• Energy Research

The multilevel inverters are becoming increasingly popular for use in the grid integration of wind and photovoltaic (PV) power plants due to their higher voltage handling capability and the better output power quality. There are several types of multilevel inverters that have been proposed in the literature; among them, the active neutral point clamp (ANPC) multilevel inverters have been drawing significant attention especially for solving the problems with other multilevel inverters. However, with the increase in the number of levels, the ANPC requires more electronic switches and flying capacitors, by which the complexity and the cost increase. In this article, an ANPC inverter with a reduced number of switches and flying capacitors is presented for the grid integration of the solar PV systems controlled using the model predictive control technique. This model predictive control technique uses a discrete-time model of the system to predict the future value of the active power and the reactive power for seven identified voltage vectors and selects the vector for the operation, which causes minimum cost function. The proposed power converter topology also effectively utilizes the dc bus voltage more when compared with the traditional ANPC converter. In the proposed inverter, a high-frequency transformer is used to eliminate the voltage balancing problems faced by the traditional ANPC inverters. The proposed magnetic linked power converter provides galvanic isolation, which is one of the most critical issues for traditional transformerless grid-connected PV systems. The proposed topology makes the control strategy simple and makes the power conversion system reliable for the PV power plants. The model predictive control-based power converter topology is simulated in MATLAB/Simulink environment and also validated in the laboratory test platform.

Network reconfiguration is an operation task, and consists in the determination of the switching operations such to reach the minimum loss conditions of the distribution networks. In this paper, a general formulation of the network reconfiguration for loss minimization is given for the optimization of distribution loss reduction and a solution approach is presented. The solution employs a search over different radial configurations, created by considering branch exchange type switches. The solution algorithm for loss minimization has been developed based on the two stage solution methodology. The first stage of this solution algorithm finds a loop which gives the maximum loss reduction in the network. For this purpose a simple-to-use formula, called loop loss reduction formula, has been developed. To find a branch exchange, which results in the maximum loss reduction in the loop, the second stage applies a proposed technique called distance-center technique. Therefore, the solution algorithm of the proposed method can identify the most effective branch exchange operations for loss reduction, with a minimum computational efforts. The solution algorithm of the proposed method has been tested, with very promising results, on a 69-bus radial distribution system. Test results prove that, via proposed network reconfiguration for loss minimization, real power loss is reduced significantly, and that the voltage profile of the network is considerably improved. As compared to the established methods the proposed method eliminates the need to run numerous load flows.

High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system.

This paper proposes an event-triggered sliding mode control (SMC)-based fault ride through (FRT) strategy for doubly-fed-induction-generator (DFIG)-based wind turbines. An event-triggered SMC (ETSMC) approach is designed for a dynamic voltage restorer (DVR) with a high frequency isolated dc-dc converter. The aim is to regulate the stator terminal voltage by injecting appropriate voltage to regulate it near the reference point. Since the control signal in the event-triggered SMC is only updated when certain conditions are violated, the proposed approach results in reduced computational burden and channel bandwidth. Additionally, it reduces the chattering phenomena typically associated with SMC and reduces harmonic distortion. The ETSMC is augmented by a disturbance observer to further improve its robustness against mismatched uncertainties. The DVR topology considered in this paper utilizes high frequency isolation transformer, which dramatically reduces the costs associated with the regular isolation transformer. The proposed FRT strategy is validated with a DFIG-based wind turbine connected to a test microgrid. The obtained results confirmed the effectiveness of the proposed approach in mitigating dynamic instabilities resulting from grid faults while minimizing the usage of the communication channel.

