An investigation of the impact of bounded rationality on the decarbonisation of Kenya's power system

How can we transition to a low-carbon energy supply to limit the effects of climate change? The methodology of quantitative energy models can have an impact on the advice inferred. We compare Kenya’s electricity system transition to 2050 with a 2-model inter-comparison. To explore the uncertainty, we use an agent-based simulation model (MUSE) and an optimisation model (OSeMOSYS)

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide

Geospatial big data analytics has received much attention in recent years for the assessment of energy data. Globally, spatial datasets relevant to the energy field are growing rapidly every year. This research has analysed large gridded datasets of outdoor temperature, end-use energy demand, end-use energy density, population and Gros Domestic Product to end with usable inputs for energy models. These measures have been recognised as a means of informing infrastructure investment decisions with a view to reaching sustainable transition of the residential sector. However, existing assessments are currently limited by a lack of data clarifying the spatio-temporal variations within end-use energy demand. This paper presents a novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating. Here, we have implemented an Unsupervised Machine Learning (UML)-based approach to assess large raster datasets of 165 countries, covering 99.6% of worldwide energy users. The UML approach defines lower and upper limits (thresholds) for each raster by applying GIS-based clustering techniques. This is done by binning global high-resolution maps into re-classified raster data according to the same characteristics defined by the thresholds to estimate intranational zones with a range of attributes. The spatial attributes arise from the spatial intersection of re-classified layers. In the new zones, the energy demand is estimated, so-called energy demand zones (EDZs), capturing complexity and heterogeneity of the residential sector. EDZs are then used in energy systems modelling to assess a sustainable scenario for the long-term transition of space heating technology and it is compared with a reference scenario. This long-term heating transition is spatially resolved in zones with a range of spatial characteristics to enhance the assessment of decarbonisation pathways for technology deployment in the residential sector so that global climate targets can be more realistic met

Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector

Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment)but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available

A bottom-up appraisal of the technically installable capacity of biogas-based solid oxide fuel cells for self power generation in wastewater treatment plants

This paper proposes a bottom-up method to estimate the technical capacity of solid oxide fuel cells to be installed in wastewater treatment plants and valorise the biogas obtained from the sludge through an efficient conversion into electricity and heat. The methodology uses stochastic optimisation on 200 biogas profile scenarios generated from industrial data and envisages a Pareto approach for an a posteriori assessment of the optimal number of generation unit for the most representative plant configuration sizes. The method ensures that the dominant role of biogas fluctuation is included in the market potential and guarantees that the utilization factor of the modules remains higher than 70% to justify the investment costs. Results show that the market potential for solid oxide fuel cells across Europe would lead up to 1,300 MW of installed electric capacity in the niche market of wastewater treatment and could initiate a capital and fixed costs reduction which could make the technology comparable with alternative combined heat and power solutions

Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India’s industry sector

This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India’s iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of ~12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price)

Polarized micro-Raman spectroscopy and ab initio phonon modes calculations of LuPO4

The vibrational dynamics of lutetium orthophosphate (LuPO4) single crystals was carefully investigated by means of polarized micro-Raman spectroscopy and ab initio calculations. Eleven of the twelve independent components of the polarizability tensor, expected on the basis of the group theory for LuPO4, were selected in turn and assigned in symmetry. The only B1g(2) Raman mode was not observed, likewise due to either its very small intensity or its nearness in energy with forbidden Raman modes, which spill and could hide it. Both Raman and infrared vibrational modes are evaluated by densityfunctional theory calculations using effective core pseudo-potential. The agreement between calculated and experimental frequencies is very good. On the basis of our ab initio results, and of reduced-mass ratio considerations, the expected wavenumber of the missing B1g (2) mode falls close to that of Eg(3) mode peaked at about 306 cm1, and therefore we can definitively conclude that the observation of the missing B1g (2) mode is masked by the spill-over of this Eg mode

Vibrational dynamics of rutile-type GeO2 from micro-Raman spectroscopy experiments and first-principles calculations

The vibrational dynamics of germanium dioxide in the rutile structure has been investigated by using polarized micro-Raman scattering spectroscopy coupled with first-principles calculations. Raman spectra were carried out in backscattering geometry at room temperature from micro-crystalline samples either unoriented or oriented by means of a micromanipulator, which enabled successful detection and identification of all the Raman active modes expected on the basis of the group theory. In particular, the Eg mode, incorrectly assigned or not detected in the literature, has been definitively observed by us and unambiguously identified at 525 cm 12 1 under excitation by certain laser lines, thus revealing an unusual resonance phenomenon. First principles calculations within the framework of the density functional theory allow quantifying both wave number and intensity of the Raman vibrational spectra. The excellent agreement between calculated and experimental data corroborates the reliability of our findings

Low-cost emissions cuts in container shipping: thinking inside the box

Container shipping has become an emission-intensive industry; existing regulations, however, continue to display limitations. Technical emissions reduction measures require large, long-term investments, while operational measures may negatively impact transportation costs and supply-chain practices. For container shipping to become more sustainable, innovative, low-cost technological solutions are required. This study discusses such a technological game-changer which utilizes a lighter container type that, contrary to conventional ones, does not require wood in its floor. In this regard, emissions reductions are achieved both due to lower fuel consumption and tree savings. We estimate the global impact of this technology until 2050 using an integrated assessment model and considering different projections about future characteristics of the container fleet. Our results indicate that the adoption of the examined technology can reduce emissions by 4.7–18.8% depending on the main fuel used in container shipping lines, saving also a total of about 44 million trees