• Energy Research

Global energy consumption is increasing at a dramatic rate and will likely continue to do so. The major source of energy is still fossil fuel, which has resulted in the well-documented problem of global warming due to the emission of greenhouse gases from the burning of such fuel. Climate change and global warming are among the crucial and complex issues encountered by the world today, and they require an immediate solution. Technological innovation is the key to ensuring energy security without causing emissions and providing efficient cost-effective energy solutions. Power electronic technologies offer high reliability and renewable energy conversion efficiency, thus contributing to energy conservation, improving energy efficiency, and helping in the mitigation of harmful global emissions. This review focuses on various aspects of power electronic technologies and their importance in tackling carbon emission and global warming problems. The key topologies of power electronic converters are explained based on types, control difficulties, benefits, and drawbacks. Power electronic controllers utilized for energy conversion are comprehensively reviewed with regard to their structure, algorithm complexity, strengths and weaknesses, and mathematical modeling. The review focuses on power converters and controllers used in different applications and highlight their contributions to energy conservation, increasing the share of renewable energy sources, and mitigating emissions. Moreover, existing research gaps, issues, and challenges are identified. The insights provided by are expected to lead to the enhanced development of advanced power electronic converters and controllers for sustainable energy conversion. Such development can reduce carbon emissions and mitigate global warming.

The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

This paper explores the energy management of a natural gas-based thermal power plant, with a focus on improving its efficiency, sustainability, and economic feasibility. The study uses the Ashuganj Power Station Company Limited (APSCL) in Bangladesh as a case study. To evaluate the efficiency of the APSCL, both energy and exergy perspectives are considered, and sustainability is assessed through exergetic parameters. The plant’s economic feasibility is analyzed based on its fuel costs and operational losses. The results of the study indicate that the energy and exergy efficiencies of the APSCL vary from 32.97% to 33.21% and from 32.63% to 32.87%, respectively, for steam turbines and from 39.77% to 56.98% and from 39.36% to 56.40%, respectively, for combined cycle power plants (CCPP) during 2016–2021. The slightly lower efficiency in exergy calculations accounts for the system loss incurred over time, which is often omitted in efficiency calculations using energy parameters. To measure the sustainability of the APSCL, eight key indicators are used: the depletion number, exergy sustainable index, cumulative exergy loss, relative irreversibility, lack of production, the wasted exergy ratio, environmental effect factor, and improvement potential. The results indicate that APSCL still has opportunities for improvement in terms of sustainability, with 50% of the fuel being depleted in the plant, contributing to a sustainable index of 2.00 and cumulative exergy loss varying between 48.59 and 21.17. Regarding financial costs and losses, APSCL has experienced an increasing trend in the price of electricity generated. However, by implementing intelligent maintenance practices and upgrading equipment in a timely manner, it is possible to minimize costs and increase exergy output, reducing the per-unit fuel requirement for electricity production and the global carbon footprint significantly.

The state of charge (SOC) is a crucial indicator of a Li-ion battery management system (BMS). A BMS with a good SOC assessment can dramatically improve the lifespan of the battery and ensure the safety of the end-user. With deep learning making tremendous strides in many other fields, this study aims to provide an empirical evaluation of commonly used deep learning methods on the task of SOC estimation. We propose the use of two-hidden-layer gated recurrent units (GRU) to estimate the SOC at various ambient temperatures. In this work, we conducted two experiment setups to showcase the capability of the proposed GRU model. In the first setup, the GRU was trained on the DST, BJDST and US06 drive cycle and evaluated the FUDS drive cycle upon convergence. The same procedure was repeated with the second setup except the GRU was trained on the DST, BJDST and FUDS drive cycle and evaluated on the US06 drive cycle. In both experiment setups, the proposed GRU was evaluated on a novel drive cycle that it has not encountered during the training phase. We show that a two-hidden-layer GRU with appropriate hyperparameter combination and training methodology can reliably estimate the SOC of novel drive cycles at various ambient temperatures in comparison with other deep learning methods such as simple recurrent network (SRNN), Long Short-Term Memory (LSTM), 1D Residual Network (Resnet), 1D Visual Geometry Group Network (VGG) and the Multilayer Perceptron (MLP). The proposed GRU achieves 2.3% RMSE on the FUDS drive cycle and 1.2% RMSE on the US06 drive cycle outperforming all other models.

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.

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.

Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications.

Dhaka, the capital city of Bangladesh, is one of the fastest growing cities in Southern Asia, having population of more than 13 million, and is expected to accommodate more than 20 million by 2025. This growth has been accompanied by the growth of urban slums and the subsequent challenges to access basic urban services like water, sanitation, clean energy, and transport for the urban poor. Despite its importance for basic survival, electricity supply is not recognized as a basic urban service, as a result of which, the poverty alleviation and basic infrastructure provision programs have not addressed this issue completely. On the basis of a stakeholder interaction approach, following a set of logically sequenced questions to assess the availability, accessibility, affordability, reliability and continuity of usage of electricity, this study assesses the current status of electricity access in an urban poor area of Dhaka and identifies barriers to electricity access from both demand and supply side. Barriers specific recommendations are also suggested based on the experiences from field visit and the best practices outside Bangladesh are also identified.