• Energy Research

Abstract This paper introduces a new CFD based Immersed Body Force (IBF) model, and examines the performance of a new type of tidal turbine, the Momentum Reversal Lift (MRL) turbine, developed by Aquascientific Ltd using the open source computational fluid dynamics (CFD) code OpenFOAM. The IBF model was added as a forcing function into the existing large eddy simulation model to create a momentum change in the fluid flow induced by the MRL turbine. An experimental study was performed on a small scale model to determine the operating efficiency of the turbine and the data was used to validate the IBF model. The power output curves from both the IBF model and the experiments showed good agreement in most of the data except for a few discrepancies at higher torques, due to venturi flow created by the proximity of the computational domain's wall boundary to the turbine. Thus the results presented in this paper show the fidelity of the IBF model and can be used for different tidal turbine numerical modelling.

One key factor in the exploitation of tidal energy is the study of interactions of turbines when working in tidal turbine farms. The Momentum Reversal and Lift (MRL) turbine is a novel cross flow turbine. The three blades rotate around a common central horizontal axis which is parallel to their own axis and perpendicular to the flow. The novelty of the MRL turbine is that it relies on the combination of both lift and momentum reversal (drag) for energy extraction. Scaled MRL turbine models of 0.164 m in diameter were used to characterise the flow in three different tidal array settings. Detailed maps of axial velocity profiles and velocity deficits downstream of the turbine are presented, enabling the visualisation of characteristic flow patterns. The results show that the MRL generates lower velocity deficits and turbulence intensities in the near wake than those associated with horizontal axis turbines. The downstream wake was not completely symmetrical which was related to the geometry of the device but also due to the flow developed in the flume. Amongst the three array configurations studied, a fence of turbines with the lowest separation provided the highest power output.

Waste to energy concept is one of the best methods, which not only consider the environment but also generate energy from municipal solid waste (MSW). Generation of MSWs at Mekelle city, Ethiopia, has grown steadily mainly due to migration of people from rural areas. However, the waste has not been managed and utilised as a useful resource due to lack of awareness and proper technology in the city. The objective of this study was to measure the heat content of solid waste generated in the city. Measurement of heating value was performed on collected samples using bomb calorimeter and Dulong’s formula. The average heating values obtained from the experimental analysis were 17,001 kJ/kg. The energy content obtained from the elemental composition of waste using Dulong’s formula was 16,853 kJ/kg. These results indicate that it could be possible to generate 8.7 MW of power from the solid waste composition represented by the sample; it is a good potential to alleviating the power shortage and interruption problems in the city. The results of this study could be used for design considerations in the selection and establishment of waste to energy technology in Mekelle city.

AbstractThis paper investigates the influence of wake interaction and blockage on the performance of individual turbines in a staggered configuration in a tidal stream farm using the CFD based Immersed Body Force turbine modelling method. The inflow condition to each turbine is unknown in advance making it difficult to apply the correct loading to individual devices. In such cases, it is necessary to establish an appropriate range of operating points by varying the loading or body forces in order to understand the influence of wake interaction and blockage on the performance of the individual devices. The performance of the downstream turbines was heavily affected by the wake interaction from the upstream turbines, though there were accelerated regions within the farm which could be potentially used to increase the overall power extraction from the farm. Laterally closely packed turbines can improve the performance of those turbines due to the blockage effect, but this could also affect the performance of downstream turbines. Thus balancing both the effect of blockage and wake interaction continues to be a huge challenge for optimising the performance of devices in a tidal stream farm.

AbstractThe COVID‐19 pandemic is having an unprecedented impact on social, economic, and political situations of all countries around the world with no sight to its end. Business sectors such as solar distributors, which have been instrumental in supporting the governments' ambitious universal electrification programs, have been negatively affected by the pandemic. The main aim of this paper is therefore to explore and conduct a comparative assessment before and during the COVID‐19 pandemic of the key challenges of solar‐based businesses in Ethiopia focusing on the distributors and installers and to provide policy recommendations. Qualitative and quantitative assessments were employed during this study. The results show that before the pandemic, finding a skilled workforce, gaining a technical knowledge of the technology, competing in the market, and lack of consumer awareness and initial investment were the key challenges. The importation of solar technologies has been halted by the arrival of the COVID‐19 exacerbating existing challenges and threatening the very existence of the businesses. The impact of the pandemic on income levels of end‐users of solar technologies, together with the lack of sufficient supply of technologies to the businesses, most of the businesses are forced to lay off their employees deepening the unemployment rate and, in some cases, forcing businesses to be closed. These circumstances affect economic development and dents the progress made so far in facilitating energy access to remote communities. To protect these vulnerable but very essential small businesses, necessary interventions are recommended.This article is categorized under: Photovoltaics > Economics and Policy

Abstract This paper defines the notion of realising resilient community energy systems (R-CESs) through community capital to withstand unforeseen natural hazards, climate change induced risks and socio-political disruptions. It evaluates the interrelationship between different stages of CES project implementation with the development of the community’s resilience in the form of social, human, economic, physical and natural capital. This study employs empirical research by carrying out case study analysis of CES projects deployed in risk-prone regions of Malawi and Ethiopia. Three CES projects, two in Malawi and one in Ethiopia, have been examined through qualitative analysis of data collected through semi-structured interviews with CES project stakeholders. Case studies analysed the role of different stakeholders in planning, installation, and operation of projects and the evolution of the community’s resilience during phases of project implementation. In-depth critical analysis of cases demonstrates how a community’s evolved resilience in different forms of community capital enables it to cope with unforeseen shocks/disruptions encountered over the period of CES operation. Comparative analysis of cases proposed the novel R-CES framework defining seven key components of community capital to realise a R-CES in practice. The proposed framework provides recommendations and best practices to CES project developers, managers and community representatives to implement CES projects in a way that strengthens community capital to thus realise a resilient community and sustainable infrastructure.

AbstractMarine currents have been identified as a considerable renewable energy source. Therefore, in recent years, research on optimising tidal stream farm layouts in order to maximise power output has emerged. Traditionally, computational fluid dynamics (CFD) models are used to model power output, but their computational cost is prohibitive within an optimisation algorithm. This paper uses surrogate models in place of CFD simulations to optimise the layout of tidal stream farm layouts. Surrogates are functions which are designed to emulate the behaviour of other models with radically reduced computational expense. Two surrogate models are applied and compared: artificial neural network (ANN) and k-nearest neighbours regression (k-NN). We measure their suitability by four criteria: accuracy, efficiency, robustness and performance within an optimisation algorithm. The results reveal that the ANN surrogate is superior in every criteria to the k-NN surrogate. However, the k-NN surrogate is also able to perform adequate optimisation. Finally, we demonstrate that optimisation relying solely on surrogate models is a viable approach, with dramatically reduced computational expense of optimisation.

Correction to: Nature Energyhttps://doi.org/10.1038/s41560-022-01152-0, published online 24 October 2022 In the version of the article initially published, Gebrekidan Gebresilassie Eshetu’s name appeared incorrectly as Eshetu Gebrekidan Gebresilassie and has now been corrected in the HTML and PDF versions of the article. ; Water Resources