Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
Wind and solar power generation will continue to grow in the energy supply of the future, but its inherent variability (intermittency) requires appropriate energy systems for storing and using power. Storage of possibly temporary excess of power as methane from hydrogen gas and carbon dioxide is a promising option. With electrolysis hydrogen gas can be generated from (renewable) power. The combination of such hydrogen with carbon dioxide results in the energy carrier methane that can be handled well and may may serve as carbon feedstock of the future. Biogas from biomass delivers both methane and carbon dioxide. Anaerobic microorganisms can make additional methane from hydrogen and carbon dioxide in a biomethanation process that compares favourably with its chemical counterpart. Biomethanation for renewable power storage and use makes appropriate use of the existing infrastructure and knowledge base for natural gas. Addition of hydrogen to a dedicated biogas reactor after fermentation optimizes the biomethanation conditions and gives maximum flexibility. The low water solubility of hydrogen gas limits the methane production rate. The use of hollow fibers, nano-bubbles or better-tailored methane-forming microorganisms may overcome this bottleneck. Analyses of patent applications on biomethanation suggest a lot of freedom to operate. Assessment of biomethanation for economic feasibility and environmental value is extremely challenging and will require future data and experiences. Currently biomethanation is not yet economically feasible, but this may be different in the energy systems of the near future.
Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
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AANLEIDING In het RAAK-MKB project ‘Gelijkspanning breng(t) je verder’ heeft De Haagse Hogeschool, specifiek de opleiding Elektrotechniek, ervaren dat de opkomst van het onderwerp ‘Gelijkspanning’ (ook wel DC) in het beroepenveld sterk samenhangt met ontwikkelingen in het vakgebied van ‘Vermogenselektronica’ of ‘Power Eletronics’. Het beroepenveld vraagt steeds vaker om steeds meer kennis op dit vakgebied, in het kader van bijvoorbeeld de energietransitie, Smart Grids, Internet-of-Things etc. Om deze kennis op een goed gestructureerde wijze over te dragen aan studenten, moeten er een aantal belemmeringen worden weggewerkt. Een van deze belemmeringen is de beperkte beschikbaarheid van kennis; het vakgebied is relatief nieuw en nog sterk in ontwikkeling. Binnen De Haagse Hogeschool is door de opleiding Elektrotechniek (met kennis van de nog weg te werken belemmeringen) de bewuste keuze gemaakt om zich binnen Nederland te willen profileren met het onderwerp ‘Gelijkspanning’. Vanuit het eerdere RAAK-MKB project ‘Gelijkspanning breng(t) je verder’ werden hiertoe een eerste vak en practicum ontwikkeld: Vermogenselektronica 1. Hierin worden beginselen van DC-DC omvormers behandeld. DC-DC omvormers zorgen voor het transformeren van DC-spanningen, om energie bij hoge spanningen en dus lage verliezen te kunnen transporteren. Vanaf het huidige collegejaar (2015-2016) is ook een tweede vak op dit gebied toegevoegd aan het curriculum: Vermogenselektronica 2: hierin worden DC-AC omvormers op hoofdlijnen behandeld. Deze omvormers zorgen ervoor dat veel gebruikte types motoren aangedreven kunnen worden met gelijkspanning. Deze hoofdlijnen staan in de ogen van het beroepenveld nog (te) ver af van toepassingen waarmee zij werken. Daarbij moet gedacht worden aan bijvoorbeeld elektrische mobiliteit (specifieke types motoren), verlichting (DC-DC), distributietechnieken (DC-DC op hogere vermogens) of slimme netten (integratie van energietechniek, communicatietechnologie en regeltechniek / embedded systems). DOELSTELLING Het doel van het project is het opstellen van een implementatiewijze ter verdere invulling van de onderwerpen ‘Gelijkspanning’ en ‘Vermogenselektronica’ in het curriculum van de opleiding Elektrotechniek voor de teamleider van Elektrotechniek van De Haagse Hogeschool om de gewenste profilering te kunnen realiseren. ACTIVITEITEN Vanuit de curriculum commissie van de opleiding Elektrotechniek wordt opdracht gegeven aan een apart team om het implementatievoorstel voor te bereiden. Hierin werken twee docent/onderzoekers samen met de teamleider en enkele extern specialisten. In vijf opeenvolgende stappen wordt op een top-down manier gewerkt aan 1. Formuleren competenties voor DC 2. Hoofdstromen curriculum inrichten 3. Uitwerken vakinhoudelijke gebieden Elektrotechniek (‘leeg vel papier’) 4. Koppelen opzet aan bezetting en kennis in het team en bij partners 5. Voorbereiden besluitvorming RESULTAAT Op deze wijze wordt een heldere visie ontwikkeld op het benodigde onderwijs om het onderwerp gelijkspanning gestructureerd aan te kunnen bieden. Daarbij gaat het om vakinhoudelijke kennis in vakken, met bijbehorende practica en projecten. Om deze kennis goed aan te bieden wordt nadrukkelijk ook de samenwerking met andere kennisinstellingen (zoals Zuyd Hogeschool en de TU-Delft) gezocht.
The consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.
