• Energy Research
• 2021-2025close

Solar energy is one of the most abundant and widely available renewable energy sources. It can be harnessed using photovoltaic panels on top of buildings to reduce dependence on the electrical grid and to achieve the status of net-zero energy building. However, the rooftop coverage by solar panels can modify the heat interface between the roof surface and its surrounding environment. This can alter a building's energy demand for heating, ventilation, and air conditioning. Such an impact on a building's energy demand is highly correlated with its roof structure and climate. In this work, three-dimensional distributed thermal models of the bare and photovoltaic added rooftop ensembles are developed to simulate the heat gain/loss associated with the roof structure for monthly mean diurnal cycles. This work considers the low-rise, high-density building style and hot semi-arid climate of Faisalabad, Pakistan to quantify the impact of a rooftop photovoltaic on the roof-related thermal load of a building. Results depict a 42.58% reduced heat loss from the photovoltaic added roof structure during winter and a 1.98% increase in heat gain during summer. This reduces the electricity demand for indoor heating during winter and slightly increases it for indoor cooling during summer. The obtained results prove the significance of this work and provide guidelines to energy policymakers, the construction industry, and energy consumers. Moreover, this work provides a better understanding of the building's energy demand for heating, ventilation, and air conditioning with a rooftop photovoltaic system and its net-zero energy requirements, which are pivotal for modern construction.

AbstractThis work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Over the past few years, the use of DC-DC buck-boost converters for Photovoltaic (PV) in renewable energy applications has increased for better results. One of the main issues with this type of converter is that output voltage is achieved with the undesired ripples. Many models are available in the literature to address this issue, but very limited work is available that achieves the desired goal using deep learning-based models. Whenever it comes to the PV, then it is further limited. Here, a deep learning-based model is proposed to reduce the steady-state time and achieve the desired buck- or boost mode for PV modules. The deep learning-based model is trained using data collected from the conventional PID controller. The output voltage of the experimental setup is 12V while the input voltage from the PV modules is 10V (when the sunlight decreases) to 24V (for 3.6 kVA) to 48V (for more than 5 kVA). It is among the few models using a single big battery (12V) for off-grid and on-grid for a single building. Experimental results are validated using objective measures. The proposed model outperforms the conventional PID controller-based buck-boost converters. The results clearly show improved performance in terms of steady-state and less overshoot.

The purpose of this study is to investigate the potential of airborne particulate matter (PM10 and PM2.5) and its impact on the performance of the photovoltaic (PV) system installed in the Sargodha region, being affected by the crushing activities in the hills. More than 100 stone crushers are operating in this region. Four stations within this region are selected for taking samples during the summer and winter seasons. Glass–fiber papers are used as a collection medium for particulate matter (PM) in a high-volume sampler. The concentration of PM is found above the permissible limit at all selected sites. The chemical composition, concentration, and the formation of particulate matter (PM10 and PM2.5) layers on the surface of the photovoltaic module varies significantly depending on the site’s location and time. The accumulation of PM layers on the PV module surface is one of the operating environmental factors that cause significant reduction in PV system performance. Consequently, it leads to power loss, reduction of service life, and increase in module temperature. For the PV system’s performance analysis, two PV systems are installed at the site, having higher PM concentration. One system is cleaned regularly, while the other remains dusty. The data of both PV systems are measured and compared for 4 months (2 months for the summer season and 2 months for the winter season). It is found that when the level of suspended particulate matter (PM10 and PM2.5) increases, the energy generation of the dusty PV system (compared to the cleaned one) is reduced by 7.48% in May, 7.342% in June, 10.68% in December, and 8.03% in January. Based on the obtained results, it is recommended that the negative impact of PM on the performance of the PV system should be considered carefully during the decision-making process of setting solar energy generation targets in the regions with a high level of particulate matter.

