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Worldwide, voice-based Air Traffic Control (ATC) communications are gradually being replaced with data link-based equivalents, namely the Controller Pilot Data Link Communications (CPDLC) system. This helps to manage the high levels of congestion on voice-based ATC---under modern traffic levels these analog voice channels are extremely busy, especially at times of peak traffic. CPDLC offers the ability to conduct most ATC actions in the form of digital text-based messages. As with voice-based ATC, CPDLC has no built-in security mechanisms. Furthermore, the links which carry CPDLC do not have security mechanisms either. In this paper, we analyze the susceptibility of CPDLC to attacks by a software-defined radio (SDR)-equipped attacker. Crucially, this is different to attacks on aviation surveillance systems, as it requires the attacker to comply with a larger authentication protocol. We identify attacks on CPDLC, including a man-in-the-middle attack on the protocol. This attack enables a take-over of an aircraft's communication on an attacker-specified frequency, after which arbitrary CPDLC commands can be transmitted to the target without alerting the legitimate controller. We empirically assess the likely effectiveness of this attack through a data collection and analysis exercise. In order to counteract this type of attack, we propose three countermeasures of different complexities, including logical checks and a public key infrastructure approach. We also estimate to what extent these countermeasures can be implemented without altering the underlying protocol.

38th European Photovoltaic Solar Energy Conference and Exhibition; 967-972, Concurrently with the strong decline in installed PV system prices, the margin for operation and maintenance (O&M) operators has decreased, as O&M costs are commonly budgeted as a fixed percentage of installed system costs. Hence, there is a need to improve the efficiency of O&M operations. In the PV4.0 project, we are aiming to improve O&M efficiency across the value chain, by implementing Industry 4.0 concepts. One key part of these concepts is to implement an automatic fault detection system, that is used to quickly detect faults in PV plant operation, minimizing electricity generation losses. Furthermore, this fault detection system is to be used to automatically generate maintenance tickets, so corrective maintenance can be performed as soon as possible. In this paper, we present the development of two neural network (NN) based machine learning models. Model A is a four-layer NN, trained to classify individual datapoints as either normal or faulty. Detected faults are subsequently classified as either a hard (zero power output) or soft (power output higher than zero but lower than nominal) failures. Model B is a model that attempts to predict whether the number of failures in the next days exceed a certain threshold. Our results indicate that Model A has a fault detection rate of 79% in our testing dataset, while the performance of Model B is lower, at a fault detection rate of 49% for 1-day-ahead predictions, and only 30% for 2-day-ahead predictions. Further work will focus on improving the failure detection rate for both models. For Model B, we will additionally focus on improving the input dataset, so that better predictions might become possible. Model A will be implemented in a real-time O&M environment to test its performance in real operating conditions.

38th European Photovoltaic Solar Energy Conference and Exhibition; 979-988, In the framework of the new H2020 project TRUST PV, the Cost Priority Number methodology, a cost-based failure modes & effect analysis method, is tailored for its application onto real-case studies during the operational phase of a PV plant. Thereby, it is possible to calculate the energetic and economic losses for each individual failure by combining actual technical characteristics of the failure tickets, the operational costs of fixing those failures, and the power loss due to the failure occurrence. In this work, different empirical, machine learning and univariate models have been tested to find the optimal model to impute power data for all possible scenarios of missing performance data. The parameter space comprises of the amount and type of missing data, the ratio between training and test set and the predictor availability. Overall, the most desirable setting for highly accurate data imputation is the availability of a neighbouring string/plant or measured climate data as predictor with a high training/test set ratio for small test sets and ratios between 80/20 to 50/50 for bigger test sets (longer than 12 hours). In general, machine learning imputation models and empirical power models perform similar for smaller test set sizes and machine learning models are preferrable for bigger data gaps. Univariate methods should be avoided if possible.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1662-1667, BIPV (building-integrated photovoltaics) technologies may play a key role in the transition towards low-carbon energy communities. A relevant aspect to consider to correctly assessing the techno-economic and environmental performance of BIPV applications is the dynamic variation of the climate boundaries conditions during the entire systems’ lifespan. This study provides an innovative approach to optimize BIPV applications in the early design phase. By using a real case study, that consists of four residential buildings, located in Milan, Italy, including about 142 residential units, simulations have been run with two different weather datasets, one representative of the current weather conditions and one representative of their projection in 2050, to investigate the possible impacts of climate change on some relevant key performance indicators (KPIs), with a particular focus on the minimization of the levelized cost of electricity (LCOE). This approach is meant to foster a more accurate design of BIPV systems in urban contexts. Results show that the LCOE decreases by almost 50% from 2020 to 2050 optimizations, and the self-sufficiency target is guaranteed with a smaller installed PV capacity.

