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Abstract Global hydropower growth continues to accelerate with 25% of total capacity installed in just the last 10 years. This accelerating expansion and the important storage facility hydropower means it is increasingly important to understand the reasons for operational failures. This is a challenge because the major reason for failures involves the complex interaction of hydraulic, mechanical and electric subsystems. Historically, reliability modelling has been split in two directions, focusing on different sub-systems, and has not yet been unified. Here these approaches are unified with a novel expression of unbalanced forces. This model with operational data are validated and the important modes of oscillation in the shaft are identified. Finally, the mechanism of the first-order oscillation mode exciting a second-order mode is presented. This integrated and accurate mathematical model is a major advance in the diagnosis and prediction of failures in hydropower operation.

The unpredictable nature of wind energy makes its integration to the electric grid highly challenging. However, these challenges can be addressed by incorporating storage devices (batteries) in the system. We perform an overall assessment of a single domestic power system with a wind turbine supported by an energy storage device. The aim is to investigate the best operation mode of the storage device such that the occurrence of large power spills can be minimized. For estimating the small probability of large power spills, we use the splitting technique for rare-event simulations. An appropriate Importance Function for splitting is formulated such that it reduces the work-load of the probability estimator as compared to the conventional Crude Monte Carlo probability estimator. Simulation results show that the ramp constraints imposed on the charging/discharging rate of the storage device plays a pivotal role in mitigating large power spills. It is observed that by employing a new charging strategy for the storage device large power spills can be minimized further. There exists a trade-off between reducing the large power spills versus reducing the average power spills.

Dataset (partial) for the journal article Torbaghan, Shahab Shariat, et al. "Designing day-ahead multi-carrier markets for flexibility: Models and clearing algorithms." Applied Energy 285 (2021): 116390. https://doi.org/10.1016/j.apenergy.2020.116390 Historical electricity bids obtained from the website of GME, the Italian power exchange, cannot be redistributed and are not included in the present dataset.

Biomass is considered as a potential source of renewable energy. One of the major problems for biomass gasification is the presence of tar in the product gas. We are investigating catalytic behaviour of olivine as a prospective bed additive for biomass gasifiers for tar removal. In the present paper, the pretreatment of olivine is investigated to improve its activity. Pretreatment method includes heating olivine at 900 °C in the presence of air for different treatment times. The catalytic activity of olivine is investigated via steam-reforming reaction of naphthalene as model biomass tar compound. Improvement in naphthalene conversion of around 30% is observed with 1 h of pretreatment. Also effect of pretreatment time is investigated. With increasing pretreatment time, conversion increases; more than 80% naphthalene conversion is observed with 10 h of pretreatment time for olivine. Both steam and dry reforming reaction of naphthalene forms more than 50% gaseous products over 10 h pretreated olivine. Besides the gaseous products and light tars, polymerization reactions occur producing higher tars in small quantity. Naphthalene conversion under syngas mixture is somewhat lower than that of only in steam and CO2. Apparent activation energy of 187 kJ mol-1 is determined for 10 h pretreated olivine under gasification-gas mixture.

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

The present work focuses on the sampling procedure and quantification of the PAH yield from the fast pyrolysis of waste softwood. In particular, fast pyrolysis experiments were conducted using a CDS Pyroprobe 5200 at temperatures between 500 °C and 1000 °C, at a heating rate of 600 °C/s for a sample size of 30 mg. High performance liquid chromatography (HPLC) was used for the determination of the PAH compounds present in the liquid sample fraction, while a micro – GC was employed for the analysis of the main gaseous products (CO, CO2, CH4 and H2). An alternative tar sampling protocol was proposed, which employed the use of a cold trap (50 °C) and an isopropanol filled impinger bottle for the collection of the condensable products. The experiments were compared to heated foil reactor based pyrolysis tests within the same temperature range and heating rate, except for a slightly lower sample size (10 mg). The Pyroprobe and adapted sampling system proved to be more efficient regarding PAH capture and quantification compared to the heated foil reactor. Naphthalene, acenaphthylene and phenanthrene were the main PAH compounds detected. The PAH yields increased with pyrolysis temperature, up to values corresponding to roughly 0.2 wt% of the overall yield at 1000 °C. From the results it was derived that PAH evolution is mainly a product of secondary decomposition of primary tar, since the char yield stabilized for higher temperatures and the yields of CO, H2 and CH4 increased. Overall mass balance closure values were around 80 wt% on average. Char and gas yields were determined with high reproducibility, however gravimetric liquid analysis lacked due to the inability to gravimetrically measure the yield condensing in the impinger bottle. Future work is aimed on improving on this particular aspect. Overall, the alternative tar sampling system proposed was successful in the quantification of PAH from biomass fast pyrolysis experiments offering increased flexibility, accuracy and practicality of use.

As part of the Copenhagen Accord, individual countries have submitted greenhouse gas reduction proposals for the year 2020. This paper analyses the implications for emission reductions, the carbon price, and abatement costs of these submissions. The submissions of the Annex I (industrialised) countries are estimated to lead to a total reduction target of 12-18% below 1990 levels. The submissions of the seven major emerging economies are estimated to lead to an 11-14% reduction below baseline emissions, depending on international (financial) support. Global abatement costs in 2020 are estimated at about USD 60-100 billion, assuming that at least two-thirds of Annex I emission reduction targets need to be achieved domestically. The largest share of these costs are incurred by Annex I countries, although the costs as share of GDP are similar for Annex I as a group and the seven emerging economies as a group, even when assuming substantial international transfers from Annex I countries to the emerging economies to finance their abatement costs. If the restriction of achieving two-thirds of the emission reduction target domestically is abandoned, it would more than double the international carbon price and at the same time reduce global abatement costs by almost 25%.

Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades: - European offshore wind farms - tidal energy converter deployements in Europe - wave energy converter deployements in Europe