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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.

Abstract Limiting the increase of global temperature requires electrification of the railway transport, which is relatively a distinct mode of transport in terms of economy, business and socialization of a country. However, battery complemented railway transport systems such as fuel cell hybrid railway propulsion systems are constrained by the high cost of the battery, and suboptimal performance of the fuel cell under varying loads. Optimized selection of battery can reduce its costs associated with sizing, life and utilization. Similarly, managing the load distribution between the battery and fuel cell can reduce the load variations concern for fuel cell. In this study, model-based selective characterization of the battery and fuel cell is done to identify the optimal features and constraints. Following characterization, the load allocation to battery and fuel cell is determined with the targets of collaborative utilization of optimal features and mitigation of constraints. Subsequently, considering this load allocation and results of characterization, conditions are proposed for reliable sizing and optimized selection of battery. The significance of the proposed conditions in reliable sizing is explored. Based on these conditions, certain commercially developed batteries are evaluated for optimized selection of battery. The results signify this study as a foundation for research on the reliable sizing and optimized selection of battery in fuel cell hybrid railway propulsion systems.

This article describes an innovative method for measuring lighting levels and other lighting parameters through the use of aerial imagery of towns and cities. Combined with electricity consumption data from smart electricity meters, it was possible to measure the energy efficiency of public lighting installations. The results of this study also confirmed that lighting measurements, installation material, luminaire position, and electricity consumption data can be easily integrated into geographic information systems (GIS). The main advantage of this new methodology is that it provides information about lighting installations in large areas in less time than more conventional procedures. It is thus a more effective way of obtaining the data required to calculate the energy efficiency of lighting levels and electricity consumption. There is even the possibility of generating street lighting maps that provide local administrations with up-to-date information regarding the status of public lighting installations in their city. In this way, modifications or improvements can be made to achieve greater energy savings and, if necessary, to correct the distribution or configuration of public lighting systems to make them more efficient and sustainable. This research studied levels of street lighting and calculated the energy efficiency in various streets of Deifontes (Granada), through the use of aerial imagery.

A new version of the ENERGY-10 computer program simulates the performance of photovoltaic systems, in addition to a wide range of opportunities to improve energy efficiency in buildings. This paper describes two test cases in which the beta release of ENERGY-10 version 1.4 was used to evaluate energy efficiency and building-integrated photovoltaics (BIPV) for two Federal building projects: a 16,000-ft2 (1,487 m2) office and laboratory building at the Smithsonian Astrophysical Laboratory in Hilo, Hawaii, and housing for visiting scientists [three 1400-ft2 (130 m2) and three 1564-ft2 (145 m2) houses] at the Smithsonian Environmental Research Center in Edgewater, Maryland. The paper describes the capabilities of the software, the method in which ENERGY-10 was used to assist in the design, and a synopsis of the results. The results indicate that ENERGY-10 is an effective tool for evaluating BIPV options very early in the building design process. By simulating both the building electrical load and simultaneous PV performance for each hour of the year, the ENERGY-10 program facilitates a highly accurate, integrated analysis.

ABSTRACTThe goal of this study was to analyse the combustion characteristics and emissions of Jatropha curcas biodiesel (JCB) when run in a diesel engine. Jatropha curcas oil was used to produce Jatropha curcas biodiesel (JCB) through a transesterification process. The major fuel properties of JCB, including the acid value, kinematic viscosity, flash point, gross heating value, and iodine value, were determined and compared with that of soybean biodiesel (SBM), sunflower seed biodiesel (SFM), mackerel fish oil biodiesel (MB), and premium diesel (D). JCB had a higher density, acid value, kinematic viscosity, iodine value and flash point, but a lower gross heating value, than D. JCB was then used to analyze combustion characteristics, CO, CO2, NO, NOx, SO2, and particulate matter (PM), under varied engine speeds and varied engine loads. The experimental results show CO2 concentration increased with increasing engine loads for all fuels. Engine trials on D exhibited better combustion efficiency at lower engine loads (0 kW–4 kW) but engine trials on JCB exhibited better combustion efficiency for higher engine loads (5 kW– 8 kW). JCB emitted more NO and NOx on a loaded engine. Engine trials on JCB emitted higher PM concentration when the engine was not loaded, while engine trials on MB produced higher PM concentration when the engine was loaded. The estimated CO2 emissions for JCB, MB, and D are 9221.3, 9617.2, and 10185.0 g (gal fuel)–1, respectively.

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.