Engie (Belgium)

Due to the rapid increase in residential PV capacity, it gets increasingly difficult for the distribution grid to absorb power at peak hours due to overvoltage issues, causing concerns to DSOs and prosumers who are temporarily unable to inject power into the grid. In collaboration with Belgian DSOs and with realistic input data representing Belgian feeders, dedicated Monte-Carlo analyses were performed to investigate mitigation measures, among which the PQ control of residential inverters. The principle of this mitigation measure is to apply a dedicated control to the inverters since they can control their active and reactive power output to some extent. By either reducing their active power output or absorbing some reactive power in parallel of the active power production, the voltage level can be reduced and inverter tripping due to overvoltages can be avoided. Different types of controls have been assessed and their relative efficiencies compared. The inclusion of the inverters’ dynamic behaviour (i.e. disconnection and reconnection dynamic) had an important impact on the outcome of the assessment. Ultimately, the droop control on the reactive power provided the best results in terms of increased inverter production and equity among prosumers, while the droop control on the active power showed counterproductive results.

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate modeling of wind turbine wakes is essential for understanding turbine performance, wake interactions, and structural loading in wind energy applications. This work presents both numerical and experimental investigations aimed at evaluating the strengths and limitations of the Actuator Line Method (ALM) for wind turbine simulations in a controlled environment. Wind tunnel experiments were conducted using a purpose-built small-scale turbine TWIST (Turbine for Wind-tunnel Investigation &amp; Scaled Testing) to provide high-resolution measurements of near wake velocity and blade deflection. In parallel, a numerical study was carried out using an ALM model developed with a force distribution designed to avoid averaging along the blade length in order to better capture variations in the near tip and hub regions. The inflow conditions in the simulations were carefully matched to the experimental profiles of mean velocity and turbulence intensity, ensuring that discrepancies would be mostly attributed to the modeling rather than to differences in inflow representation. Comparative analyzes between simulations and experiments were performed to assess the model&rsquo;s ability to capture key near-wake features. Additionally, the influence of blade pitch angle on wake development was explored numerically. The blade deflection was analyzed both numerically and experimentally to evaluate the coupling between aerodynamic loading and structural response. The results show that the ALM, combined with distributing the hub drag over a Gaussian ellipsoid, is able to reproduce the main characteristics of the wake at 1.4 rotor diameter downstream, including the velocity deficit and the sharp transition between the wake and the free stream. Wake results at 4.35 diameter are also presented and are in line with the experiment, except very near the ground. Overall, the combined experimental and numerical approach provides valuable insight into the predictive capability of the ALM and its applicability for wind turbine modeling in controlled conditions.

The global transition to renewable energy is transforming power systems, necessitating advanced transmission solutions to ensure reliability and resilience. High-voltage direct current (HVdc) systems, particularly multiterminal dc (MTdc) networks, are pivotal in integrating diverse renewable energy sources into hybrid ac/dc networks. These systems facilitate efficient power transfer over long distances and enable dynamic energy sharing across regions. However, the increasing penetration of inverter-based resources introduces complex control challenges that must be addressed to maintain grid stability and resilience.

The effect of catalyst formulations and coating procedures on the deposition of thin active layers based on Ni/CeO 2 and Ni/CeO 2 -Al 2 O 3 onto AlSi10Mg Periodic Open Cellular Structures (POCS) with cylindrical shape (10 mm diameter and 15 mm length) was experimentally assessed in this work. An aqueous liquid medium based on water, glycerol, and polyvinyl alcohol was used for powder dispersion, while a combined dip- /spin- coating technique was chosen for washcoat deposition on metallic 3D printed structures characterized by Body-Centered Cubic (BCC) and Kelvin unit cells. The rheological behaviour of the prepared slurry was also studied. Multiple depositions with intermediate flash drying steps at 450 °C were carried out. Washcoat loads of about 15 % (0.1 g cm −3 ) were obtained. The used powders were characterized by BET, XRD and pycnometry, instead, the as built and activated supports were characterized by SEM/EDX and pycnometry. No pore clogging phenomena were observed and irrespective of the geometry used, the layers are deposited homogeneously. Adhesion of the deposited layers, evaluated by means of accelerated stress test in ultrasound bath, pointed out that the thermal/anodization pre-treatment of support as well as the use of Disperal P2® as primer, play a crucial role in achieving high mechanical stability associated with weight loss between 0.86 and 12.7wt %. Finally, preliminary catalytic activity tests were performed to evaluate the impact of POCS morphology on performance. The Kelvin structure exhibited superior efficiency in methane steam reforming (SR) at low temperatures, attributed to its optimized pore network and improved distribution of the active phase.

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people.

