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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.
Learning together
(2019)
Investigations on mechanical stability of laser machined optical fibre tips for medical application
(2019)
Light delivery is a challenging task, when it comes to medical applications. The light is guided through optical fibers from the light source towards the treatment region. In case of interstitial light application, the light has to be decoupled from the fibre and spread to the surrounding tissue. To reach larger tissue volumes, this can be either obtained by adding a scattering volume to the tip of the fibre, or by directly modifying the optical fibre itself in order to break the total reflection within the fibre core. Such modifications can be either on the fibre surface itself or internally in the fibre core. One approach to obtain the fibre structuring could be laser induced surface roughening using an ultrafast laser source. While using volume scattering as diffusor at the fibre tip is currently the gold standard for non-thermal applications (< 0.3W/cm), the decoupling of high power laser intensities for thermal treatment options is still challenging. Structuring the fibre core itself usually is related with a loss of mechanical stability. As fibre breakage and potential loss within the human body can have serious consequences, the mechanical stability is one of the quality criterion in diffuser manufacturing. Therefore, investigations about the mechanical stability of laser manufactured optical fibre diffusers are needed.
In order to evaluate the mechanical stability, a 4-point as well as a 2-point breaking test were developed. Different fibre diffusers, based on volume or surface scattering, were manufactured using fs-laser ablation techniques and its breaking strengths were investigated.
It could be shown that for surface fibre modifications, the mechanical stability reduces with increasing defect depth. The stability significantly drops when the laser ablation was performed in the thermal energy range. Volume scattering modified fibres only showed a slight reduction in stability compared to un-machined fibres.
In conclusion, internal fibre modification seems to be the most promising method to establish optical fibre diffusers, which are capable of several watts of emission power, while preserving its mechanical strength.
Production and tribological characterization of tailored laser-induced surface 3D microtextures
(2019)
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.
Czech and Slovak social work
(2019)
On the extension of digital ecosystems for SCM and customs with distributed ledger technologies
(2019)
Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major – albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICTenabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region – on which we are currently working – concludes the paper.