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In contrast to fossil energy sources, the supply by renewable energy sources likewind and photovoltaics can not be controlled. Therefore, flexibilities on the demandside of the electric power grid, like electro-chemical energy storage systems, are usedincreasingly to match electric supply and demand at all times. To control those flex-ibilities, we consider two algorithms that both lead to linear programming problems.These are solved autonomously on the demand side, i.e., by household computers.In the classic approach, an energy price signal is sent by the electric utility to thehouseholds, which, in turn, optimize the cost of consumption within their constraints.Instead of an energy price signal, we claim that an appropriate power signal that istracked in L1-norm as close as possible by the household has favorable character-istics. We argue that an interior point of the household’s feasibility region is neveran optimal price-based point but can result in a L1-norm optimal point. Thus, pricesignals can not parametrize the complete feasibility region which may not lead to anoptimal allocation of consumption.We compare the price and power tracking algorithms over a year on the base ofone-day optimizations regarding different information settings and using a large dataset of daily household load profiles. The computational task constitutes an embarrassingly parallel problem. To this end, the performance of the two parallel computation frameworks DEF [1] and Ray [2] are investigated. The Ray framework is used to run the Python applications locally on several cores. With the DEF frameworkwe execute our Python routines parallelly in a cloud. All in all, the results providean understanding of when which computation framework and autonomous algorithmwill outperform the other.
Application of various tools to design, simulate and evaluate optical demultiplexers based on AWG
(2015)
Arrayed Waveguide Gratings
(2016)
Arrayed Waveguide Grating (AWG) is a passive optical component, which have found applications in a wide range of photonic applications including telecommunications and medicine. Silica-on-Silicon (SoS) based AWGs use a low refractive-index contrast between the core (waveguide) and the cladding which leads to some significant advantages such as low propagation losses and low fiber coupling losses between the AWG waveguides and the fibres. Therefore, they are an attractive DWDM solution offering higher channel count technology and good performance characteristics compared to other methods. However, the very low refractive-index contrast means the bending radius of the waveguides needs to be very large (on the order of several millimeters) and may not fall below a particular critical value to suppress bending losses. As a result, silica-based waveguide devices usually have a very large size that limits the integration density of SiO2-based photonic integrated devices. High-index contrast AWGs (such as silicon, silicon nitride or polymer-based waveguide devices) feature much smaller waveguide size compared to low index contrast AWGs. Such compact devices can easily be implemented on a chip and have already found applications in emerging applications such as optical sensors, devices for DNA diagnostics and optical spectrometers for infrared spectroscopy.In this work, we present the design, simulation, technological verification and applications of both, the low-index contrast and high-index contrast AWGs. For telecommunication applications AWG-MUX/Demux with up to 128-channels will be presented. For medical applications the AWG-spectrometer with up to 512-channels will be presented.This work was carried out in the framework of the projects: ADOPT No. SK-AT-20-0012, NOVASiN No. SK-AT-20-0017 and AUTOPIC No. APVV-17-0662 from Slovak research and development agency of Ministry of Education, Science, Research and Sport of the Slovak Republic and No. SK 07/2021 and SK 08/2021 from Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH); and project PASTEL, no. 2020-10-15-001, funded by SAIA.
Assessing antecedents of entrepreneurial activities of academics at south african universities
(2016)
The photonic integrated circuits are required in the next generations of coherent terabit optical communications. The software tools for automated adjustment and coupling of optical fiber arrays to photonic integrated circuits has been developed. The obtained results are needed in final production phase in the technology process of photonic integrated circuits packaging.
The usage of data gathered for Industry 4.0 and smart factory scenarios continues to be a problem for companies of all sizes. This is often the case because they aim to start with complicated and time-intensive Machine Learning scenarios. This work evaluates the Process Capability Analysis (PCA) as a pragmatic, easy and quick way of leveraging the gathered machine data from the production process. The area of application considered is injection molding. After describing all the required domain knowledge, the paper presents an approach for a continuous analysis of all parts produced. Applying PCA results in multiple key performance indicators that allow for fast and comprehensible process monitoring. The corresponding visualizations provide the quality department with a tool to efficiently choose where and when quality checks need to be performed. The presented case study indicates the benefit of analyzing whole process data instead of considering only selected production samples. The use of machine data enables additional insights to be drawn about process stability and the associated product quality.
