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As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.

Abstract In situ and ex situ spatially-resolved techniques are employed to investigate reactant distribution and its impacts in a polymer electrolyte fuel cell. Temperature distribution data provides further evidence for secondary flows inferred from reactant imaging data, highlighting the contribution of convection in heat as well as reactant distribution. Water build-up from neutron tomography is linked to component degradation, matching the pattern seen in the reactant distribution and thus suggesting that high, non-uniform local current densities shape degradation patterns in fuel cells. The correlations shown between different techniques confirm the use of the versatile reactant imaging technique, which is used to compare commonly used flow field designs. Among serpentine-type designs, the single serpentine is superior in both equivalent current density and reactant distribution, showing large contributions from convective flow. On the other hand, the interdigitated design is shown to produce larger equivalent current densities, while showing a somewhat poorer reactant distribution. Considering the correlations drawn between the techniques, this suggests that the interdigitated design compromises durability in favour of power output. The results highlight how established techniques provide a robust background for the use of a new and flexible imaging technique toward designing advanced flow fields for practical fuel cell applications.

Abstract Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June–July Italian electricity demand for the period 1990–2007. We find a relationship between summer (June–July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach. Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions. This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe.

Abstract Recent results have concluded that the efficacy of compression stroke injections in enhancing natural thermal stratification are dependent on the injector’s included angle. Therefore, there is a need to further understand how different hardware affects the efficacy of thermally stratified compression ignition. In this study, three injector included angles are considered: 150°, 118°, and 60°. Compression stroke injection timing sweeps are performed with these three injectors using two distinct piston geometries: a re-entrant bowl piston geometry found in a production, light-duty diesel engine, and a custom-made open, shallow bowl piston geometry, designed to reduce surface-to-volume ratio. Using an equivalence ratio of 0.5 and a split fraction of 80%, it was found that, with the re-entrant bowl piston geometry, the 150° injector displayed high controllability over the burn duration and was able to elongate the burn duration by a factor of 1.8×. The 118° injector displayed slight controllability over the burn duration, while the 60° injector displayed no controllability. With the open bowl piston geometry, the 150° maintained high controllability over the burn duration, albeit with less efficacy. The 60° injector still had no controllability and now the 118° injector had no controllability. The low surface-to-volume ratio of the shallow bowl piston led to less natural thermal stratification than the re-entrant bowl piston geometry, which impacted the compression stroke injection’s ability to control the burn rate. Therefore, the hardware setup that achieves the highest efficacy is a re-entrant bowl-like piston geometry with a wide spray angle injector.

A 117.8 l three-dimensional pressure vessel is used to study the methane hydrate dissociation with the steam assisted gravity drainage (sagd) method. it is called the pilot-scale hydrate simulator (phs). this study proposes the evaluation and the comparisons of the gas production performance by sagd method from the methane hydrate reservoir with different steam injection rates. it indicates that the experiment could be divided into three main stages: the original gas releasing stage, the original and the hydrate-originating gas releasing stage, and the hydrate-originating gas releasing stage (the sagd process). furthermore, the temperature change consists of the four periods: decreasing dramatically, keeping stable, rising gradually, and keeping steady. with the injected steam flowing downwards and sideways, the steam chamber is expanding. the gas production rate increases with the steam injection rate, while the energy efficiency ratio (eer) and gas-to-water ratios are improved by the decrease of the steam injection rate. (c) 2013 elsevier ltd. all rights reserved.

Abstract The flow past a Bach-type vertical-axis wind or current turbine is simulated using a viscous Discrete-Vortex Method at a Reynolds number of 1500. The main purpose of the study is to evaluate the suitability of Bach-type turbines for use as micro-scale energy harvesters that can be applied to power, for example, sensor nodes of a wireless sensor network. The maximum power coefficient of the turbine operating at a prescribed constant tip-speed ratio is found to be 0.18, which is comparable to the performance of the same turbine at much higher Reynolds numbers, thus indicating only minimal performance penalty for miniaturization. The speed of the turbine has a strong influence on the evolution of vortical flow structures. A new wake-capturing mechanism that boosts the performance of the turbine is discovered from the simulations for a certain range of tip-speed ratios where the vortex shed by the advancing blade helps drive the returning blade. In addition to prescribed rotation, free rotation of a steel Bach-type turbine in water is also investigated. Significant fluctuation in angular velocity over one period of rotation is observed. This speed fluctuation is found to be detrimental to energy extraction, reducing the maximum power coefficient to approximately 0.16. The estimated power generating capacity of a micro-scale turbine indicates that it can significantly extend the life expectancy of a wireless sensor node or even maintain the node in a low-power state indefinitely.

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects.