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Abstract A 500 L biomass fast pyrolysis Auger reactor was designed, constructed and experimented with biomass of Mesquite (Prosopsis juliflora) and rice straw (Oryza sativa). The thermogravimetric analysis of feed stock and the physico chemical properties of the feed and product bio-crude was done as per ASTM standard. An optimization based on Response Surface Methodology was carried out for the operating parameters chosen as: (1) reactor temperature, (2) feedstock-heat carrier ratio and (3) rotational speed of the auger reactor. The optimum bio-crude yield of 42.6 wt.% was observed at 500 °C, feedstock-heat carrier ratio 1:2 and 30 rpm for mesquite sawdust and 34.6 wt.% at 475 °C, 1:2 ratio and 50 rpm for rice straw. Among the two kinds of feedstock tested, the sawdust yielded better product under identical operating conditions. The final bio-crude have properties similar to the results that was reported in the past and has HHV- higher heating value less than petroleum fuel.

A specific role for the HTGR in a national energy strategy is examined. The issue is addressed in two ways. First, the role of the HTGR-GT Binary cycle plant is examined in a national energy strategy based on symbiosis between fast breeder and advanced converter reactors utilizing the thorium U233 fuel cycle. Second, the advantages of the HTGR-GT dry-cooled plant operating in arid regions is examined and compared with a dry-cooled LWR. An event tree analysis of potential benefits is applied.

The variability of large scale wind power generation at high penetration levels has a significant impact on the secure and economic operation of onshore power systems. Grid bottlenecks, high gradients and wasting of wind energy can be avoided using local Energy Storage Systems (ESS) with dedicated power and capacity. The storage type considered in this study is an adiabatic compressed air energy storage (ACAES). This paper presents a comparison of different ESS applications aiming both to increase the profit of the power plant operator in selling the electricity into the electricity market and to support the intermittent electricity production of the wind park. This contribution presents the investigation methodology and discusses computation results for a region in Germany.

In the search for a nontoxic replacement of the commonly employed CdS buffer layer for Cu(In,Ga)(S,Se) $_\mathrm{2}$ based solar cells, chemically deposited Zn(O,S) thin films are a most promising choice. In this paper, we address the usually slow deposition speed of Zn(O,S) in a newly developed ammonia-free chemical bath process, resulting in a deposition of 30 nm in 3 min with good homogeneity on 30 cm × 30 cm sized substrates. Solar cells with buffer layers prepared from this process match the efficiency of CdS reference cells. In a second step, we address the light-soaking post-treatment, still needed for maximum efficiencies. By addition of aluminum to the deposition process, the initial efficiencies can be increased slightly. With the addition of boron, the light-soaking post-treatment is rendered unnecessary, while maintaining high efficiencies above 15%, surpassing reference cells with CdS buffer.

Forecasting and acquiring of short-term demand for frequency control reserve is a significant driver of cost for transmission system operators (TSO). A method is presented of identifying the significance of different factors by analyzing the correlation between historical data of actual reserve activation and renewable energy feed-ins, planned and unplanned power plant outages, vertical load, renewable energy forecasting errors and balancing energy provision. Application of this method shows that balancing energy has a moderate influence on the activation of secondary reserve. It has also been found that for minute reserves, corresponding activation of secondary reserves shows weak influence. Quarter-hourly prior activation of reserves also has a significant influence on the demand for control power. The analysis shows insignificant correlation between the investigated factors and the demand for secondary control reserves and minute reserves.

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

AbstractSolar fuels offer a promising approach to provide sustainable fuels by harnessing sunlight1,2. Following a decade of advancement, Cu2O photocathodes are capable of delivering a performance comparable to that of photoelectrodes with established photovoltaic materials3–5. However, considerable bulk charge carrier recombination that is poorly understood still limits further advances in performance6. Here we demonstrate performance of Cu2O photocathodes beyond the state-of-the-art by exploiting a new conceptual understanding of carrier recombination and transport in single-crystal Cu2O thin films. Using ambient liquid-phase epitaxy, we present a new method to grow single-crystal Cu2O samples with three crystal orientations. Broadband femtosecond transient reflection spectroscopy measurements were used to quantify anisotropic optoelectronic properties, through which the carrier mobility along the [111] direction was found to be an order of magnitude higher than those along other orientations. Driven by these findings, we developed a polycrystalline Cu2O photocathode with an extraordinarily pure (111) orientation and (111) terminating facets using a simple and low-cost method, which delivers 7 mA cm−2 current density (more than 70% improvement compared to that of state-of-the-art electrodeposited devices) at 0.5 V versus a reversible hydrogen electrode under air mass 1.5 G illumination, and stable operation over at least 120 h.

This research examines the economic implications of different designs for a national low carbon fuel standard (NLCFS) for the road transportation sector. A NLCFS based on the average Carbon Intensity (CI) of all fuels sold generates an incentive for fuel suppliers to reduce the measured CI of their fuels. The economic impacts are determined by the availability of low carbon fuels, estimates of which can vary widely. Also important are the compliance path, reference level CI, and the design of the credit system, particularly the opportunities for trading and banking. To quantitatively examine the implications of a NLCFS, we created the Transportation Regulation and Credit Trading (TRACT) Model. With TRACT, we model a NLCFS credit trading system among profit maximizing fuel suppliers for light- and heavy-duty vehicle fuel use for the United States from 2012 to 2030. We find that credit trading across gasoline and diesel fuel markets can lower the average costs of carbon reductions by an insignificant amount to 98% depending on forecasts of biofuel supplies and carbon intensities. Adding banking of credits on top of trading can further lower the average cost of carbon reductions by 5%–9% and greatly reduce year-to-year fluctuations in credit prices.