Load shifting of resistive domestic hot water heaters has been done in Europe since the 1930s, primarily to ease the power supply during peak times. However, the pursued and already commenced energy transition in Europe changes the requirements for the underlying logic. In this more general context, demand side management is considered a viable approach to utilize the flexibility of thermal and electrochemical storage systems for buffering energy generated from renewables. In this work, an autonomous approach for demand side management of energy storage systems is developed, which is based on unidirectional communication of an incentive. This concept is then applied to the specific problem of resistive domestic hot water heaters.
The basic algorithms for an optimized operation are developed and evaluated based on simulation studies. The optimization problem considered, maps the search for the optimal heating schedule, while ensuring the temperature limits defined: Firstly, a maximum, which is defined by the hysteresis set point temperature; Secondly, during hot water draw offs, the outlet temperature should not fall below a set minimum. To establish this, the time series of hot water usage has to be predicted.
Depending on the complexity of the hot water heater model used, the formulation of the problem ranges from a linear to non-linear optimization with discontinuous constraints. The simulation studies presented, comprise a formulation as binary linear optimization problem, as well as a solution based on a heuristic direct method to solve the non-linear version. In contrast to the first linear approach, the latter takes stratification inside the tank into account. One-year simulations based on realistic hot water draw profiles are used to investigate the potentials with respect to load shift and energy efficiency improvements. Additional to assuming perfect prediction of user behavior, this work also considers the k-nearest neighbors algorithm to predict the time series. If compared to usual night-tariff switched operation, assuming perfect prediction shows 30 % savings on the electricity market when stratification is taken into account. The user prediction proposed leads to 16 % cost savings, while 6 % of the electric energy is conserved.
Based on the linear approach, a prototype is developed and used in a field test. A micro computer processes the sensor information for local data acquisition, receives electricity spot market prices up to 34 hours in advance, solves the optimization problem for this time horizon, and switches the power supply of the resistive heating element accordingly. Beside the temperature of the environment, the inlet and outlet temperatures, the temperature inside the tank is measured at five points, as well as the water volume flow rate and the electric power recorded. Two test runs of 18 days each, compare the night-tariff switched operation to the price-based optimization in a real-world environment. Results show a significant increase of 6 % in thermal efficiency during the operation based on the algorithm developed, which can be contributed to the optimization accounting for the usage expected.
To facilitate the technical and economic feasibility for retrofit-able implementations of the method proposed for autonomous demand side management, the sensors used must be kept to a minimum. A sufficiently accurate state estimation of the storage has to be achieved, to facilitate a useful model predictive control. Therefore, the last part of this work focuses on the aspect of automated system identification and state estimation of resistive domestic hot water heaters. To that end, real hot water usage profiles and schedules gathered in a field test are used in a lab setup, to collect data on the temperature distribution inside the tank during realistic operating conditions. Four different thermal models, common in literature, are considered for state estimation and system identification. Based on the data collected in the lab, they are evaluated with respect to robustness, computational costs, and estimation accuracy. Based on the observations made in the experiments, an extension of the one-node model by a single additional parameter is proposed. By this adaption, a linear temperature distribution in the lower part of the tank can be modeled during heating. The resulting model exhibits improved robustness and lower computational costs, when compared to the original model. At the same time, the average temperature in the storage tank is estimated nearly as accurate (6 % mean average percentage error) as in the case of the about 50 times more computationally expensive multi-layer model (4 % mean average percentage error).
Demand-side management approaches that exploit the temporal flexibility of electric vehicles have attracted much attention in recent years due to the increasing market penetration. These demand-side management measures contribute to alleviating the burden on the power system, especially in distribution grids where bottlenecks are more prevalent. Electric vehicles can be defined as an attractive asset for distribution system operators, which have the potential to provide grid services if properly managed. In this thesis, first, a systematic investigation is conducted for two typically employed demand-side management methods reported in the literature: A voltage droop control-based approach and a market-driven approach. Then a control scheme of decentralized autonomous demand side management for electric vehicle charging scheduling which relies on a unidirectionally communicated grid-induced signal is proposed. In all the topics considered, the implications on the distribution grid operation are evaluated using a set of time series load flow simulations performed for representative Austrian distribution grids. Droop control mechanisms are discussed for electric vehicle charging control which requires no communication. The method provides an economically viable solution at all penetrations if electric vehicles charge at low nominal power rates. However, with the current market trends in residential charging equipment especially in the European context where most of the charging equipment is designed for 11 kW charging, the technical feasibility of the method, in the long run, is debatable. As electricity demand strongly correlates with energy prices, a linear optimization algorithm is proposed to minimize charging costs, which uses next-day market prices as the grid-induced incentive function under the assumption of perfect user predictions. The constraints on the state of charge guarantee the energy required for driving is delivered without failure. An average energy cost saving of 30% is realized at all penetrations. Nevertheless, the avalanche effect due to simultaneous charging during low price periods introduces new power peaks exceeding those of uncontrolled charging. This obstructs the grid-friendly integration of electric vehicles.
