The DREAM innovative software architecture for high DG-RES distribution grids

The DREAM software architecture model describes a reference class model, that aids in integrating the different components for active distribution grids. Theapplication domains, in which the framework can be used range from simulation (proof-of-concept) to implementation (proof-of feasibility). The frameworkfacilitates interoperability on the software and hardware level as well as from the communication technology level. The framework was designed from a use casesperspective. The major functionality implemented relates to flexible, heterarchic aggregation and coordination ofdevices involved in demand and supply of electricity. In the grid context aim, is to achieve a common objective, prioritize actions and operate on various timescales of grid operational and market functions. To that end, in the framework, monitoring data are handled and stored in a distributed fashion in order to directly steer or coordinate the operation of devices. These persistent dataalso allow handling forecasts and create interaction possibilities with actors or communities of actors on global and local markets and with operations in activedistribution grids and customer energy management. A first implementation is now being built

Tool catalogue frame-based information tools

In the perception, knowledge production and policymaking on complex issues (‘wicked problems’), such as climate change, frames and framing play an important but often hidden role. Frames relate to one’s ‘schemas of interpretation’; the conceptual images, values, starting points, and mental models that one may have of an issue. This can include, for instance, one’s problem definition, perceptions of the cause-effect relationships in an issue, one’s primary goals, perception of one’s and others’ roles and responsibilities relating to the issue, and views on suitable strategies and interaction with (other) stakeholders (cf. Dewulf et al., 2005)

Dynamic vehicle routing with time windows in theory and practice

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon’s benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment

Dynamic vehicle routing with time windows in theory and practice

The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon's benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment.Algorithms and the Foundations of Software technolog

Precision study of radio emission from air showers at LOFAR

Radio detection as well as modeling of cosmic rays has made enormous progress in the past years. We show this by using the subtle circular polarization of the radio pulse from air showers measured in fair weather conditions and the intensity of radio emission from an air shower under thunderstorm conditions

Thunderstorm electric fields probed by extensive air showers through their polarized radio emission

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