• Energy Research

Abstract Photovoltaic (PV) plants can be exposed to partial shading, which reduces the energy production and causes multi-peaks to form in the Power-Voltage (P-V) curve. As a result, the row currents of the PV modules will not be constant. Several techniques have been proposed to overcome partial shading, such as the static and dynamic reconfiguration techniques, with both aiming to reduce the difference in the row currents to improve energy production. Minimization of the row current via static techniques requires laborious work and extra wiring. On the other hand, dynamic techniques require an extensive monitoring system to support different tasks. Therefore, to improve the power generated from the PV array, this paper suggests a new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix. In this technique, only the electrical connections of the PV panels are changed while its physical location remains unchanged. To verify the effectiveness of the proposed reconfiguration technique, the system was simulated and tested using MATLAB/SIMULINK software, with four shading patterns. The results were compared with other reconfiguration techniques, namely TCT configuration, competence square (CS), SuDoKu, two-phase array reconfiguration, Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Modified Harris Hawks Optimization (MHHO). The performance of each shading case was also analyzed. Also, a comparative study on performance analysis in real-time application was carried out for each shading pattern. The results prove the superiority of the proposed technique over other techniques for overcoming partial shading.

In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs.

Ce document suggère une méthode pour placer et dimensionner le système de stockage d'énergie par batterie (BESS) de manière optimale afin de minimiser les pertes totales du système dans un système de distribution. Par la suite, le phénomène de la courbe du canard est pris en compte lors de la détermination de l'emplacement et du dimensionnement. Les emplacements et le dimensionnement de BESS ont été optimisés à l'aide d'un algorithme métaheuristique à haute capacité d'exploration et d'exploitation, connu sous le nom d'algorithme d'optimisation des baleines (WOA). Pendant ce temps, les performances de WOA ont été validées à l'aide d'autres algorithmes, à savoir l'optimisation de l'essaim de particules et l'algorithme Firefly. Les résultats ont démontré la capacité de WOA à déterminer l'emplacement et le dimensionnement optimaux de BESS pour tous les cas, avec et sans tenir compte de la question de la courbe du canard pour la réduction des pertes. En outre, le problème de la courbe du canard peut être atténué en optimisant de manière appropriée le système de stockage d'énergie (ESS) pour réduire la rampe abrupte du cou et de la queue de canard et pour soulever le ventre du canard. En conclusion, bien que moins de réduction des pertes ait été obtenue en tant que compromis pour répondre à la contrainte sur la limite de rampe de charge nette, le dimensionnement BESS requis était beaucoup plus petit que le cas sans ces contraintes et opération de charge, ce qui rend cette solution économiquement viable. Este documento sugiere un método para colocar y dimensionar el sistema de almacenamiento de energía de la batería (BESS) de manera óptima para minimizar las pérdidas totales del sistema en un sistema de distribución. Posteriormente, se tiene en cuenta el fenómeno de la curva de pato al determinar la ubicación y el tamaño. Las ubicaciones y el tamaño de BESS se optimizaron utilizando un algoritmo metaheurístico con alta capacidad de exploración y explotación que se conoce como el Algoritmo de Optimización de Ballenas (WOA). Mientras tanto, el rendimiento de WOA se validó utilizando otros algoritmos, es decir, Particle Swarm Optimization y Firefly Algorithm. Los resultados demostraron la capacidad de WOA para determinar la ubicación y el tamaño óptimos de BESS para todos los casos, con y sin considerar el problema de la curva de pato para la reducción de pérdidas. Además de eso, el problema de la curva de pato se puede mitigar optimizando adecuadamente el sistema de almacenamiento de energía (ESS) para reducir la rampa empinada del cuello y la cola de pato y levantar el vientre del pato. En conclusión, aunque se logró una menor reducción de pérdidas como compensación para cumplir con la restricción en el límite de rampa de carga neta, el tamaño BESS requerido fue mucho menor que en el caso sin esas restricciones y operación de carga, lo que hace que esta solución sea económicamente viable. This paper suggests a method to place and size the battery energy storage system (BESS) optimally to minimise total system losses in a distribution system. Subsequently, the duck curve phenomenon is taken into consideration while determining the location and sizing. The locations and sizing of BESS were optimised using a metaheuristic algorithm with high exploration and exploitation ability which is known as the Whale Optimisation Algorithm (WOA). Meanwhile, the performance of WOA was validated using other algorithms, i.e., Particle Swarm Optimisation and Firefly Algorithm. The results demonstrated the capability of WOA to determine the optimal BESS location and sizing for all cases, with and without considering the duck curve issue for loss reduction. Besides that, the duck curve issue can be mitigated by appropriately optimising the energy storage system (ESS) to reduce the steep ramp of the duck neck and ducktail and to lift the duck belly. In conclusion, although less loss reduction was achieved as a tradeoff to fulfil the constraint on net load ramp limit, the required BESS sizing was much smaller than the case without those constraints and charging operation, which makes this solution economically viable. تقترح هذه الورقة طريقة لوضع وحجم نظام تخزين طاقة البطارية (BESS) على النحو الأمثل لتقليل إجمالي خسائر النظام في نظام التوزيع. بعد ذلك، تؤخذ ظاهرة منحنى البط في الاعتبار عند تحديد الموقع والحجم. تم تحسين مواقع وحجم BESS باستخدام خوارزمية metaheuristic ذات قدرة عالية على الاستكشاف والاستغلال والتي تعرف باسم خوارزمية تحسين الحيتان (WOA). وفي الوقت نفسه، تم التحقق من صحة أداء WOA باستخدام خوارزميات أخرى، أي تحسين سرب الجسيمات وخوارزمية Firefly. أظهرت النتائج قدرة WOA على تحديد موقع BESS الأمثل وتحديد الحجم لجميع الحالات، مع وبدون النظر في مشكلة منحنى البط للحد من الخسارة. إلى جانب ذلك، يمكن التخفيف من مشكلة منحنى البط من خلال تحسين نظام تخزين الطاقة (ESS) بشكل مناسب لتقليل المنحدر الحاد لعنق البط وذيل البط ولرفع بطن البط. في الختام، على الرغم من تحقيق انخفاض أقل في الخسارة كمقايضة للوفاء بالقيود المفروضة على حد منحدر الحمولة الصافية، إلا أن حجم BESS المطلوب كان أصغر بكثير من الحالة بدون تلك القيود وعملية الشحن، مما يجعل هذا الحل قابلاً للتطبيق اقتصاديًا.