This article presents a comparative analysis of the latest swarm-based optimization approaches under partial shading conditions (PSCs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The swarm-based MPPT algorithms are stochastic meta-heuristic approaches that have become very popular recently in various applications owing to the drawbacks of conventional MPPT algorithms under different operating conditions. A comprehensive review of the recent research on these algorithms is carried out particularly focusing on the PSCs. The advantages, disadvantages, applications, computational efficiency, and stability of these algorithms are critically surveyed in detail. Moreover, to analyze the comparative performance of the swarm-based algorithms, a special case study is conducted in the MATLAB/Simulink environment for a solar-powered DC load with a boost converter. The performance of seven swarm-based MPPT techniques is evaluated in this case study in terms of their settling time, convergence speed, overshoot, and efficiency under different levels of PSCs. The statistical analysis for 30 simulation runs shows that under heavier shading conditions, the grasshopper optimization algorithm (GOA) and salp swarm algorithm (SSA) outperform other swarm-based MPPT algorithms. It is envisaged that this work will be a one-stop source of guidance for researchers working in the field of MPP optimization under PSCs.

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

La répartition optimale de la puissance réactive (ORPD) a un impact crucial pour améliorer la sécurité, la fiabilité et le fonctionnement économique du système d'alimentation électrique. L'ORPD est un problème variable non linéaire, non convexe et mixte, qui a été résolu par de nombreux chercheurs via différents algorithmes méta-heuristiques au cours de la dernière décennie. Dans ce travail, un nouvel algorithme appelé algorithme sinus-cosinus (SCA) est utilisé pour résoudre le problème ORPD en considérant à la fois les contraintes variables de contrôle dépendantes et indépendantes. SCA a été testé et validé sur les systèmes d'alimentation standard 14, 30 et 57 bus. Pour valider la supériorité de l'algorithme proposé, les résultats obtenus grâce à SCA sont comparés aux résultats publiés récents obtenus grâce à l'optimisation des essaims de particules (PSO), l'optimisation basée sur l'enseignement et l'apprentissage des os nus gaussiens modifiés (BBTLBO), l'optimisation des colonies d'abeilles fourmis (ABCO), l'algorithme d'optimisation des baleines (WOA) et les algorithmes de recherche de retour en arrière (BSA). Les résultats obtenus avec SCA montrent l'amélioration de la minimisation des pertes de puissance. Ainsi, avec le système standard à 14 bus, les pertes de puissance sont minimisées de 0,04 % à 4,78 %. Alors que, avec le bus standard 30, les pertes de puissance sont minimisées de 0,4% à 3,4% et avec le bus standard 57, les pertes de puissance sont réduites de 0,9% à 1,99%. En outre, une analyse comparative avec 30 passages indépendants sur les systèmes de bus susmentionnés est effectuée pour examiner le fonctionnement de la méthode proposée en termes de fonction de densité de probabilité (PDF) et de fonction de densité cumulative (CDF). Pour une telle analyse, des algorithmes méta-heuristiques bien connus tels que PSO, WOA, l'évolution différentielle (DE) sont comparés au SCA proposé pour résoudre le problème ORPD. Les résultats de cette analyse montrent clairement que l'algorithme proposé est robuste, efficace et facile à calculer pour résoudre le problème ORPD par rapport aux algorithmes méta-heuristiques existants. El despacho óptimo de energía reactiva (ORPD) tiene un impacto crucial para mejorar la seguridad, la