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AbstractDecarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO2 emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWhel and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation.

SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

AbstractChemical looping combustion is a highly efficient CO2 separation technology without direct contact between combustion air and fuel. A metal oxide is used as an oxygen carrier in dual fluidized beds to generate clean CO2. The use of biomass is the focus of current research because of the possibility of negative CO2 emissions and the utilization of biogenic carbon. The most commonly proposed OC are natural ores and residues, but complete combustion has not yet been achieved. In this work, the direct utilization of CLC exhaust gas for methane synthesis as an alternative route was investigated, where the gas components CO, CH4 and H2 are not disadvantageous but benefit the reactions in a methanation step. The whole process chain, the coupling of an 80 kWth pilot plant with gas cleaning and a 10 kW fluidized bed methanation unit were for this purpose established. As OC, ilmenite enhanced with limestone was used, combusting bark pellets in autothermal operation at over 1000 °C reaching high combustion efficiencies of up to 91.7%. The fuel reactor exhaust gas was mixed with hydrogen in the methanation reactor at 360 °C and converted with a methane yield of up to 97.3%. The study showed especially high carbon utilization efficiencies of 97% compared to competitor technologies. Based on the experimental results, a scale-up concept study showed the high potential of the combination of the technologies concerning the total efficiency and the adaptability to grid injection. Graphical Abstract

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Adsorption refrigeration, as a renewable cooling method, has received more attention in the last few years. The interest in this technology comes especially from developing and tropical countries, where the demand for cooling increases every year due to economy and population growth. Based on this scenario, this work aims to develop a numerical model of an adsorption chiller driven with solar energy, which can be used to optimize the cooling system operation of the building where the device is situated and compare it with the current cooling methods in use. The numerical study here presented was created using Matlab/Simulink™, it is based on a lumped parameter model that relies on physical properties and represents a cooling system using a pair of silica gel-water in a two-bed chiller. In this study, the authors proposed a simplified version of the system and the numerical model, which aims to reduce the simulation time and provide faster results. Besides the temperatures in the system, which range from 52 °C to 72 °C in the hot cycle and 12 °C to 23 °C in the chilled water cycle, the results also include the variation of water uptake in the two adsorbent beds. In general, the simulated temperature, cooling and heating power and coefficient of performance (COP) are in fair agreement with the literature data, nevertheless, the final results show that improvements still have to be performed.

This paper is devoted to study the electrochemical behavior of Pt catalyst in a polymer electrolyte fuel cell at various operating conditions and at different electric potential difference (also known as voltage) cycling applied in accelerated stress tests. The degradation of platinum is considered with respect to the Pt ion dissolution and the Pt oxide coverage of catalyst described by a one-dimensional model. In the model, degradation rate increases with temperature and decreasing particle diameter of Pt nano-particles. The theoretical study of the underlying diffusion system with the nonlinear reactions is presented by analytical methods and gives explicit solutions through a first integral of the ODE system. Numerical tests are obtained using a second order implicit-explicit scheme. The computer simulation shows that the lifetime of the catalyst depends on the voltage profile and the upper potential level. By this Pt mass loss is more significant at the membrane surface than at the gas diffusion layer.

The automated toolkit for assessing environmental and investment attractiveness of a mining region and the results of its application are discussed in the article. This toolkit includes the optimization mathematical model, the algorithms for the interaction between a regional control center and a producer within the territory, as well as the automated software package for their analysis. The use of the optimization mathematical model makes it possible to take into account the maximum economic potential of a producer, which determines, respectively, a mining region’s environment pollution potential. Accounting for environmental risks will allow the control center or other decision makers to identify not only the optimal pattern of eco-economic interaction in the region, but also reflect changes in the environmental and investment climate as a combination of economic potential and involved risks. The model and the algorithms of interaction between a regional control center and a producer, as well as the results of their numerical analysis given in this paper, allow considering this toolkit as an effective decision support tool aimed at improving environmental and investment attractiveness of a mining region by encouraging a producer to use the best available technologies and conserve the natural environment.