ALBERT

Description: The European Green Deal and its endeavors will make rapid and far-reaching decisions with major implications for the European cement industry in the short- and longer-term. Accordingly, new measures should be dealt with quickly and effectively to minimize the adverse impact on global warming and global climate change by this sector. The aim of this study is to show and assess the measures to be undertaken to reach carbon neutrality by the Spanish cement industry by 2050. They may be categorized into three broad types based on the main materials: clinker, cement, and concrete. The cement sector must implement breakthrough initiatives, inventions, and technologies regarding the clinker and cement production processes. Furthermore, carbon dioxide uptake by cement-based materials must be considered to achieve the carbon neutrality objective. Accordingly, two methodologies named simplified and advanced, consistent with Guidelines for National Greenhouse Gas Inventories elaborated by the Intergovernmental Panel on Climate Change (IPCC), were selected to model the carbon offsetting by mortars and concretes. Finally, the existing climate change mitigation technologies available in Spain are insufficient to reach the net zero carbon footprint. Therefore, breakthrough technologies such as novel and efficient carbon dioxide capture, utilization, and storage (CCUS) technologies should be implemented by the Spanish cement industry to achieve zero carbon dioxide emissions in 2050.

Description: This article assesses the costs and benefits of incorporating battery energy storage systems (BESS) in transmission network expansion planning (TEP) over multiple time periods. We propose a mixed-integer programming model (MIP) for joint planning of the installation of battery energy storage systems (BESS) and construction of new transmission lines in multiple periods of time. The mathematical formulation of the presented model is based on the strategies of the agents of a transmission network to maximize their benefit, and on the operational restrictions of the power flows in transmission networks. This analysis is performed for the Garver 6 node test system takes into account the power losses in the lines and the restrictions for the energy stored in BESS. The power flows obtained with the MIP model are compared with AC power flows generated with specialized software for flows in power systems. This allows us to demonstrate the potential of models based on DC power flows to achieve approximate results applicable to the behavior and characteristics of real transmission networks. The results show that the BESS increase the net profit in the transmission networks and reduce their power losses.

Description: The analysis of synoptic satellite data of total chlorophyll-a (Chl-a) and the environmental drivers that influence nutrient and light availability for phytoplankton growth allows us to understand the spatio-temporal variability of phytoplankton biomass. In the Panama Bight Tropical region (PB; 1–9°N, 79–84°W), the spatial distribution of Chl-a is mostly related to the seasonal wind patterns and the intensity of localized upwelling centers. However, the association between the Chl-a and different physical variables and nutrient availability is still not fully assessed. In this study, we evaluate the relationship between the Chl-a and multiple physical (wind, Ekman pumping, geostrophic circulation, mixed layer depth, sea level anomalies, river discharges, sea surface temperature, and photosynthetically available radiation) and chemical (nutrients) drivers in order to explain the spatio-temporal Chl-a variability in the PB. We used satellite data of Chl-a and physical variables, and a re-analysis of a biogeochemical product for nutrients (2002–2016). Our results show that at the regional scale, the Chl-a varies seasonally in response to the wind forcing and sea surface temperature. However, in the coastal areas (mainly Gulf of Panama and off central-southern Colombia), the maximum non-seasonal Chl-a values are found in association with the availability of nutrients by river discharges, localized upwelling centers and the geostrophic circulation field. From this study, we infer that the interplay among these physical-chemical drivers is crucial for supporting the phytoplankton growth and the high biodiversity of the PB region.