The integration of large-scale wind farms and large-scale charging stations for electric vehicles (EVs) into electricity grids necessitates energy storage support for both technologies. Matching the variability of the energy generation of wind farms with the demand variability of the EVs could potentially minimize the size and need for expensive energy storage technologies required to stabilize the grid. This paper investigates the feasibility of using the wind as a direct energy source to power EV charging stations. An interval-based approach corresponding to the time slot taken for EV charging is introduced for wind energy conversion and analyzed using different constraints and criteria, including the wind speed averaging time interval, various turbines manufacturers, and standard high-resolution wind speed datasets. A quasi-continuous wind turbine’s output energy is performed using a piecewise recursive approach to measure the EV charging effectiveness. Wind turbine analysis using two years of wind speed data shows that the application of direct wind-to-EV is able to provide sufficient constant power to supply the large-scale charging stations. The results presented in this paper confirm that the potential of direct powering of EV charging stations by wind has merits and that research in this direction is worth pursuing.

This paper proposes a novel Non-Intrusive Load\ud Monitoring (NILM) method which incorporates appliance usage\ud patterns (AUPs) to improve performance of active load identi-\ud fication and forecasting. In the first stage, the AUPs of a given\ud residence were learnt using a spectral decomposition based standard\ud NILM algorithm. Then, learnt AUPs were utilized to bias\ud the priori probabilities of the appliances through a specifically\ud constructed fuzzy system. The AUPs contain likelihood measures\ud for each appliance to be active at the present instant based on\ud the recent activity/inactivity of appliances and the time of day.\ud Hence, the priori probabilities determined through the AUPs\ud increase the active load identification accuracy of the NILM\ud algorithm. The proposed method was successfully tested for\ud two standard databases containing real household measurements\ud in USA and Germany. The proposed method demonstrates an\ud improvement in active load estimation when applied to the\ud aforementioned databases as the proposed method augments the\ud smart meter readings with the behavioral trends obtained from\ud AUPs. Furthermore, a residential power consumption forecasting\ud mechanism, which can predict the total active power demand of\ud an aggregated set of houses, five minutes ahead of real time, was\ud successfully formulated and implemented utilizing the proposed\ud AUP based technique.

With the development of offshore wind generation, the interest in cross-country connections is also increasing, which requires models to study their complex static and dynamic behaviors. This paper presents the mathematical modeling of an offshore wind farm integrated into a cross-country HVDC network forming a multi-terminal high-voltage DC (MTDC) network. The voltage source converter models were added with the control of active power, reactive power, frequency, and DC link voltages at appropriate nodes in the MTDC, resembling a typical cross-country multi-terminal type of HVDC scenario. The mathematical model for the network together with the controllers were simulated in MATLABTM and experimentally verified using a real-time digital simulator hardware setup. The resulting static and dynamic responses from the hardware setup agreed well with those from simulations of the developed models.

