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Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

The classical approach to load modeling is to divide the load by customer class and then identify and model the load components corresponding to each customer class. This work explores an alternative way to break down the load using readily available data. Namely, using historical usage data and historical weather data the load can be first divided into temperature-dependent and temperature-independent portions. A simple thermal model of the structure(s) served by the electrical circuit forms the theoretical and mathematical basis of this separation. During summer peaking conditions, the temperature-dependent load is generally composed of air conditioners, which must be modeled properly in order to analyze delayed voltage recovery or other dynamic phenomenon. Once the temperature-dependent fraction of the load has been characterized, the size of compressor, fan, and pump motors can be inferred by the customer class.

The hydraulic balance of heating network is considered as a pre-condition for the implementation of low temperature district heating (LTDH). Its imbalance result into high energy consumption and heat-losses in the network. In this study, a novel hydraulic model is presented which investigates hydraulic imbalance in the LTDH network, using real weather and hourly monitored operational heating data from an existing boiler based building. Analysis of delta t in space-heating system shows that the delta t is maximum when the outside air temperature is lowest and it decreases with increase in outside air temperature. Furthermore, the hydraulic imbalance is analysed for four different control scenarios with the aim to find an optimum scenario with minimum pumping power, energy consumption and heat-losses in the LTDH network. Results show that the hydraulic imbalance is due to the absence of flow-limiters and balancing valves on the return pipe and thermostatic radiator valves (TRVs) alone are unable to maintain hydraulic balance in the space-heating system of buildings. Moreover, the control scenario with variable flow-rate and fixed supply water temperature from the sub-station is found to be optimum. Compared to the constant flow-rate scenario, the pumping power, energy consumption and heat-losses in the LTDH network are reduced by approximately 2%, 63% and 14%, respectively.

Wind turbine gearboxes are required to operate under adverse operational conditions over a long service lifetime. Unfortunately, gearbox designers are yet to achieve the reliability anticipated by wind turbine manufacturers and operators. The poor understanding of variable loading conditions has resulted in the majority of wind turbine gearboxes being unable to reach their expected service lifetime of 20-25 years. This has led to an increasing need to investigate the fundamental issues associated with the degradation of wind turbine gearbox materials during operation in order to improve existing designs and optimise future ones. This paper investigates the various challenges that need to be addressed in order to achieve a noteworthy increase in the operational service lifetime of large-scale industrial wind turbine gearboxes.

The steady-state phase distribution and the structural parameters have been taken as the input for the nuclear physics calculation in the ADS windowless spallation target. The distribution of the extreme large power density of the heat load is imported back as the source term in the energy equation. Then temperature distribution is obtained based on the flow process and heat transfer. The preliminary results show that the temperature distribution reaches the steady-state and its shape is like the broken wings of the butterfly. This is very important for the further design and optimization of the ADS windowless spallation target. So the two-way coupling simulation of the heat transfer process is successfully performed between the computational fluid dynamics and the nuclear physics simulation.

Previous studies reveal that the primary distribution of the current density is sharply enhanced at the edge of a disk electrode submerged into a semi-infinite space of conductive solution. The current enhancement will cause the double layer capacitance at the periphery of the electrode to be charged much faster compared to the center, and can also lead to severe corrosion at the edge. While several studies focused on the geometric design of the electrode to reduce this enhancement, we explore the feasibility of achieving similar effect by shaping the edges of the current input. The simulation uses finite element analysis software to solve the system of partial differential equations and results show that the edge enhancement could be greatly reduced without significantly changing the input efficacy of current and/or charge.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

Electric power planners in developing countries have the opportunity to anticipate air conditioning-induced demand peaks, rather than merely react to them, as has happened in the United States. Demand-side management activities targeting future air conditioning installations therefore may be warranted, based on economic efficiency considerations, the U.S. experience, and worldwide trends in air conditioning usage. The feasibility of such anticipatory efforts is likely to hinge on local parameters, including domestic energy supplies, foreign exchange and capital constraints, rates of load growth, evidence of impacts on load profiles, and familiarity with demand-side planning concepts.

A mathematical model of the LiAl/LiCl, KCl/FeS cell has been used to study the behavior of the system during relaxation and charging. The effects of state of charge, cell temperature, and current density are presented, and the influence of a period of relaxation on the subsequent charging operation is investigated. In addition, factors that can have a significant impact on the charging characteristics are identified.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.