38th European Photovoltaic Solar Energy Conference and Exhibition; 690-695, As the PV sector continues to grow, becoming an increasingly important contributor to the electricity supply mix, proving true sustainability is becoming increasingly important to support the promise of a viable, longlasting, and eco-friendly electricity source. Various data suggest room for improvement when it comes to reliability and sustainability, and therefore, several initiatives from the private sector and from governments – including the EU Commission – have been launched. Following the conclusion of the European Commission’s preparatory study for Eco-Design, Energy Labeling, Green Public Procurement and Ecolabeling for PV modules, inverters and systems, a Joint Mission Group (JMG) of international PV experts evaluated the proposals and provided recommendations on the parameters, structure and benchmarks for the implementation of sustainable product policy instruments for PV in the European Union. The closely interlinked topics of product lifetime, quality and reliability, energy yield and sustainability performance require a holistic approach, to ensure comparability and transparency for electricity prosumers and a level playing field for the value chain actors – the combination of mandatory product requirements, backed by quality, reliability & sustainability standards, information requirements on performance through prosumer labels and incentivization of more sustainable products can become the backbone of a reinstated European PV value chain along all life cycle stages. Included in the recommendations of the JMG is the concept of an “Environmental Impact Index”.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1042-1050, Procurement, installation and operation are different phases of a photovoltaic (PV) plant life cycle. Each of these phases come with different risks. These risks and their possible effects are shown in this paper with particular focus on PV modules, explained along the real construction of a PV plant in northern Italy. The quality is tested during all phases; before procurement to ensure module quality, after transport and installation to detect the impact of wrong handling, and during the operational phase. Electroluminescence, current-voltage curve measurements, infrared thermography and daylight photoluminescence were performed. Electroluminescence and current-voltage curve measurements were carried out during procurement and after transport and installation to evaluate the impact of module mishandling. The damage wrong handling of a PV module can cause is shown. During the first year of operation, two infrared thermography inspections were performed to assess the health status of the PV plant and to detect possible further degradation modes, which have not been noticed before. The development of hot spots over half a year is analyzed and severe cell cracks were found as root causes, detected through daylight photoluminescence imagery. Further, the applicability of daylight photoluminescence and the detectability of degradation modes in images of two varying distances is investigated.

38th European Photovoltaic Solar Energy Conference and Exhibition; 667-669, In the framework of the new H2020 project TRUST PV, a benchmarking methodology is being developed. The objective is analyzing how different contextual parameters – such as micro-climate and environment, different assets composed of different components from different manufactures in different configurations – influence the distribution of performance Key Performance Indicators (KPIs). This is achieved by applying big data analytics to over 6.5 GW of solar monitoring data coming from more than 10.000 PV sites monitored through 3E’s asset management platform SynaptiQ. In this paper, the data requirements, and data architecture for this type of benchmarking are presented as well as several preliminary case studies. However, the work is still ongoing within the TRUST PV project and needs to be further extended and validated. The end goal of this component-benchmarking tool is to support technical decisions of different stakeholders (manufacturers, designers, operators, financial entities, etc.), to improve the assumptions taken in PV yield and degradation modelling.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1601-1607, Green hydrogen will be an essential part of the future 100% sustainable energy and industry system. Up to one third of the required solar and wind electricity would eventually be used for water electrolysis to produce hydrogen, increasing the cumulative electrolyser capacity to about 17 TWel by 2050. Key method applied in this research are a learning curve approach for the key technologies, i.e. solar photovoltaic (PV) and water electrolysers, and levelised cost of hydrogen (LCOH). Sensitivities for the hydrogen demand and various input parameters are considered. Electrolyser capital expenditure (CAPEX) for a large utility-scale system is expected to decrease from current 400 €/kWel to 240 €/kWel by 2030 and to 80 €/kWel by 2050. With the continuing solar PV cost decrease, this will lead to an LCOH decrease in Europe from current 47-81 €/MWhH2,LHV (1.6-2.7 €/kgH2) to 31-54 €/MWhH2,LHV (1.0-1.8 €/kgH2) by 2030 and 16-27 €/MWhH2,LHV (0.5-0.9 €/kgH2) by 2050, depending on the location. The share of PV electricity cost in the LCOH will increase from the current 63% to 74% by 2050.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1222-1226, In this paper we present a decision support system for mitigation of PV plant failures to be used in a platform supporting operation and maintenance (O&M) operators in taking the most cost-effective decision in case of performance loss. Within the Solar Bankability project risk assessment and risk management were studied. Several real maintenance tickets were collected and analyzed for typical failures during the lifetime of PV plants in order to develop a technical risk framework and to determine mitigation measures able to minimize the risk for project investments. Besides preventive and corrective mitigation measures applied before the failure happens or implemented during the operation and maintenance phase, this work introduces also direct mitigation options which can be applied in already running plants. Within PV4.0 the aim is to improve further failure data collection and to develop a fully automated decision support system that calculates the economic impact of failures and suggests a list of possible intervention strategies. The Cost Priority Number (CPN) methodology is in the core center of the PV4.0 tool since it prioritizes O&M interventions according to the economic impact of the technical risks helping so in the decision process.