The occurrence of voltage violations is a major deterrent for absorbing more rooftop solar power into smart Low-Voltage Distribution Grids (LVDGs). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue, a novel sensitivity matrix was developed to estimate the voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart Photovoltaic (PV) inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 55.1% compared to load flow simulations, enabling near-real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The failure of standalone PSO to converge to a feasible solution for 34% of the scenarios evaluated further validates the necessity of the two-stage optimization using FRS. The performance of the proposed methodology was analysed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near-real time. The deviations of the mean voltages of the proposed methodology from the load flow method were: 6.5×10−3 p.u for reactive power control using Q-injection, 1.02×10−2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case.

Au cours des dernières années, l'énergie éolienne a attiré une attention considérable dans divers pays en raison de la forte demande énergétique et de la pénurie de sources d'énergie électrique traditionnelles. Parce que l'énergie éolienne constitue une source rentable et respectueuse de l'environnement, elle peut contribuer de manière significative à la réduction des émissions de carbone toujours croissantes. C'est l'une des technologies vertes à la croissance la plus rapide au monde, avec une part de production totale de 564 GW à la fin de 2018. En Malaisie, l'énergie éolienne a été un sujet brûlant dans les universités et l'industrie de l'énergie verte. Dans ce document, l'état actuel de la recherche sur l'énergie éolienne en Malaisie est examiné. Différents facteurs contributifs tels que la potentialité et les évaluations, la modélisation de la vitesse et de la direction du vent, la prévision du vent et la cartographie spatiale, et le dimensionnement optimal des parcs éoliens sont largement discutés. Ce document traite des progrès de toutes les études liées à l'énergie éolienne et présente des conclusions et des recommandations pour améliorer la recherche sur l'énergie éolienne en Malaisie. En los últimos años, la energía eólica ha ganado una gran atención en los últimos años en varios países debido a la alta demanda de energía y la escasez de fuentes de energía eléctrica tradicionales. Debido a que la energía eólica constituye una fuente rentable y respetuosa con el medio ambiente, puede contribuir significativamente a la reducción de las emisiones de carbono cada vez mayores. Es una de las tecnologías verdes de más rápido crecimiento en todo el mundo, con una participación total de generación de 564 GW a finales de 2018. En Malasia, la energía eólica ha sido un tema candente tanto en el mundo académico como en la industria de la energía verde. En este documento, se revisa el estado actual de la investigación en energía eólica en Malasia. Se discuten ampliamente diferentes factores contribuyentes, como la potencialidad y las evaluaciones, el modelado de la velocidad y la dirección del viento, la predicción del viento y el mapeo espacial, y el tamaño óptimo de los parques eólicos. Este documento discute el progreso de todos los estudios relacionados con la energía eólica y presenta conclusiones y recomendaciones para mejorar la investigación en energía eólica en Malasia. In recent years, wind energy has gained extensive attention in the recent years in various countries due to the high energy demand of energy and shortage of traditional electric energy sources.Because wind energy constitutes a cost effective and environmentally friendly source, it can significantly contribute toward the reduction of the ever-increasing carbon emissions.It is one of the fastest growing green technologies worldwide, with a total generation share of 564 GW as of the end of 2018.In Malaysia, wind energy has been a hot topic in both academia and green energy industry.In this paper, the current status of wind energy research in Malaysia is reviewed.Different contributing factors such as potentiality and assessments, wind speed and direction modeling, wind prediction and spatial mapping, and optimal sizing of wind farms are extensively discussed.This paper discusses the progress of all studies related to wind energy and presents conclusions and recommendations for improving wind energy research in Malaysia. في السنوات الأخيرة، اكتسبت طاقة الرياح اهتمامًا واسعًا في السنوات الأخيرة في مختلف البلدان بسبب ارتفاع الطلب على الطاقة ونقص مصادر الطاقة الكهربائية التقليدية. نظرًا لأن طاقة الرياح تشكل مصدرًا فعالًا من حيث التكلفة وصديقًا للبيئة، فإنها يمكن أن تساهم بشكل كبير في الحد من انبعاثات الكربون المتزايدة باستمرار. إنها واحدة من أسرع التقنيات الخضراء نموًا في جميع أنحاء العالم، حيث بلغ إجمالي حصة التوليد 564 جيجاوات اعتبارًا من نهاية عام 2018. في ماليزيا، كانت طاقة الرياح موضوعًا ساخنًا في كل من الأوساط الأكاديمية وصناعة الطاقة الخضراء. في هذه الورقة، تمت مراجعة الوضع الحالي لأبحاث طاقة الرياح في ماليزيا. تتم مناقشة عوامل مساهمة مختلفة مثل الإمكانات والتقييمات والتقييمات، ونمذجة سرعة الرياح واتجاهها، والتنبؤ بالرياح ورسم الخرائط المكانية، والتحجيم الأمثل لمزارع الرياح على نطاق واسع. تناقش هذه الورقة تقدم جميع الدراسات المتعلقة بطاقة الرياح وتقدم استنتاجات وتوصيات لتحسين أبحاث طاقة الرياح في ماليزيا.