The electricity demand due to the increasing number of EVs presents new challenges for the operation of the electricity network, especially for the distribution grids. The existing grid infrastructure may not be sufficient to meet the new demands imposed by the integration of EVs. Thus, EV charging may possibly lead to reliability and stability issues, especially during the peak demand periods. Demand side management (DSM) is a potential and promising approach for mitigation of the resulting impacts. In this work, we developed an autonomous DSM strategy for optimal charging of EVs to minimize the charging cost and we conducted a simulation study to evaluate the impacts to the grid operation. The proposed approach only requires a one way communicated incentive. Real profiles from an Austrian study on mobility behavior are used to simulate the usage of the EVs. Furthermore, real smart meter data are used to simulate the household base load profiles and a real low voltage grid topology is considered in the load flow simulation. Day-ahead electricity stock market prices are used as the incentive to drive the optimization. The results for the optimum charging strategy is determined and compared to uncontrolled EV charging. The results for the optimum charging strategy show a potential cost saving of about 30.8% compared to uncontrolled EV charging. Although autonomous DSM of EVs achieves a shift of load as pursued, distribution grid operation may be substantially affected by it. We show that in the case of real time price driven operation, voltage drops and elevated peak to average powers result from the coincident charging of vehicles during favourable time slots.
A new software tool, called AWG-Channel-Spacing, is developed to calculate accurate channel spacing of an arrayed waveguide gratings (AWG) optical multiplexer/demultiplexer. This tool has been developed with the application framework QT in the programming language C++. The tool was evaluated with a design of 20-channel 200 GHz AWG. The achieved simulated transmission characteristics prove the correct functionality of the tool.
By a simple femtosecond laser process, we fabricated metal-oxide/gold composite films for electrical and optical gas sensors. We designed a dripple wavelength AWG-spectrometer, matched to the plasma absorption wavelength region of the composite films. H2/CO absorptions fit well with the AWG design for multi gas detection sensor arrays
A new software tool, called AWG-Wuckler, is developed to calculate geometric parameters of arrayed waveguide grating structures for telecommunication and medical applications. These parameters are crucial for a AWG layout which will be created and simulated using commercial photonic design tools. The design process of AWG is very complex because its geometric dimensions depend on a large number of input design parameters and other input design parameters. Often geometric constraints require an adjustment of the input design parameters and vice versa. Calculation and adjustment of the geometric parameters is a time-consuming process that is currently not fully supported by any commercial photonic tool. AWG-Wuckler tool overcomes this issue and offers a fast and easy to use solution. The tool was already applied in various AWG designs and is technologically well proven.
Back to the future of gaming
(2014)
Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement
(2023)
Entangled photon pairs are essential for a multitude of quantum photonic applications. To date, the best performing solid-state quantum emitters of entangled photons are semiconductor quantum dots operated around liquid-helium temperatures. To favor the widespread deployment of these sources, it is important to explore and understand their behavior at temperatures accessible with compact Stirling coolers. Here we study the polarization entanglement among photon pairs from the biexciton–exciton cascade in GaAs quantum dots at temperatures up to ∼65 K. We observe entanglement degradation accompanied by changes in decay dynamics, which we ascribe to thermal population and depopulation of hot and dark states in addition to the four levels relevant for photon pair generation. Detailed calculations considering the presence and characteristics of the additional states and phonon-assisted transitions support the interpretation. We expect these results to guide the optimization of quantum dots as sources of highly entangled photons at elevated temperatures.
Black titanium dioxide in situ generated on femtosecond laser induced periodic surface structures
(2018)
Blood and breath profiles of volatile organic compounds in patients with end-stage renal disease
(2014)
Bridges between families
(2016)