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.Index Terms-Python -open source -power flow -optimal power flow -short circuit -IEC60909 -automated network analysis -power system analysis -graph search

Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system's behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model's predictions through a swarm robotics experiment in which robot teams Electronic supplementary material The online version of this article (306 Swarm Intell (2011) 5:305-327 must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.

The artificial bee colony optimization (ABC) is a population-based algorithm for function optimization that is inspired by the foraging behavior of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new, good solutions and onlooker bees (OBs) that search in the neighborhood of solutions found by the EBs. In this paper we study in detail the influence of ABC's parameters on its optimization behavior. It is also investigated whether the use of OBs is always advantageous. Moreover, we propose two new variants of ABC which use new methods for the position update of the artificial bees. Extensive empirical tests were performed to compare the new variants with the standard ABC and several other metaheuristics on a set of benchmark functions. Our findings show that the ideal parameter values depend on the hardness of the optimization goal and that the standard values suggested in the literature should be applied with care. Moreover, it is shown that in some situations it is advantageous to use OBs but in others it is not. In addition, a potential problem of the ABC is identified, namely that it performs worse on many functions when the optimum is not located at the center of the search space.Finally it is shown that the new ABC variants improve the algorithm's performance and achieve very good performance in comparison to other metaheuristics under standard as well as hard optimization goals.

Network integration studies try to assess the impact of future developments, such as the increase of Renewable Energy Sources or the introduction of Smart Grid Technologies, on large-scale network areas. Goals can be to support strategic alignment in the regulatory framework or to adapt the network planning principles of Distribution System Operators. This study outlines an approach for the automated distribution system planning that can calculate network reconfiguration, reinforcement and extension plans in a fully automated fashion. This allows the estimation of the expected cost in massive probabilistic simulations of large numbers of real networks and constitutes a core component of a framework for large-scale network integration studies. Exemplary case study results are presented that were performed in cooperation with different major distribution system operators. The case studies cover the estimation of expected network reinforcement costs, technical and economical assessment of smart grid technologies and structural network optimisation.

In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action. The novelty of our method is that it allows robots to collectively find consensus on the fastest action without measuring explicitly the execution times of all available actions. We study two analytical models of the decision-making method in order to understand the dynamics of the consensus formation process. Moreover, we verify the applicability of the method in a real swarm robotics scenario. To this end, we conduct three sets of experiments that show that a robotic swarm can collectively select the shortest of two paths. Finally, we use a Monte Carlo simulation model to study and predict the influence of different parameters on the method.