Les bâtiments représentent une quantité importante de consommation d'énergie, ce qui entraîne des problèmes d'émissions mondiales et de changement climatique. Ainsi, la gestion de l'énergie dans un bâtiment est de plus en plus explorée en raison de son potentiel important de réduction des dépenses globales d'électricité pour les consommateurs et d'atténuation des émissions de carbone. En ligne avec cela, un plus grand contrôle et une optimisation de la gestion de l'énergie intégrée aux ressources énergétiques renouvelables sont nécessaires pour améliorer l'efficacité énergétique des bâtiments tout en satisfaisant le confort de l'environnement intérieur. Même si des mesures sont prises pour réduire la consommation d'énergie dans les bâtiments avec plusieurs techniques d'optimisation et de contrôle, certains problèmes restent non résolus. Par conséquent, ce travail fournit un examen complet des méthodes de contrôle conventionnelles et intelligentes en mettant l'accent sur leur classification, leurs caractéristiques, leur configuration, leurs avantages et leurs inconvénients. Cet examen examine de manière critique les différents objectifs et contraintes d'optimisation en ce qui concerne la gestion du confort, la consommation d'énergie et la planification. En outre, la revue décrit les différentes approches méthodologiques des algorithmes d'optimisation utilisés dans la gestion de l'énergie des bâtiments. Les contributions du contrôleur et de l'optimisation dans la gestion de l'énergie des bâtiments avec la relation des objectifs de développement durable (ODD) sont expliquées rigoureusement. Des discussions sur les principaux défis des méthodes existantes sont présentées afin d'identifier les lacunes pour les recherches futures. L'examen fournit des orientations futures efficaces qui seraient bénéfiques pour les chercheurs et les industriels afin de concevoir un contrôleur optimisé efficacement pour la gestion de l'énergie du bâtiment en vue de cibler les ODD. Los edificios representan una cantidad significativa de consumo de energía que conduce a los problemas de las emisiones globales y el cambio climático. Por lo tanto, la gestión de la energía en un edificio se explora cada vez más debido a su importante potencial para reducir los gastos generales de electricidad para los consumidores y mitigar las emisiones de carbono. En línea con ello, se requiere un mayor control y optimización de la gestión energética integrada con los recursos energéticos renovables para mejorar la eficiencia energética del edificio a la vez que se satisface el confort del entorno interior. A pesar de que se están tomando medidas para reducir el consumo de energía en edificios con varias técnicas de optimización y control, algunos problemas siguen sin resolverse. Por lo tanto, este trabajo proporciona una revisión exhaustiva de los métodos de control convencionales e inteligentes con énfasis en su clasificación, características, configuración, beneficios e inconvenientes. Esta revisión investiga críticamente los diferentes objetivos y restricciones de optimización con respecto a la gestión del confort, el consumo de energía y la programación. Además, la revisión describe los diferentes enfoques metodológicos de los algoritmos de optimización utilizados en la gestión energética de los edificios. Se explican de forma rigurosa los aportes del controlador y la optimización en la gestión energética del edificio con la relación de los objetivos de desarrollo sostenible (ODS). Se presentan discusiones sobre los desafíos clave de los métodos existentes para identificar las brechas para futuras investigaciones. La revisión ofrece algunas direcciones futuras efectivas que serían beneficiosas para los investigadores e industriales para diseñar un controlador optimizado de manera eficiente para la gestión de la energía de los edificios hacia la consecución de los ODS. Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs. تمثل المباني كمية كبيرة من استهلاك الطاقة مما يؤدي إلى قضايا الانبعاثات العالمية وتغير المناخ. وبالتالي، يتم استكشاف إدارة الطاقة في المبنى بشكل متزايد بسبب إمكاناته الكبيرة في تقليل نفقات الكهرباء الإجمالية للمستهلكين والتخفيف من انبعاثات الكربون. وتماشياً مع ذلك، يلزم زيادة التحكم في إدارة الطاقة المتكاملة مع موارد الطاقة المتجددة وتحسينها لتحسين كفاءة استخدام الطاقة في المباني مع إرضاء راحة البيئة الداخلية. على الرغم من اتخاذ إجراءات لتقليل استهلاك الطاقة في المباني باستخدام العديد من تقنيات التحسين والتحكم، إلا أن بعض المشكلات لا تزال دون حل. لذلك، يوفر هذا العمل مراجعة شاملة لأساليب التحكم التقليدية والذكية مع التركيز على تصنيفها وميزاتها وتكوينها وفوائدها وعيوبها. تبحث هذه المراجعة بشكل نقدي في أهداف وقيود التحسين المختلفة فيما يتعلق بإدارة الراحة واستهلاك الطاقة والجدولة. علاوة على ذلك، تحدد المراجعة الأساليب المنهجية المختلفة لخوارزميات التحسين المستخدمة في بناء إدارة الطاقة. يتم شرح مساهمات المراقب والتحسين في بناء إدارة الطاقة مع العلاقة بين أهداف التنمية المستدامة (SDGs) بدقة. يتم تقديم مناقشات حول التحديات الرئيسية للطرق الحالية لتحديد الثغرات للبحث في المستقبل. تقدم المراجعة بعض الاتجاهات المستقبلية الفعالة التي ستكون مفيدة للباحثين والصناعيين لتصميم وحدة تحكم محسنة بكفاءة لبناء إدارة الطاقة نحو استهداف أهداف التنمية المستدامة.