Description: The transition from traditional energy to cleaner energy sources has raised concerns from companies and investors regarding, among other things, the impact on financial downside risk. This article implements backtesting techniques to estimate and validate the value-at-risk (VaR) and expected shortfall (ES) in order to compare their performance among four renewable energy stocks and four traditional energy stocks from the WilderHill New Energy Global Innovation and the Bloomberg World Energy for the period 2005-2016. The models used to estimate VaR and ES are AR(1)-GARCH(1,1), AR(1)-EGARCH(1,1), and AR(1)-APARCH(1,1), all of them under either normal, skew-normal, Student’s t, skewed-t, Generalized Error or Skew-Generalized Error distributed innovations. Backtesting performance is tested through traditional Kupiec and Christoffersen tests for VaR, but also through recent backtesting ES techniques. The paper extends these tests to the skewed-t, skew-normal and Skew-Generalized Error distributions and applies it for the first time in traditional and renewable energy markets showing that the skewed-t and the Generalized Error distribution are an accurate tool for risk management in those markets. Our findings have important implications for portfolio managers and regulators in terms of capital allocation in renewable and traditional energy stocks, mainly to reduce the impact of possible extreme loss events.

Description: The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.1 data product provides global surface irradiances. Uncertainties in the global and regional monthly and annual mean all-sky net shortwave, longwave, and shortwave plus longwave (total) irradiances are estimated using ground-based observations. Error covariance is derived from surface irradiance sensitivity to surface, atmospheric, cloud and aerosol property perturbations. Uncertainties in global annual mean net shortwave, longwave, and total irradiances at the surface are, respectively, 5.7 Wm−2, 6.7 Wm−2, and 9.7 Wm−2. In addition, the uncertainty in surface downward irradiance monthly anomalies and their trends are estimated based on the difference derived from EBAF surface irradiances and observations. The uncertainty in the decadal trend suggests that when differences of decadal global mean downward shortwave and longwave irradiances are, respectively, greater than 0.45 Wm−2 and 0.52 Wm−2, the difference is larger than 1σ uncertainties. However, surface irradiance observation sites are located predominately over tropical oceans and the northern hemisphere mid-latitude. As a consequence, the effect of a discontinuity introduced by using multiple geostationary satellites in deriving cloud properties is likely to be excluded from these trend and decadal change uncertainty estimates. Nevertheless, the monthly anomaly timeseries of radiative cooling in the atmosphere (multiplied by −1) agrees reasonably well with the anomaly time series of diabatic heating derived from global mean precipitation and sensible heat flux with a correlation coefficient of 0.46.

Description: Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.

Description: The Modular Multilevel Matrix Converter is a relatively new power converter topology appropriate for high-power Alternating Current (AC) to AC purposes. Several publications in the literature have highlighted the converter capabilities such as modularity, control flexibility, the possibility to include redundancy, and power quality. Nevertheless, the topology and control of this converter are relatively complex to design and implement, considering that the converter has a large number of cells and floating capacitors. Therefore multilayer nested control systems are required to maintain the capacitor voltage of each cell regulated within an acceptable range. There are no other review papers where the modelling, control systems and applications of the Modular Multilevel Matrix Converter are discussed. Hence, this paper aims to facilitate further research by presenting the technology related to the Modular Multilevel Matrix Converter, focusing on a comprehensive revision of the modelling and control strategies.

Description: The modular multilevel matrix converter is a relatively new power converter topology suitable for high-power alternating current (AC)-to-AC applications. Several publications in the literature have highlighted the converter capabilities, such as full modularity, fault-redundancy, control flexibility and input/output power quality. However, the topology and control of this converter are relatively complex to realise, considering that the converter has a large number of power-cells and floating capacitors. To the best of the authors’ knowledge, there are no review papers where the applications of the modular multilevel matrix converter are discussed. Hence, this paper aims to provide a comprehensive review of the state-of-the-art of the modular multilevel matrix converter, focusing on implementation issues and applications. Guidelines to dimensioning the key components of this converter are described and compared to other modular multilevel topologies, highlighting the versatility and controllability of the converter in high-power applications. Additionally, the most popular applications for the modular multilevel matrix converter, such as wind turbines, grid connection and motor drives, are discussed based on analyses of simulation and experimental results. Finally, future trends and new opportunities for the use of the modular multilevel matrix converter in high-power AC-to-AC applications are identified.

Description: Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.

Description: In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to 1) quantify spatial correlations and 2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.