confiabilidad y el funcionamiento económico del sistema de energía eléctrica. ORPD es un problema de variables no lineal, no convexo y mixto, que ha sido resuelto por muchos investigadores a través de diferentes algoritmos metaheurísticos durante la última década. En este trabajo, se utiliza un nuevo algoritmo llamado algoritmo seno-coseno (SCA) para resolver el problema ORPD al considerar las restricciones de las variables de control dependientes e independientes. SCA ha sido probado y validado en los sistemas de alimentación estándar 14, 30 y 57-bus. Para validar la superioridad del algoritmo propuesto, los resultados obtenidos a través de SCA se comparan con los resultados publicados recientemente obtenidos a través de la optimización de enjambre de partículas (PSO), la optimización basada en la enseñanza-aprendizaje gaussiana modificada (BBTLBO), la optimización de colonias de hormigas (ABCO), el algoritmo de optimización de ballenas (WOA) y los algoritmos de búsqueda de retroceso (BSA). Los resultados obtenidos utilizando SCA muestran la mejora en la minimización de las pérdidas de potencia. Por lo tanto, con el sistema estándar de 14 buses, las pérdidas de potencia se minimizan de 0.04% a 4.78%. Mientras que, con el bus 30 estándar, las pérdidas de potencia se minimizan del 0,4% al 3,4% y con el bus 57 estándar, las pérdidas de potencia se reducen del 0,9% al 1,99%. Además, se realiza un análisis comparativo con 30 ejecuciones independientes en los sistemas de bus mencionados anteriormente para examinar el funcionamiento del método propuesto en términos de función de densidad de probabilidad (PDF) y función de densidad acumulada (CDF). Para dicho análisis, se comparan algoritmos metaheurísticos bien conocidos como PSO, WOA, evolución diferencial (DE) con SCA propuesto para resolver el problema ORPD. Los resultados de este análisis muestran claramente que el algoritmo propuesto es robusto, efectivo y computacionalmente fácil para resolver el problema ORPD en comparación con los algoritmos metaheurísticos existentes. Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching–learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms. إن إرسال الطاقة التفاعلية الأمثل (ORPD) له تأثير حاسم لتعزيز السلامة والموثوقية والتشغيل الاقتصادي لنظام الطاقة الكهربائية. ORPD هي مشكلة غير خطية وغير محدبة ومتغيرة مختلطة، والتي تم حلها من قبل العديد من الباحثين عبر خوارزميات استدلالية مختلفة خلال العقد الماضي. في هذا العمل، يتم استخدام خوارزمية جديدة تسمى خوارزمية جيب التمام (SCA) لحل مشكلة ORPD من خلال النظر في كل من قيود متغيرات التحكم التابعة والمستقلة. تم اختبار SCA والتحقق من صحتها على أنظمة الطاقة القياسية 14 و 30 و 57 حافلة. للتحقق من تفوق الخوارزمية المقترحة، تتم مقارنة النتائج التي تم الحصول عليها من خلال SCA بالنتائج المنشورة حديثًا والتي تم تحقيقها من خلال تحسين سرب الجسيمات (PSO)، والتحسين القائم على تعليم وتعلم عظام الجاوس المعدلة (BBTLBO)، وتحسين مستعمرة النحل (ABCO)، وخوارزمية تحسين الحيتان (WOA) وخوارزميات البحث عن التتبع العكسي (BSA). تظهر النتائج التي تم تحقيقها باستخدام SCA التحسن في تقليل فقدان الطاقة. وبالتالي، مع نظام 14 حافلة القياسي، يتم تقليل فقدان الطاقة من 0.04 ٪ إلى 4.78 ٪. بينما، باستخدام 30 حافلة قياسية، يتم تقليل فقدان الطاقة من 0.4 ٪ إلى 3.4 ٪ ومع 57 حافلة قياسية، يتم تقليل فقدان الطاقة من 0.9 ٪ إلى 1.99 ٪. علاوة على ذلك، يتم إجراء تحليل مقارن مع 30 عملية تشغيل مستقلة على أنظمة الناقل المذكورة أعلاه لفحص أداء الطريقة المقترحة من حيث دالة كثافة الاحتمال (PDF) ودالة الكثافة التراكمية (CDF). بالنسبة لمثل هذا التحليل، تتم مقارنة الخوارزميات الاستدلالية المعروفة مثل PSO و WOA والتطور التفاضلي (DE) مع SCA المقترحة في حل مشكلة ORPD. تُظهر نتائج هذا التحليل بوضوح أن الخوارزمية المقترحة قوية وفعالة وسهلة حسابيًا في حل مشكلة ORPD مقارنة بالخوارزميات الاستدلالية التلوية الحالية.