• Energy Research

Abstract Microgrid systems, such as solar photovoltaic (PV) power and wind energy, integrated with diesel generators are promising energy supplies and are economically feasible for current and future use in relation to increased demands for energy and depletion of conventional sources. It is thus important to optimize the size of hybrid microgrid system (HMS) components, including storage, to determine system cost and reliability. In this paper, optimal sizing of a PV/wind/diesel HMS with battery storage is conducted using the Multi-Objective Self-Adaptive Differential Evolution (MOSaDE) algorithm for the city of Yanbu, Saudi Arabia. Using the multi-objective optimization approach, the objectives are treated simultaneously and independently, thereby leading to a reduction in computational time. One of the main criteria to consider when designing and optimizing the HMS is the energy management strategy, which is required to coordinate the different units comprising the HMS. The multi-objective optimization approach is then used to analyze the Loss of Power Supply Probability (LPSP), the Cost of Electricity (COE), and the Renewable Factor (RF) in relation to HMS cost and reliability and is tested using three case studies involving differing house numbers. Results verify its application in optimizing the HMS and in its practical implementation. In addition, optimization results using the proposed approach provided a set of design solutions for the HMS, which will assist researchers and practitioners in selecting the optimal HMS configuration. Moreover, it is important to select optimally sized HMS components to ensure that all load demands are met at the minimum energy cost and high reliability.

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.

Abstract The potential of hybrid wind/solar energy system in the west coast area of Saudi Arabia is analyzed in this paper. The investigation puts emphasis on the energy production and cost of energy from both wind turbine and photovoltaic (PV) in the hybrid system. Unmet electric load and excess electricity are taken into consideration. The annual average solar irradiation and wind speed considered in this study are 5.95 kWh/m 2 /day and 3.53 m/s, respectively. MATLAB and HOMER software are used to perform the technical and economic analyses of the hybrid system. As indicated from the simulation results, the PV array shares more electricity production than the wind turbine generator if both wind turbine and PV array are utilized in the wind/solar hybrid system with the same sizes. The wind levelized cost of energy is $0.149/kWh, which is more expensive than the solar energy of $0.0637/kWh. The energy cost of the hybrid system is dominated by battery and wind turbine expenses.

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.

Abstract In this paper, investigation of the performance of a support vector machine (SVM) and artificial neural networks (ANN) in predicting solar radiation on PV panel surfaces with particular tilt angles was carried out on two sites in Saudi Arabia. The diffuse, direct, and global solar radiation data on a horizontal surface were used as the basis for predicting the radiation on a tilted surface. The amount of data used is equivalent to 360 days, averaged from the 5-min basis data. By solving the tilt angle equation, an optimum value of solar radiation was obtained using a tilt angle of 16° and 37.5° for Jeddah and Qassim locations, respectively. The evaluation of performance and comparison of results of ANN as well as SVM and the measured/calculated data are made on the basis of statistical measures including the root mean square error (RMSE), coefficient of correlation (CC), and magnitude of relative error (MRE). The speed of computation of the algorithms is also considered for comparison. Results indicate that for Jeddah, the CC for SVM is between 0.918 and 0.967 for training and in the range of 0.91981–0.97641 for testing while that of ANN is in the range of 0.517–0.9692 for training and 0.0361–0.0961 for testing. For Qassim, the results are even better with CC of 0.999 for training and 0.987 for testing ANN showed higher values of MRE ranging between 0.19 and 1.16 and SVM is between 0.33 and 0.51 for training and testing respectively. In terms of speed of computation, it is observed that SVM is faster than ANN in predicting solar radiation data with a lower speed of 2.15 s compared to 4.56 s for ANN during training. Moreover, SVM has lower values of RMSE indicating that it is robust and has the capability to minimize errors during computations. Therefore, SVM has significantly higher accuracy, robust during computation and is faster in predicting the radiation on the tilted surfaces in comparison with ANN.

This paper aims to optimally design a PV/Wind/Diesel Hybrid Microgrid System (HMS) for a small number of houses considering load uncertainty for the city of Yanbu, Saudi Arabia. Designing such a hybrid system with all the renewable and non-renewable sources, storage devices, converters, and loads is a complicated task. A multiobjective approach has been adopted to optimize the microgrid design. Two methodologies are available for solving such multiobjective problems. In the first approach, the problem is transformed into a single objective one (using aggregation, for instance), whereas, the second technique treats objectives simultaneously and independently as adopted in this paper. The proposed approach offers the Pareto front; a set of solutions in one run opening the door of choosing the most suitable solution from the available options based on the experience, expertise and requirement of the designer. This paper presents a novel approach of using Decomposition Based Multiobjective Evolutionary Algorithm (MOEA/D) to optimally design the PV/Wind/Diesel HMS considering load uncertainty. Loss of Power Supply Probability (LPSP) and Cost of Electricity (COE) are considered as the objective functions of the optimization problem. Furthermore, two separate load cases of 5 and 10 houses are tested to verify the robustness of the approach. The obtained results are beneficial in assisting researchers and practitioners in selecting the optimal configuration of the microgrid.

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.