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Abstract In many cases, hazardous wastes are subject to thermal treatment at elevated temperatures. Some types of wastes do not have a sufficient calorific value to cover the heat demand of the high temperature process. For thermal treatment of e.g. filter residues, dusts, sulfuric acid, aluminium dross, foundry sand, or waste water, supplementary energy supply is needed. The specific energy demand ranges from 0.5 to 2.5 kWh/kg (2–10 MJ/kg). An important aim of process optimisation is the reduction of (fossil) energy consumption and exhaust gas flow. Concentrated solar energy promises advantages when applied to high energy consuming waste treatment processes with regard to substitute fossil or electric energy consumption, to reduce CO2 emissions, and exhaust gas flow. In parallel to conceptional studies, a solar-heated rotary kiln mini-plant has been designed and constructed for tests in the DLR solar furnace. The tests will give indications of boundary conditions for solar thermal treatment or conversion of selected hazardous materials.

The presented Report forms Deliverable D6.2 resulted from the realization of Workpackage WP.6.3 titled “Overview of market drivers, fiscal measures and subsidies” in frame of the EU-Project “Geothermal communities – demonstrating the cascaded use of geothermal energy for district heating with small scale RES integration and retrofitting measures” (GEOCOM). The following seven countries were covered by WP6.3 works and this Deliverable D6.2: o Macedonia, o Hungary, o Italy, o Poland, o Romania, o Serbia, o Slovakia. The work was done with the contribution of all GEOCOM Project Partner teams and appointed experts, coordinated, interpreted and summarized by the Mineral and Energy Economy Research Institute of PAS team, WP6 leader. FP7

Abstract. Since the first estimate of global CO2 emissions was published in 1894, important progress has been made in the development of estimation methods while the number of available datasets has grown. The existence of parallel efforts should lead to improved accuracy and understanding of emissions estimates, but there remains significant deviation between estimates and relatively poor understanding of the reasons for this. Here I describe the most important global emissions datasets available today and – by way of global, large-emitter, and case examples – quantitatively compare their estimates, exploring the reasons for differences. In many cases differences in emissions come down to differences in system boundaries: which emissions sources are included and which are omitted. With minimal work in harmonising these system boundaries across datasets, the range of estimates of global emissions drops to 5 %, and further work on harmonisation would likely result in an even lower range, without changing the data. Some potential errors were found, and some discrepancies remain unexplained, but it is shown to be inappropriate to conclude that uncertainty in emissions is high simply because estimates exhibit a wide range. While true emissions cannot be known, by comparing different datasets methodically, differences that result from system boundaries and allocation approaches can be highlighted and set aside to enable identification of true differences, and potential errors. This must be an important way forward in improving global datasets of CO2 emissions. Data used to generate figures 4–19 are available at https://doi.org/10.5281/zenodo.3687042 (Andrew, 2020).

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models. Thus, our simulation model can operate without measurements of a PV device. It combines wave and ray optics and a dedicated semiconductor simulation to model the optoelectronic PV device properties resulting in the IV-curve. The system surroundings are described via spectrally resolved ray tracing resulting in a cell resolved irradiance distribution, and via the fluid dynamics-based thermal model, in the individual cell temperatures. A lumped-element model is used to calculate the IV-curves of each solar cell for every hour of the year. These are combined factoring in the interconnection to obtain the PV module IV-curves, which connect to the inverter for calculating the AC energy yield. In our case study, we compare two types of 2 terminal perovskite/silicon tandem modules with STC PV module efficiencies of 27.7% and 28.6% with a reference c-Si module with STC PV module efficiency of 20.9%. In four different climates, we show that tandem PV modules operate at 1–1.9 °C lower yearly irradiance weighted average temperatures compared to c-Si. We find that the effect of current mismatch is significantly overestimated in pure optical studies, as they do not account for fill factor gains. The specific yields in kWh/kWp of the tandem PV systems are between −2.7% and +0.4% compared to the reference c-Si system in all four simulated climates. Thus, we find that the lab performance of the simulated tandem PV system translates from the laboratory to outdoors comparable to c-Si systems. Photovoltaic Materials and Devices Electrical Sustainable Energy Energie and Industrie

This paper explores the ability of European refineries to pass-through costs associated with the introduction of the EU Emissions Trading Scheme (EU ETS). We estimated a sequence of vector error correction models (VECM) within a multi-national setting which covers 14 EU member states. Using weekly data at the country level, this paper finds a significant influence of prices for European Union Allowances (EUAs) on unleaded petrol retail prices during the trial phase of the EU ETS from 2005 to 2007. Petrol prices are found to be elastic with respect to crude oil prices and exchange rates but rather inelastic with respect to carbon costs. The long-run elasticity of petrol prices with respect to the EUA prices typically ranges between 0.01% and 0.09%. Furthermore, by computing the variance decomposition our analysis shows that a significant fraction of petrol price changes in Austria, Germany, France and Spain can be explained by changes in allowances prices (between 10% and 20%).