pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

The Effect of Ego Depletion on Prosocial Behavior

Individuals’ exertion of self-control may lead to ego depletion, a psychological concept suggesting that people have a finite amount of self-control (Wang et al., 2017). The current research examines if ego depletion affects (i.e., reduces) individuals’ prosocial behavioral intentions (study 1) and/or their likelihood to engage in prosocial behavior (study 2). The current project also examines if the effect of ego depletion on prosocial outcomes is moderated by participants’ moral identity. After providing informed consent, participants were randomly assigned to one of three conditions (high depletion, low depletion, control), where they were asked to write continuously for at least five minutes. Participants were either restricted from using the letters A or N (high depletion), X or Z (low depletion), or did not have letter restrictions (control) during the writing task. After completing one of the writing tasks, participants were presented with a self-report questionnaire assessing their prosocial behavioral intentions (study 1) or two optional short answer questions that would presumably help the researchers with future work (study 2). The results revealed that having a strong moral identity was associated with greater prosocial intentions and behavior. Additionally, although ego depletion condition did not affect individuals’ prosocial intentions, individuals who exerted the greatest self-control—in the high ego depletion condition—were less likely to engage in prosocial behavior. Finally, the interaction between moral identity and ego depletion was not significant. The present studies suggest that high ego depletion may reduce people’s self-control behavior, yet they are unaware of such depletion

Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources

A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization

Gefördert durch den Publikationsfonds der Universität Kasse

Decentralized Packet Clustering in Router-Based Networks

Different types of decentralized clustering problems have been studied so far for networks and multi-agent systems. In this paper we introduce a new type of a decentralized clustering problem for networks. The so called Decentralized Packet Clustering (DPC) problem is to find for packets that are sent around in a network a clustering. This clustering has to be done by the routers using only few computational power and only a small amount of memory. No direct information transfer between the routers is allowed. We investigate the behavior of new a type of decentralized k-means algorithm — called DPClust — for solving the DPC problem. DPClust has some similarities with ant based clustering algorithms. We investigate the behavior of DPClust for different clustering problems and for networks that consist of several subnetworks. The amount of packet exchange between these subnetworks is limited. Networks with different connection topologies for the subnetworks are considered. A dynamic situation where the packet exchange rates between the subnetworks varies over time is also investigated. The proposed DPC problem leads to interesting research problems for network clustering

Self-synchronized duty-cycling for mobile sensor networks with energy harvesting capabilities: A swarm intelligence study

When asked if ants rest or if they work untiringly all day long, most people would probably respond that they had no idea. In fact, when watching the bustling life of an ant hill it is hard to imagine that ants take a rest from now and then. However, biologists discovered that ants rest quite a large fraction of their time. Surprisingly, not only single ants show alternate phases of resting and being active, but whole ant colonies exhibit synchronized activity phases that result from self-organization. Inspired by this self-synchronization behaviour of ant colonies, we develop a mechanism for self-synchronized duty-cycling in mobile sensor networks. In addition, we equip sensor nodes with energy harvesting capabilities such as, for example, solar cells. We show that the self-synchronization mechanism can be made adaptive depending on the available energy.Postprint (published version

Pancreaticoduodenectomy in 11-Year-Old Male With a Non-Functional Pancreatic Neuroendocrine Tumor

Pancreatic neuroendocrine tumors (pNETs) are rare pancreatic neoplasms and are even more uncommon in the pediatric patients, thus leading to a lack of clinical research on diagnosis and management in this population. The purpose of this report is to review relevant literature and discuss a rare occurrence of a non-functioning pNET in a pediatric patient. This is a report of an 11-year-old male who presented with symptomatic anemia and was found to have a 6 cm mass near the pancreatic head with erosion into the duodenum. Surgical biopsy demonstrated a non-functioning pNET. He was successfully managed with complete surgical resection via a standard pancreaticoduodenectomy without evidence of recurrence after 1 year follow up. Further multi-institutional prospective studies or meta-analyses are warranted to further explore optimal management in the pediatric population

Schlussbericht Ladeinfrastruktur 2.0 des Fraunhofer Instituts für Energiewirtschaft und Energiesystemtechnik (IEE)

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