Future distribution networks with increasing level of solar PV penetration will be managed using smart grid technologies capable of producing appropriate and timely response during normal and abnormal operational events. Distribution feeder loads vary throughout the day according to the trend of consumption of the customers. Solar PV outputs fluctuate in proportion to irradiance level of sun. Simultaneous occurrence of both of these variations would result in various operating conditions that may lead to unexpected events, and would require a large amount of network data to be processed and analyzed for decision making. It is envisaged that such data will be available in the future grids with the availability of smart technologies and advanced communication in residential dwellings, commercial buildings and industrial complexes. In this paper, an advanced intelligent computational tool is developed to characterize and analyze the large amount of data associated with wide variations in network behavior using SAX (Symbolic Aggregate Approximation) and pattern recognition. The proposed tool is capable of dealing with network asymmetry, load unbalance, single-phase solar PV integration and their impacts on upstream networks and will assist in making right and timely decision to mitigate adverse impacts of solar PV. The proposed tool has been tested with a practical three-phase distribution system in Australia and can provide an extensive assessment with less computational efforts and time.

This paper introduces the hybridization of multiple linear permanent magnet generator (LPMG)-based wave energy conversion systems with a doubly-fed induction generator (DFIG)-based wind energy conversion system. A detailed investigation of the impact of the integration of the wind-wave hybrid system with the distribution network is presented in this study. The control scheme for the multiple LPMGs ensures the optimum extraction of the wave power and the ability to maintain voltage balance at the outputs of the LPMGs. Similarly, the back-to-back converters of the DFIG are controlled for the maximum power point tracking of the wind turbine and the dc-link voltage regulation. The turbine rotor blade pitch angle is controlled to minimize the power fluctuations caused by the integration of LPMGs at the common DC bus. The time-domain simulation results confirm the effectiveness of the proposed system by showing a stable operation when integrated with the distribution test feeder. Finally, the steady-state analysis shows that the voltage profile of the distribution network is also improved, and the step voltage regulator of the distribution feeder can be removed in the presence of the proposed system without violating the voltage limits at any node of the selected distribution network. Further, the response of the distribution network is also evaluated under an electrical fault condition in the presence of the wind-wave hybrid system.

The predispatch price forecast plays a key element in the electricity market. However, such a forecast usually depends on the traditional offline batch-learning technologies, which cannot respond in time to the unexpected changes in the local power system environment. Further, the predispatch local price forecast is often affected by the dynamic price changes from the neighboring regions. This article proposes a novel online learning forecast approach to overcome the above issues to provide a better predispatch price forecast by using the online sequential extreme learning machine (OS-ELM) algorithm. The article proposes a novel data structure in the form of a 2-D orthogonal list and two corresponding OS-ELM modules. One module provides the rolling day-ahead price prediction and prediction intervals using the day-by-day online training update, while the other provides the rolling 30-min prediction using the 2-h-by-2-h online training update. The proposed approach can continuously perceive any unexpected events and any price fluctuations from the neighboring regions in the nonlinear patterns. The proposed approach is validated using simulation studies based on the data from the Australian electricity market, and the simulation results show that the proposed approach can help in improving the forecast accuracy, especially when unexpected changes occur both locally and in the neighboring area.