiguration basées sur un optimiseur d'essaim de particules (PSO). De plus, pour démontrer la pertinence des méthodes proposées, des réseaux photovoltaïques à grande échelle de $ 16\ times16 $ et $ 25\times25 $ sont considérés et évalués. Les résultats révèlent que le MPA a augmenté la puissance du réseau photovoltaïque de 28,6 %, 2,7 % et 5,7 % dans les cas des réseaux photovoltaïques $ 9\ times9 $ , $ 16\ times16 $ et $ 25\ times25 $ , respectivement. Les comparaisons complètes confirment que la MPA montre une dispersion d'ombre réussie ; par conséquent, le nombre de pics multiples dans les caractéristiques PV a diminué, et des valeurs élevées de puissance ont été récoltées avec le temps d'exécution moyen le plus faible par rapport à PSO, HHO et MRFO. De plus, le test de Wilcoxon a été réalisé pour confirmer la fiabilité et l'applicabilité de l'approche proposée pour les réseaux photovoltaïques à grande échelle. Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of $16\times16$ and $25\times25$ are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of $9\times9$ , $16\times16$ and $25\times25$ PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion; hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale arrays as well. تلعب محطات الطاقة الشمسية الكهروضوئية واسعة النطاق دورًا أساسيًا في توفير الطلب المتزايد على الطاقة في الآونة الأخيرة. لذلك، لغرض تحقيق أعلى قوة محصودة في ظل ظروف التظليل الجزئي وكذلك حماية المصفوفة الكهروضوئية من كارثة البقعة الساخنة، يتم إنشاء استراتيجية إعادة تشكيل الكهروضوئية كإجراء فعال. يتم ذلك عن طريق إعادة توزيع الوحدات الكهروضوئية وفقًا لمستويات التظليل الخاصة بها. بدافع من هذا، قدم المؤلفون في هذه المقالة خوارزمية جديدة قائمة على السكان تُعرف باسم خوارزمية الحيوانات المفترسة البحرية (MPA) لإعادة هيكلة المصفوفة الكهروضوئية ديناميكيًا. علاوة على ذلك، يتم تقديم وظيفة موضوعية جديدة لتعزيز أداء الخوارزمية بدلاً من استخدام وظيفة الهدف المرجحة العادية في الأدبيات. يتم تقييم فعالية الخوارزميات المقترحة بناءً على وظيفة الهدف الجديدة باستخدام العديد من المقاييس مثل عامل التعبئة، وفقدان عدم التطابق، والنسبة المئوية لفقدان الطاقة، والنسبة المئوية لتعزيز الطاقة. إلى جانب ذلك، تتم مقارنة النتائج التي تم الحصول عليها مع اتصال منتظم إجمالي التعادل (TCT)، وتحسين البحث عن مانتا راي (MRFO)، ومحسن هاريس هوك (HHO) وتقنيات إعادة التكوين القائمة على محسن سرب الجسيمات (PSO). علاوة على ذلك، لإثبات ملاءمة الطرق المقترحة، يتم النظر في المصفوفات الكهروضوئية واسعة النطاق التي تبلغ 16 دولارًا\ مرات 16 دولارًا و 25 دولارًا\ مرات 25 دولارًا وتقييمها. كشفت النتائج أن MPA عززت طاقة المصفوفة الكهروضوئية بنسبة 28.6 ٪ و 2.7 ٪ و 5.7 ٪ في حالات المصفوفات الكهروضوئية 9 $\ times9 $ و 16 $\ times16 $ و 25 $\ times25 $ على التوالي. تؤيد المقارنات الشاملة أن الآلام والكروب الذهنية تظهر تشتتًا ناجحًا للظل ؛ وبالتالي انخفض عدد القمم المتعددة في الخصائص الكهروضوئية، وتم حصاد القيم العالية للقوة بأقل وقت للتنفيذ مقارنة بـ PSO و HHO و MRFO. علاوة على ذلك، تم إجراء اختبار تصنيف ويلكوكسون الموقّع لتأكيد موثوقية وقابلية تطبيق النهج المقترح للمصفوفات الكهروضوئية واسعة النطاق أيضًا.

Abstract Integrating distributed generators (DGs) in radial distribution networks plays a vital role in improving the system performance via enhancing the bus voltage and minimizing the system losses. Nonetheless, uncoordinated DGs integration may cause technical issues if they are not efficiently planned, controlled, and operated. Therefore, this paper proposes a new methodology based on the recent metaheuristic chimp optimizer approach (CO) to identify DGs’ optimal allocations and rated powers. This work's objective function is minimizing the total active power loss of the network; the considered constraints are load flow, buses’ voltages, and transmission lines. The proposed CO is characterized by ease of implementation, high convergence rate, and avoiding stuck in local optima. CO is adapted such that the first design variables are integer numbers representing the locations of DGs while the others are assigned to be the DGs’ powers. The proposed CO is applied on three radial networks, 33-bus, 69-bus, and 119-bus, moreover two modes of DGs, unity power factor (DGs generate only active power) and non-unity power factor (DGs generate active and reactive powers), are studied. The results obtained via the proposed CO are compared to other reported approaches of exhaustive load flow (ELF), genetic algorithm (GA), and different programmed approaches of particle swarm optimizer (PSO) and Archimedes optimization algorithm (AOA). The obtained results confirmed the superiority and reliability of the proposed CO methodology in achieving a minor power loss via installing the DGs in the correct sites.

