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Improving access to sustainable energy services and reducing the carbon footprint and deforestation of developing countries presents a substantial challenge to the development industry. This paper aims to critically assess two prevalent business models emerging to meet this challenge: financial institution–technology provider partnerships and the one-stop-shop, pay-as-you-go (PAYG) model. The authors find that collaborations between financial institutions and energy providers need to address grey areas such as product quality standards, marketing, and post-sales services, and incorporate feedback loops to continually evaluate the performance of the partnership. Pay-as-you-go presents an intriguing business case to leapfrog more traditional energy access programmes. However, more robust results from the field need to be reported before the PAYG movement can claim success. Ultimately, collaborative and integrated actions by all stakeholders are needed to achieve universal energy access.

Current energy market designs and pricing schemes fail to give investors the appropriate market signals. In particular, energy prices are not high enough to attract investors to build new or maintain existing power capacity. In this paper we propose a method to compute second-best Pareto optimal equilibrium prices for any market exhibiting non-convexities and, based on this result, an energy market design able to restore the correct energy price signals for supply investors.

Abstract The implementation of occupancy-controlled and daylighting-dimmed lighting systems has an impact on the energy consumption of residential buildings. The BAC factor method of EN ISO 52120-1 estimates that 8% of the lighting energy can be saved compared to conventional manual control. However, it is assumed that their ability to potentially lower the lighting energy consumption is strongly related to external factors, such as the extent of daylight entrance and the behaviour of the inhabitants. By means of simulations in EnergyPlus, the performances of automated and manual lighting control are compared for an apartment and semi-detached building located in Brussels (Belgium) with variation in the occupant behaviour and orientation. It appears that an automated lighting control including 0-100% dimmer reduces the lighting energy demand for all investigated cases with savings up to 38.4%, whereas a similar control without dimmer does not necessarily reduce the lighting electricity demand. However, the results show a considerable variation, making prediction methods as the BAC factor method highly inaccurate. The actual relative energy performance depends on the automation system, type of building, orientation and occupant behaviour (i.e. number of inhabitants and occupancy rate). Hereby, the number of inhabitants has the most considerable impact on the relative energy performances with differences up to 50%, while the occupancy rate shows a significant correlation, especially for low numbers of inhabitants.

Abstract In many visions and roadmaps, there is a broad agreement that fuel cells – both for stationary and mobile applications – are the key technology to allow the development of a hydrogen infrastructure. Furthermore, this development is generally thought to be based on a gradual, decentralised evolution. Nevertheless, in this paper it is argued that, taking into account the entire hydrogen chain (production, transport, storage, distribution and end-use), this decentralised fuel-cell based philosophy shows some serious flaws. Therefore, a new hydrogen-transition approach was pushed forward: mixing in of hydrogen into the natural-gas bulk. Using Flanders – the Northern part of Belgium – as a case study, the development of a transitory hydrogen infrastructure has been studied, taking into account the entire hydrogen chain and its dynamics, from production to end use. In a next step, this transition is being quantified. An optimisation model has been developed using Matlab and the commercial solvers GAMS and CPLEX. Following a mixed-integer linear-programming approach, this model is able to determine the economically optimal hydrogen-production mix and operational behaviour of each hydrogen-production plant separately. The model then allows gaining valuable insights in the importance of storage and the influence of fuel prices and carbon taxes with regard to the development of an early hydrogen economy.

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.

The Flameless Combustion (FC) regime has been pointed out as a promising combustion technique to lower the emissions of nitrogen oxides (NOx) while maintaining low CO and soot emissions, as well as high efficiencies. However, its accurate modeling remains a challenge. The prediction of pollutant species, especially NOx, is affected by the usually low total values that require higher precision from computational tools, as well as the incorporation of relevant formation pathways within the overall reaction mechanism that are usually neglected. The present work explores a multiple step modeling approach to tackle these issues. Initially, a CFD solution with simplified chemistry is generated [both the Eddy Dissipation Model (EDM) as well as the Flamelet Generated Manifolds (FGM) approach are employed]. Subsequently, its computational cells are clustered to form ideal reactors by user-defined criteria, and the resulting Chemical Reactor Network (CRN) is subsequently solved with a detailed chemical reaction mechanism. The capabilities of the clustering and CRN solving computational tool (AGNES—Automatic Generation of Networks for Emission Simulation) are explored with a test case related to FC. The test case is non-premixed burner based on jet mixing and fueled with CH4 tested for various equivalence ratios. Results show that the prediction of CO emissions was improved significantly with respect to the CFD solution and are in good agreement with the experimental data. As for the NOx emissions, the CRN results were capable of reproducing the non-monotonic behavior with equivalence ratio, which the CFD simulations could not capture. However, the agreement between experimental values and those predicted by CRN for NOx is not fully satisfactory. The clustering criteria employed to generate the CRNs from the CFD solutions were shown to affect the results to a great extent, pointing to future opportunities in improving the multi-step procedure and its application.

Abstract Various assumptions are often made to model turbomachinery bladed assemblies. In particular, the cyclic symmetry of single rotor stages, and dynamically independence of isolated rotor stages are frequently used. The first assumption enables a drastic reduction of the required computational resources by considering only one sector instead of the entire assembly to model and analyze the dynamic behavior of the complete structure. However, small random blade-to-blade structural variations, known as mistuning, exist due to manufacturing tolerances, etc. and significantly affect the dynamic behavior of bladed disks. The second assumption also reduces the needed computational resources and time. However, ignore inter-stage coupling does not always describe accurately the disk or drum flexibility especially at the inter-stage boundaries. In this work, the component mode mistuning method is used for multi-stage assemblies to create a mistuning identification approach. An experimental modal analysis is performed on a two-stage monobloc academic bladed drum. The frequency response function is measured using a base excitation with an electrodynamic shaker and one measurement point per blade of each stage is used. The approach is used to identify mistuning in a multi-stage rotor. Numerical and experimental results are presented. Results show that the proposed approach is effective even for modes which are multi-stage.