Due to the rapid increase in residential PV capacity, it gets more and more difficult for an LV grid to absorb power at peak hours due to overvoltage issues, causing concerns to DSOs and prosumers who are temporarily unable to inject power into the grid. Monte-Carlo simulations were performed with a parametrized LV feeder model and with input parameters representing typical feeder characteristics in Belgium that could lead to overvoltage occurrences, in order to gain insights on the effectiveness of using a zigzag transformer as neutral point compensator to mitigate the issue. The simulations’ outputs highlighted that such mitigation measure can bring significant voltage excursion (|Vph-N- Vnom|) reductions. Reductions typically comprised between 14% and 28% have been observed when the zigzag transformer is located near the end of the LV feeder, depending on the zigzag transformer impedance considered. The corresponding reduction of the risk for overvoltage occurrences was comprised between 41% and 77%. The neutral point compensator is only as effective as the unbalance level in the LV feeder, which is linked to the distribution of 1ph PV inverters and 3ph PV inverters on the feeder. The neutral point compensator can thus be regarded as a promising mitigation measure against PV-related overvoltages but with limited effectiveness depending on the unbalance level of the PV production.

Heritage sites are central to the cultural identity and historical narrative of a community. The Al Qattara Oasis, located in the United Arab Emirates (UAE), illustrates this function well. This study addresses the pressing need to preserve and maintain built heritage by employing modern technological solutions for infrastructure upkeep. Specifically, it focuses on crack detection, a critical aspect of ensuring the structural integrity of heritage buildings. Utilizing data collected from various structures across the UAE, obtained through handheld cameras, the effectiveness of Vision Transformers (ViTs) and Swin Transformers in identifying cracks within Al Qattara is assessed. The research thoroughly evaluates models, with particular attention to different input patch sizes. Through systematic experimentation and analysis over 100 epochs, ViT models with a patch size 16 exhibit significant promise in crack detection. Notably, the ViT-16 model achieves good performance metrics, including training and validation accuracies of 85% and (82%) respectively, and correctly classifies 791 out of 957 cracks and 772 out of 903 non-cracks. In contrast, Swin Transformers show lower validation accuracies (70-74%) and higher misclassification rates. The outcomes underscore the potential of ViT models in enhancing infrastructure maintenance efforts within heritage sites like Qattara Oasis. By combining advanced technology with a profound respect for historical preservation, this study aims to contribute to the sustainable conservation and protection of cultural heritage, ensuring that future generations can continue to appreciate the enduring legacy of sites such as Qattara Oasis.

Abstract Supercritical carbon dioxide (sCO2) cycles are compact, cost-effective and widely adaptable to various heat sources, including the waste heat from gas turbine (GT) exhaust gases. While the addition of a steam cycle enhances the typical 40% efficiency of GTs up to 60%, their substantial investments render them less appealing for smaller GTs. This creates an opportunity for sCO2 cycles, but a comprehensive comparison of their performance with that of steam across a range of applications remains lacking. Moreover, their applicability to various industrial scenarios based on existing installations is missing from a techno-economic standpoint. To address these needs, four promising sCO2 cycles are evaluated and optimized using Aspen, and compared with the simple steam cycle. Their techno-economic performances are then investigated for 20 industrial GTs of different size up to the larger combined cycle gas turbine (CCGT) units incorporating amine-based carbon capture systems. Due to the significant investments required by the carbon capture unit, the implementation of a CC unit is only investigated for the largest CCGT units. The analysis yielded performance maps demonstrating comparable performances for sCO2 and steam cycles, as well as significant techno-economic advantages for sCO2 bottoming cycles for smaller GTs. However, when it comes to larger GTs combined with reheats and expansions steam cycles, sCO2 cannot outperform them in current technological standards. Nevertheless sCO2 cycles offers an attractive alternative, facilitating cogeneration. Among the different approaches designed to integrate the heat requirements of amine-based capture, steam cycles have always proved more suitable because of the thermal stability of amines. In conclusion, the research underscores the cost-effectiveness and adaptability of sCO2 cycles for heat recovery applications, particularly as bottoming cycles for smaller GTs, while larger GTs present a challenge. The work conducted sheds light on the substantial promise of sCO2 cycles, encouraging further exploration and implementation of these systems in the energy sector.

Building thermal dynamics often fall into a challenging scenario where available data is limited, but some physics is known. While Physics-Informed Neural Networks (PINNs) can incorporate physical laws to address data scarcity, they are typically restricted to single-building models, limiting their generalizability. In this work, we propose the use of Physics-Informed Deep Operator Networks (DeepONets) to overcome this limitation by learning general operators that predict the thermal behavior of multiple buildings with diverse characteristics. We train a DeepONet using both data-driven and physics-based loss terms and test it on three buildings with varying thermal properties. After 10,000 training epochs, the model accurately captures the indoor air temperature dynamics across all buildings, demonstrating its capacity to generalize and provide a scalable solution for energy-efficient building control.