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Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. This article therefore provides data that can be used to create a simple zero order energy system model for Mauritania, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost ) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Abstract Post-combustion solvent-based carbon capture is a promising technology that potentially can offset the greenhouse gas emissions from fossil-driven power generation systems. The challenge is that CO2 absorption (similar to other CCS technologies) imposes energetic penalties, and constrains the operational flexibility. In this paper, we build upon our recent contributions in the field (Sharifzadeh et al., 2016; Sharifzadeh and Shah, 2016), and study the dynamic response of such process to the electricity load changes in the power plant. The key research question is to investigate if the steady-state integrated process design and control framework applied in the previous studies, can also ensure controllability under a wide range of disturbances. The present study considers the mutual interactions between the power plant and capture process. Other features of interest include the implications of key design and operational decisions such as reboiler temperature, solvent circulation flow rate, solvent concentration and the rate of power load change or CO2 setpoint tracking for flexible process operation. The results suggest that the capture process exhibits a high degree of flexibility and the integrated design and control framework could be the key enabler for the commercialization of post-combustion solvent-based carbon capture.

This study builds a holistic, transparent life cycle assessment model of a variety of aqueous mineral carbonation processes using a hybrid process model and economic input–output life cycle assessment approach (hybrid EIO-LCA). The model allows for the evaluation of the tradeoffs between different reaction enhancement processes while considering the larger lifecycle impacts on energy use and material consumption. A preliminary systematic investigation of the tradeoffs inherent in mineral carbonation processes is conducted to provide guidance for the optimization of the life-cycle energy efficiency of various proposed mineral carbonation processes. The life-cycle assessment of aqueous mineral carbonation suggests that a variety of alkalinity sources and process configurations are capable of net CO2 reductions. The total CO2 storage potential for the alkalinity sources considered in the U.S. ranges from 1.8% to 23.7% of U.S. CO2 emissions, depending on the assumed availability of natural alkalinity sources and efficiency of the mineral carbonation processes.

The evolution of distribution systems (DS) towards the smart-grid concept posses new challenges, triggered by the integration of distributed generation (DG) and the installation of new devices. These challenges raise the need to reconsider the traditional network operation during the planning stage, enabling the DS to be flexible to operate under different network configuration scenarios. In this paper we propose a DS planning methodology for the connection of support feeders in radial networks, explicitly considering reconfiguration options with open-and closed-loop operation. To this end, we propose an efficiency evaluation, based on Data Envelopment Analysis, to assess candidate feeders in terms of expansion costs, energy losses, and lines' chargeability, under a range of demand scenarios that include GD penetration. Additionally, we have developed a method to identify the main feeder in a radial system, obtaining a simplified version of the DS, better suited for analysis. Simulation results on a real urban DS show the effectiveness of the method to identify the best nodes in a main feeder to connect support feeders, further indicating how to divide the network into operation areas for an improved network performance.

We consider the problem of dispatching a fleet of distributed energy reserve devices to collectively meet a sequence of power requests over time. Under the restriction that reserves cannot be replenished, we aim to maximise the survival time of an energy-constrained islanded electrical system; and we discuss realistic scenarios in which this might be the ultimate goal of the grid operator. We present a policy that achieves this optimality, and generalise this into a set-theoretic result that implies there is no better policy available, regardless of the realised energy requirement scenario. Accepted for publication to the 2018 Power Systems Computation Conference (PSCC)

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

This paper presents the application of isogeometric analysis (IGA) to the spatial discretisation of the multi-group, self-adjoint angular flux (SAAF) form of the neutron transport equation with a discrete ordinate (SN) angular discretisation. The IGA spatial discretisation is based upon non-uniform rational B-spline (NURBS) basis functions for both the test and trial functions. In addition a source iteration compatible maximum principle is used to derive the IGA spatially discretised SAAF equation. It is demonstrated that this maximum principle is mathematically equivalent to the weak form of the SAAF equation. The rate of convergence of the IGA spatial discretisation of the SAAF equation is analysed using a method of manufactured solutions (MMS) verification test case. The results of several nuclear reactor physics verification benchmark test cases are analysed. This analysis demonstrates that for higher-order basis functions, and for the same number of degrees of freedom, the FE based spatial discretisation methods are numerically less accurate than IGA methods. The difference in numerical accuracy between the IGA and FE methods is shown to be because of the higher-order continuity of NURBS basis functions within a NURBS patch as well as the preservation of both the volume and surface area throughout the solution domain within the IGA spatial discretisation. Finally, the numerical results of applying the IGA SAAF method to the OECD/NEA, seven-group, two-dimensional C5G7 quarter core nuclear reactor physics verification benchmark test case are presented. The results, from this verification benchmark test case, are shown to be in good agreement with solutions of the first-order form as well as the second-order even-parity form of the neutron transport equation for the same order of discrete ordinate (SN) angular approximation. Funding was provided by the following grants: EPSRC impact acceleration award grant reference number: EP/R511547/1, Adaptive Hierarchical Radiation Transport Methods to Meet Future Challenges in Reactor Physics (EPSRC Grant No.: EP/ J002011/1), RADIANT: A Parallel, Scalable, High Performance Radiation Transport Modelling and Simulation Framework for Reactor Physics, Nuclear Criticality Safety Assessment and Radiation Shielding Analyses (EPSRC Grant No.: EP/K503733/1)

Thin (approximately 10 nm) oxide buffer layers grown over lead-halide perovskite device stacks are critical for protecting the perovskite against mechanical and environmental damage. However, the limited perovskite stability restricts the processing methods and temperatures (<=110 C) that can be used to deposit the oxide overlayers, with the latter limiting the electronic properties of the oxides achievable. In this work, we demonstrate an alternative to existing methods that can grow pinhole-free TiOx (x = 2.00+/-0.05) films with the requisite thickness in <1 min without vacuum. This technique is atmospheric pressure chemical vapor deposition (AP-CVD). The rapid but soft deposition enables growth temperatures of >=180 ��C to be used to coat the perovskite. This is >=70 ��C higher than achievable by current methods and results in more conductive TiOx films, boosting solar cell efficiencies by >2%. Likewise, when AP-CVD SnOx (x ~ 2) is grown on perovskites, there is also minimal damage to the perovskite beneath. The SnOx layer is pinhole-free and conformal, which reduces shunting in devices, and increases steady-state efficiencies from 16.5% (no SnOx) to 19.4% (60 nm SnOx), with fill factors reaching 84%. This work shows AP-CVD to be a versatile technique for growing oxides on thermally-sensitive materials. R.D.R and R.A.J contributed equally. 23 pages. 6 figures