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<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>

Abstract Secondary concentrators are used in solar concentrating systems to redirect solar beams reflected by the primary concentrators to the focal point or line. These components allow to increase the concentrated solar flux density and hence to lower thermal radiation losses. Solar reflectors for secondary concentrators are permanently exposed to environmental conditions, high radiation fluxes and elevated temperatures that potentially cause stress and degradation throughout the time. Therefore, analyzing solar reflectors of secondary concentrators by simulating these conditions is crucial. No previous research works about the durability of solar reflector materials for secondary concentrators have been reported. The present work is focused on studying the degradation of the reflector materials by simulating accelerated aging, caused by several ambient parameters and the effect of concentrated radiation. Both cooled and uncooled systems for secondary concentrators are included in this study. According to results obtained, aluminum reflectors and thin silvered-glass reflectors glued to an aluminum structure showed minimum reflectance losses and structural degradation under the operation conditions of cooled 3D secondary concentrators (tower systems). Following critical aspects to avoid reflector degradation were identified: to select a suitable adhesive material to glue the thin silvered-glass reflector to the support aluminum structure, to properly protect reflectors edges, to design a suitable cooling system and to avoid the combination of high radiation fluxes with mechanical stress. In addition, laminated silvered-glass reflectors have shown to be suitable for uncooled 2D secondary concentrators (Fresnel collectors). Furthermore, a comparison with naturally aged secondary concentrators using silvered-glass reflectors glued to an aluminum structure revealed that the simulated degradation under accelerated conditions performed in this work did reproduce the most frequent degradation patterns suffered in real operating conditions.

Smart grid (SG) is an innovative technology which aims to make the conventional power grids capable enough to handle the ever increasing demands of power in an efficient manner. SG technology renders the electric distribution system with the capability of accumulating energy from various sources like wind, solar etc. But these sources have intermittency issues which can be handled in an effective manner with the coupling of electric vehicles (EVs) into the SGs. Thus, this paper presents a novel concept in the vehicle-to-grid (V2G) configuration. The primary objective of this paper to provide frequency support to grid by regulating the charging and discharging rates of EVs. These EVs are made to charge and discharge their respective energies at the charging stations (CSs) based on grid's overall requirements. Aggregators (AGs) at the CS level have been specially deployed to regulate EVs activities and maintain grid's stability. It has been verified through extensive simulations that EVs in V2G environment can stabilize the grid in terms of frequency if the coordination amongst the EVs is achieved through aggregators. The results obtained clearly depict that the controlled charging and discharging of EVs' battery can stabilize the grid in terms of frequency.

Acidogenic anaerobic fermentation route was explored for the production of bioethanol and volatile fatty acids (VFA) from the press mud (PM) obtained from sugar mill. Slurry was prepared from PM having 10% of total solids and the same was hydrolyzed under acidic thermal conditions. Both press mud slurry (PMS) and pre-treated press mud slurry (PTPMS) was used as feedstock with mixed microbial consortia (MMC) and enriched mixed microbial consortia (EMMC). Mix of bioethanol and VFA were obtained in all the four cases (PMS-MMC, PMS-EMMC, PTPMS-EMC and PTPMS-EMMC), but, bioethanol and VFA yield of 0.04 g/g and 0.27 g/g, respectively obtained from PTPMS with EMMC was found to be comparatively higher. Control experiments carried out with glucose yielded bioethanol and VFA of 0.042 g/g and 0.28 g/g, respectively demonstrating that the organism was using reducible sugars in the feedstock for the generation of bioethanol by simultaneously producing the VFA from COD.

This paper introduces a model predictive control (MPC) strategy for the purpose of fuel-optimal operation of a range-extender hybrid vehicle. The modern navigation system nowadays can provide abundant road information. Using this information, the proposed controller solves a global optimization problem offline in order to determine a preset trajectory of the state of charge (SoC). The online MPC uses the resulting SoC trajectory as set-points for the terminal state in every moving horizon. Repeating this process, the optimal energy management along the trip to be traveled can thus be calculated. This proposed control strategy is implemented in the commercial vehicle simulation environment IPG CarMaker. From the first simulation results, the proposed strategy shows a promising fuel saving potential with real-time capability.

AbstractIn this paper a complete vehicle system simulation tool chain which applies a Multi Objective Optimization (MOO) methodology for designing the Electric Powertrain (ePT) of Battery Electric Vehicles (BEV) is presented. Optimization scope includes all relevant electric powertrain components from battery, inverter, electric machine to gear box. In addition to cost, vehicle dynamics, energy consumption and range are further optimization targets. For an overall minimal system cost design a multiplicity of interactions between all powertrain components has to be taken into account. High system complexity prevents an expert to consider all relevant correlations without the support of numeric simulation tools. The presented simulation tool chain enables fast identification of the best cost/benefit trade off regarding system cost while considering all defined system performance requirements. The approach enables experts to find unconventional solutions which would have been overlooked applying a classical straight forward approach and, thus, helps to sharpen the expert's knowledge in cause-effect relationships on the system level. Typical use cases are given and illustrated by several practical examples.

