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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.

The most severe failures in power grids are often characterized as cascading failures where an initial event triggers consequent failures all along the grid often leading to blackouts. Upon identifying a failure and its cascade potential, timely control actions should be performed by the grid operators to mitigate the effect of the cascade. These actions have to be delivered to one or more control devices, creating a dependency between the power grid and its control network. This paper examines the dependency of the operation of the power grid on the control network. Different from literature studies on failure control, our dependency model captures the impact of networking parameters. We formulate an algorithmic model that describes the impact of this dependency on cascade control. Based on this model, we propose an efficient cascade control algorithm using load shedding with consideration of delays in the communication network for power grids. Finally, we evaluate the impact of the power-communication network dependency with uncontrolled grids, ideal/simple control grids and our proposed control scheme. The results demonstrate that the proposed algorithm can significantly reduce the failure of power lines while sustaining larger power demand for users.

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.