• Energy Research
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Abstract With the rapid advancement in wearable electronics, energy harvesting devices based on triboelectric nanogenerators (TENGs) have been intensively investigated for providing sustainable power supply for them. However, the fabrication of wearable TENGs still remains great challenges, such as flexibility, breathability and washability. Here, a route to develop a new kind of woven-structured triboelectric nanogenerator (WS-TENG) with a facile, low-cost, and scalable electrospinning technique is reported. The WS-TENG is fabricated with commercial stainless-steel yarns wrapped by electrospun polyamide 66 nanofiber and poly(vinylidenefluoride-co-trifluoroethylene) nanofiber, respectively. Triggered by diversified friction materials under a working principle of freestanding mode, the open-circuit voltage, short-circuit current and maximum instantaneous power density from the WS-TENG can reach up to 166 V, 8.5 µA and 93 mW/m2, respectively. By virtue of high flexibility, desirable breathability, washability and excellent durability, the fabricated WS-TENG is demonstrated to be a reliable power textile to light up 58 light-emitting diodes (LED) connected serially, charge commercial capacitors and drive portable electronics. A smart glove with stitched WS-TENGs is made to detect finger motion in different circumstances. The work presents a new approach for self-powered textiles with potential applications in biomechanical energy harvesting, wearable electronics and human motion monitoring.

Background: The global palm oil market experienced a remarkable boom since the year 2000. Since palm oil can be used for biodiesel production, the global expansion of oil palm cultivation has been associated with the global biofuel boom. Biofuel policies—especially those adopted in the European Union (EU)—have been blamed for the socio-environmental impacts of oil palm expansion. We explore how the global biofuel boom interacts with national geographies and social-economic and political processes to produce country-specific trajectories of biofuel crops expansion. We analyse the expansion of oil palm cultivation in Colombia between 2000 and 2010 from a political ecology perspective. Methods: The analysis is based on a framework that positions expansion of commodity frontiers within the ‘space-of-flows’ and the ‘space-of-place’. Through this approach, we identify the markets and geographies that define the country-specific trajectories of expansion of oil palm in Colombia, and their connections with general patterns of land control. The empirical analysis is based on primary data collected during fieldwork, and on an extensive review of secondary data about the palm oil sector and the socio-environmental effects of oil palm expansion in the country. Results: The contemporary oil palm expansion in Colombia was not specifically influenced by the international biofuel market. Expansion was characterized by an increasing production of palm oil for biodiesel, to supply a policy-driven national biofuel market controlled by national palm oil producers. The evidence shows that this oil palm expansion proceeded through a variety of land control practices that constitute forms of ‘accumulation by dispossession’ and ‘assimilation’. These are embedded in contextual factors that include the agrarian history of Colombia, the armed conflict, and government policies. Conclusions: Our study shows that the ways in which expansion of biofuel crops unfold in each producing country depend not only on the global biofuel market. They are also shaped by the country-specific geographies and political economies. Therefore, research and policies on the global expansion of energy crops should account for the complex and interrelated factors that mediate the specific ways in which the global demand for biofuels creates biofuel crop booms at country level.

Production of biodiesel from yellow grease (waste cooking oil and waste animal fats) is fast emerging as a promising alternative to address the twin challenges before the biodiesel industry today-fluctuation in prices of vegetable oil and the food versus fuel debate. Yellow grease has a high percentage of free fatty acids (FFA) and proves to be an unsuitable feedstock for biodiesel production from commercially viable alkali-catalyzed production systems due to saponification problems.“Green” methodologies based on heterogeneous solid acid catalyzed reactions have the potential to simultaneously promote esterification and transesterification reactions of yellow grease to produce biodiesel without soap formation and offer easy catalyst separation without generation of toxic streams. This paper presents kinetic studies for the conversion of model yellow grease feeds to biodiesel using a heteropolyacid supported on alumina (HSiW/Al2O3) using a batch autoclave. Three model yellow grease feeds were prepared using canola oil with added FFA such as palmitic, oleic and linoleic acid. A pseudo homogeneous kinetic model for the parallel esterification and transesterification was developed. The rate constants and activation parameters for esterification and transesterification reactions for the model yellow grease feeds were determined. The rate constants for esterification are higher than the transesterification rate constants. The kinetic model was validated using the experimental biodiesel data obtained from processing a commercial yellow grease feed. The kinetic model could be used to design novel processes to convert various low-value waste oils, fats and non-food grade oils to sustainable biodiesel. Keywords: Yellow grease, Canola oil, Free fatty acids, Heteropolyacid, Kinetics for esterification and transesterification

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.

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.

This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

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.

Abstract In this paper, we analyze the rationale for an energy policy mix when the European Emissions Trading Scheme (ETS) is considered from a public choice perspective. That is, we argue that the economic textbook model of the ETS implausibly assumes (1) efficient policy design and (2) climate protection as the single objective of policy intervention. Contrary to these assumptions, we propose that the ETS originates from a political bargaining game within a context of multiple policy objectives. In particular, the emissions cap is negotiated between regulators and emitters with the emitters' abatement costs as crucial bargaining variable. This public choice view yields striking implications for an optimal policy mix comprising RES supporting policies. Whereas the textbook model implies that the ETS alone provides sufficient climate protection, our analysis suggests that support for renewable energies (1) contributes to a more effective ETS-design and (2) may even increase the overall efficiency of climate and energy policy if other externalities and policy objectives besides climate protection are considered. Thus, our analysis also shows that a public choice view not necessarily entails negative evaluations concerning efficiency and effectiveness of a policy mix.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.