13 pages, 5 figures, supplementary information https://doi.org/10.1038/s41587-020-0718-6.-- Data availability: All available metagenomic data, bins and annotations are available through the IMG/M portal (https://img.jgi.doe.gov/). Bulk download for the 52,515 MAGs is available at https://genome.jgi.doe.gov/GEMs and https://portal.nersc.gov/GEM. Genome-scale metabolic models for the nonredundant, high-quality GEMs are summarized at https://doi.org/10.25982/53247.64/1670777 and available in KBase (https://narrative.kbase.us/#org/jgimags). IMG/M identifiers of all metagenomes binned, including detailed information for each metagenome, are available in Supplementary Table 1.-- The pipeline used to generate the metagenome bins is available at https://bitbucket.org/berkeleylab/metabat/src/master/, Publisher Correction: A genomic catalog of Earth’s microbiomes; Nature Biotechnology 39: 520 (2021); https://doi.org/10.1038/s41587-020-00769-4, The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes, This work was conducted by the US DOE Joint Genome Institute, a DOE Office of Science User Facility (contract no. DE-AC02–05CH11231), and used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US DOE (contract no. DE-AC02–05CH11231). This work was also supported as part of the Genomic Sciences Program DOE Systems Biology KBase (award nos. DE-AC02-05CH11231, DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-98CH10886).-- With the funding support of the ‘Severo Ochoa Centre of Excelle

Left ventricular assist devices (LVAD) are increasingly being used to treat end-stage heart failure. Despite the increased prevalence of these devices, there is a still a high 1-year mortality rate. The purpose of this integrative review was to systematically evaluate research studies that focused on the process and content of LVAD related end-of-life discussions. A literature search was conducted from earliest available date to November, 2017. A total of 59 citations were produced, 13 articles went under full review, and six studies remained for inclusion in this review. Patients and their caregivers experienced confusion and frustration regarding the end-of-life experiences and care related to disjointed care by their provider teams. Clinician experiences and opinions also suggested a controversy about deactivation of the LVAD at the end-of-life, and a divide regarding practices at the end-of-life. Research focused on system-level factors involved in end-of-life care for patients with an LVAD is needed.

37th European Photovoltaic Solar Energy Conference and Exhibition; 2046-2049, Solar photovoltaics (PV) is already the cheapest form of new electricity generation almost all over the world. PV system prices are still expected to decrease by about 60% from 2020 to 2050. PV system and module price reduction is usually projected by applying historical learning rates (LR) on the main cost components. In this study, a detailed bottom-up analysis showed that the future PV module LR is likely to be close to the long-term historical average of 23.5% whereas it has been about 40% for the past decade. Module and cell efficiency is by far the most important cost component and it will cover about half of the future price reduction. Efficiency improvement will also decrease Balance of System (BoS) cost, Operational Expenditure (OPEX) and Levelised Cost of Electricity (LCOE).

37th European Photovoltaic Solar Energy Conference and Exhibition; 1820-1825, With the diffusion of electric heating and cooling devices, coupling the electric and thermal systems in the residential sector is becoming attractive and could help to increase photovoltaic penetration. The heating and cooling needs of buildings correspond to an important component of the total energy consumption of the residential sector. Thus, it is important to properly design the thermal and electric systems accounting of the interactions from the first phases of the design process. In the design phase, detailed models implemented in dynamic simulation tools can be used for the sizing process of system components, but they hardly can be adopted in optimization algorithms due to the computational time required for each simulation. This is particularly true for multi-objective optimization algorithms, where usually a wide number of simulations is required. In this work, TRNSYS was used to train a machine learning model that is used in a multi-objective optimization with the final goal of improving the design of the thermal system and optimizing the KPIs of a coupled photovoltaic plus battery system.