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

Dans des scénarios réalistes, la performance dynamique d'un cluster de micro-réseaux est largement affectée par la puissance intermittente des sources d'énergie renouvelables et les changements de charge fréquents. Pour résoudre ce problème, un contrôleur secondaire à double couche basé sur le temps fixe distribué est conçu pour améliorer les performances dynamiques inter-microgrid et intra-microgrid dans un temps de stabilisation fixe. Le contrôleur proposé est indépendant des valeurs de fonctionnement initiales par opposition à la loi de contrôle à temps fini. Chaque agent global dans un micro-réseau fonctionne pour atténuer le décalage de charge entre les autres agents globaux, tandis que chaque agent local dans un micro-réseau fonctionne pour réaliser un partage de courant de charge proportionnel et une régulation de tension moyenne entre eux dans un temps fixe. Cependant, comme l'atténuation de la non-concordance de chargement dans des conditions de charge légère affecte l'efficacité du système en raison de pertes de ligne importantes, le fonctionnement du cluster passe à une approche de minimisation des pertes distribuées, qui fonctionne en utilisant des mesures en ligne des micro-réseaux voisins. Pour caractériser le mode de fonctionnement dans la cyber-couche globale, un seuil de point de chargement critique pour le cluster est ainsi déterminé. La performance du cluster utilisant la stratégie proposée est simulée dans l'environnement MATLAB/SIMULINK pour divers scénarios afin de démontrer sa fiabilité et son efficacité. En escenarios realistas, el rendimiento dinámico de un grupo de microrredes se ve afectado en gran medida por la potencia intermitente de las fuentes de energía renovables y los frecuentes cambios de carga. Para abordar este problema, un controlador secundario de doble capa basado en tiempo fijo distribuido está diseñado para mejorar el rendimiento dinámico entre microrredes y entre microrredes dentro de un tiempo de asentamiento fijo. El controlador propuesto es independiente de los valores operativos iniciales en oposición a la ley de control de tiempo finito. Cada agente global en una microrred opera para mitigar el desajuste de carga entre otros agentes globales, mientras que cada agente local en una microrred opera para lograr un reparto de corriente de carga proporcional y una regulación de voltaje promedio entre ellos en un tiempo fijo. Sin embargo, como la mitigación de la falta de coincidencia de carga durante condiciones de carga ligera afecta la eficiencia del sistema debido a pérdidas de línea significativas, la operación del clúster cambia a un enfoque de minimización de pérdidas distribuidas, que opera utilizando mediciones en línea de las microrredes vecinas. Para caracterizar el modo de operación en la capa cibernética global, se determina así un punto crítico de umbral de carga para el clúster. El rendimiento del clúster que emplea la estrategia propuesta se simula en el entorno MATLAB/SIMULINK para varios escenarios para demostrar su confiabilidad y eficiencia. In realistic scenarios, the dynamic performance of a microgrid cluster is largely affected by the intermittent power of renewable energy sources and frequent load changes. To address this issue, a distributed fixed-time based dual layer secondary controller is designed to improve inter-microgrid and intra-microgrid dynamic performance within a fixed settling time. The proposed controller is independent of initial operating values as opposed to the finite time control law. Each global agent in a microgrid operates to mitigate loading mismatch between other global agents, whereas each local agent in a microgrid operates to achieve proportionate load current sharing and average voltage regulation between them in fixed time. However, as loading mismatch mitigation during light load conditions affects the system efficiency due to significant line losses, the cluster operation switches to a distributed loss minimization approach, which operates using online measurements from the neighboring microgrids. To characterize the mode of operation in the global cyber layer, a critical point of loading threshold for the cluster is thus determined. The performance of the cluster employing the proposed strategy is simulated in MATLAB/SIMULINK environment for various scenarios to demonstrate its reliability and efficiency. في السيناريوهات الواقعية، يتأثر الأداء الديناميكي لمجموعة الشبكات الصغيرة إلى حد كبير بالطاقة المتقطعة لمصادر الطاقة المتجددة والتغيرات المتكررة في الحمل. لمعالجة هذه المشكلة، تم تصميم وحدة تحكم ثانوية ثنائية الطبقة موزعة على أساس الوقت الثابت لتحسين الأداء الديناميكي بين الشبكات الدقيقة وداخلها في غضون وقت استقرار ثابت. وحدة التحكم المقترحة مستقلة عن قيم التشغيل الأولية بدلاً من قانون التحكم في الوقت المحدود. يعمل كل عامل عالمي في شبكة صغرى على التخفيف من عدم تطابق التحميل بين العوامل العالمية الأخرى، في حين يعمل كل عامل محلي في شبكة صغرى على تحقيق مشاركة تيار الحمل المتناسب ومتوسط تنظيم الجهد بينهما في وقت محدد. ومع ذلك، نظرًا لأن تخفيف عدم تطابق التحميل أثناء ظروف الحمل الخفيف يؤثر على كفاءة النظام بسبب الخسائر الكبيرة في الخطوط، تتحول عملية المجموعة إلى نهج تقليل الخسارة الموزعة، والذي يعمل باستخدام القياسات عبر الإنترنت من الشبكات الصغيرة المجاورة. لتوصيف طريقة التشغيل في الطبقة السيبرانية العالمية، يتم تحديد نقطة حرجة لعتبة التحميل للمجموعة. تتم محاكاة أداء المجموعة التي تستخدم الاستراتيجية المقترحة في بيئة ماتلاب/سيمولينك لسيناريوهات مختلفة لإثبات موثوقيتها وكفاءتها.