Abstract Environmental conditions have a strong influence on the behavior and quality of the generated photovoltaic (PV) power. The partial sharing condition is a natural phenomenon that reduces the lifetime of the PV array and the effectiveness of the entire system. Dispersing the shadow equally over PV panels is the solution to avoid the negative impact of the partial shading condition. Accordingly, PV array reconfiguration has become an attractive topic of study for researchers. In this paper, the authors provide a comprehensive review of all the schemes proposed for the PV reconfiguration system. In addition, a comparative study among all the published approaches and the challenges facing each approach are presented. Future research topics that highlight new methods to produce higher power from an available PV system are discussed.

The operation of photovoltaic (PV) module under partial shadow conditions considers a big challenge for most researchers due to power loss and hot spots that reduce the amount of extracted power. In such an operation, the panel voltage–power curve has a unique global maximum power (GMP) to be tracked. Therefore, this paper proposes a new maximum power point tracker (MPPT) implemented by Raspberry Pi 4-based embedded board programmed via two metaheuristic approaches of cuckoo search (CS) and particle swarm optimizer (PSO). The approaches are developed using python software programming language to adapt the duty cycle fed to the MOSFET of DC/DC boost converter connected to the panel terminals. The panel is simulated in Simulink/Matlab library to identify the GMP in each studied case. An experimental setup is conducted in the lab room of the college of Engineering, Jouf University, Saudi Arabia to assess the proposed tracker. Moreover, eight shade patterns are considered via covering 10% to 80% with step 10% of panel with shadow. Furthermore, statistical tests of the Wilcoxson sign rank test and ANOVA are conducted to assess the validity of the proposed tracker. The obtained results are compared to perturb and observe (P&O) and gray​ wolf optimizer (GWO). The PSO-based tracker achieved the best efficiency of 96.92%, the CS achieved 93.62%, and GWO get an efficiency of 93.15%. Additionally, on the side of Wilcoxson sign rank and ANOVA tests, the PSO outperformed CS and GWO. The results confirmed the superiority of the proposed Raspberry Pi system programmed via PSO over that of CS and GWO in enhancing the power generated from the panel operated at different partial shades.

Abstract An accurate identification of the parameters of solid oxide fuel cell (SOFC) models is the first step to provide a reliable design for an energy storage system using SOFC. Therefore, in the current work, a novel developed variant for the marine predators algorithm (MPA) is proposed based on comprehensive learning and dynamic multi-swarm approaches to extract highly accurate, precise, and efficient parameters of the SOFC model that achieve the closely matching between the actual and estimated system responses. The proposed comprehensive learning dynamic multi-swarm marine predators algorithm (CLDMMPA) is examined with two scenarios that are SOFC steady-state and dynamic state-based models under variable operating conditions. The results of the proposed algorithm are validated via an intensive comparison based on statistical metrics and non-parametric tests with other recent counterparts. Furthermore, the accuracy of identified parameters in the case of the dynamic model is evaluated with two cases of sudden power load variations, and the dynamic responses of the stack voltage and current are analyzed. The comparisons and analyses have confirmed the superiority of the proposed CLDMMPA to provide highly accurate identified parameters that exhibit the minimum deviation between the measured and estimated stack current–voltage and stack current–power curves. Moreover, the consistency of the CLDMMPA results and the smooth decaying in its convergence curves are other remarkable points superior to other counterparts.

Abstract Photovoltaic modeling has attracted researchers’ attention worldwide because of its importance in the photovoltaic system design. Therefore, several photovoltaic models have been introduced as static and dynamic photovoltaic models. Moreover, a novel fractional order dynamic photovoltaic model has been recently developed to enhance the accuracy and flexibility of the conventional integral order one. The unknown parameters of these models should be extracted accurately to achieve a proper photovoltaic system design and operation. In this work, novel Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are introduced, where the Heterogeneous Comprehensive Learning Particle Swarm Optimizer is combined with ten different chaos maps to adapt its parameters. Six Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are proposed in addition to the standard Heterogeneous Comprehensive Learning Particle Swarm Optimizer version to identify the parameters of both the static and the dynamic models based on different experimental datasets. To demonstrate the superiority of the developed variants, their results are compared to the most recent state-of-the-art algorithms with the aid of statistical analysis methods. The main outcome is that, in both of the static and the dynamic photovoltaic models, the Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants show their efficiency, accuracy and robustness not only over Heterogeneous Comprehensive Learning Particle Swarm Optimizer but also over recently published algorithms. They provide better fitting relative to the experimental datasets with the least deviation error and the fastest convergence speed as well. In the case of static models, the fourth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with an iterative map for the single diode model, the third variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with singer map for the double diode model of solar cell. On the other hand, for the dynamic models, the second Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variant with sinusoidal map for the integral order dynamic photovoltaic model and the sixth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with Gauss/mouse map for the fractional order dynamic photovoltaic model offer the best performance.