37th European Photovoltaic Solar Energy Conference and Exhibition; 908-913, Daylight Photoluminescence (dPL) is a relatively novel imaging technique using the principle idea of photoluminescence with the sun as excitation source. The difficult process of filtering the luminescence signal is described by means of implementing a dPL technique at EURAC Research in Bolzano, Italy. The described approach uses modern inverter or maximum power point tracker to alter the working point of modules, which allows imaging of two different images, whose subtraction results in the luminescence signal. Further, the impact of temperature on the luminescence signal is investigated. For this, the signal intensity is measured with dPL for different temperatures under controlled conditions. The impact that temperature has on different factors influencing the luminescence intensity is described. Finally, the impact of temperature on dPL in practical use is calculated from the results, presenting a first attempt to calculate the temperature coefficient of signal intensity in the dPL image.

37th European Photovoltaic Solar Energy Conference and Exhibition; 2050-2056, This study aims at evaluating the production costs of photovoltaic plants that includes the impacts of adding new photovoltaic installations in the existing energy system via the use of the metric “system LCOE”. In the system LCOE, the Levelized Cost of Electricity concept is enlarged to incorporate the so-called integration costs: i.e. the costs of reinforcing the grid infrastructure to accept the increase of variable renewable sources production and the effects on the operating conditions of the existing fossil fuels power plants. These costs are applied to utility-scale photovoltaic plants with and without storage to analyse how their market parity and profitability would change in the future if a more systematic approach is used to evaluate their production costs. The results of themarket parity highlight that its achievement will not be compromised when the integration costs are considered, mainly thanks to the strong decrease of the investment costs of PV and BESS components.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1378-1380, In this work we present the ongoing PV4.0 project, which aims to develop a cost-effective PV plant monitoring and intervention system. By linking PV plant measurement data to a digital ticketing and asset management system, and additionally including the recently developed Cost-Priority-Number method [1], a tool will be developed that applies the concepts of Industry 4.0 to the PV sector. The developed tool aims to improve O&M by prioritizing maintenance activities, and streamlining and further digitalizing the workflow of technicians. Fault detection algorithms are developed based on machine learning approaches like clustering and outlier detection to automatically detect and classify anomalies in PV system output and consequently create maintenance tickets that specify a further course of action. By applying automatic failure detection, the aim is to decrease the time required for failure detection and intervention, in order to minimize revenue losses. By analysing historical and real-time PV plant data and historical failures in operation, the potential for predictive monitoring will be investigated. The digital tools will furthermore produce valuable statistics on PV plant failure modes within each analysed PV plant, and for PV plants in general.

37th European Photovoltaic Solar Energy Conference and Exhibition; 2032-2037, In this work we perform an analysis of the energy communities as forms of collective self-consumption. We have created a program which is combined with a genetic algorithm to perform a multi-objective optimization (MOO) of an EC. The decision variables of the optimization are the PV and the battery investments of each EC member and the adopted business model. The optimization aims at (i) maximizing the cost savings thanks to the EC, (ii) minimizing the percentage of fossil fuel production and (iii) minimizing the value of a Fairness Index which measures the fairness of the benefit distribution. The novelty of this work consists in focusing at the same time on the global benefit for the entire energy community and the profit of each participant of the EC, which do not always progress at the same time. EC benefits are not only economic, therefore this work also focuses on encouraging renewable energy consumption, by minimizing fossil fuel production purchased from the grid. A scenario which is convenient from an economic and environmental perspective is not always applicable if it doesn’t guarantee savings for all the EC members; almost 74% of RES consumption can be achieved while ensuring global satisfaction.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1567-1572, In this work, we present a comparison of Performance Loss Rate calculation methodologies based on originally 8,367 PV system datasets, primarily installed in central Europe. The system data are summarized in the Pearl PV database. Pearl PV is a European COST action aiming at improving the energy performance and reliability of PV systems. The datasets consist of only power measurements. Significant dataset filtering was necessary to remove short time series as well as outliers and resulted in the exclusion of over 90% of the datasets. Three different PLR calculation methodologies have been tested, namely statistical clear-sky fitting, the Year-on-Year approach and seasonal and trend decomposition. Depending on the chosen approach, average system performance losses between -0.75%/a and -1.1%/a have been recorded.