The ongoing penetration of energy systems with information and communications technology (ICT) and the introduction of new markets increase the potential for malicious or profit-driven attacks that endanger system stability. To ensure security-of-supply, it is necessary to analyze such attacks and their underlying vulnerabilities, to develop countermeasures and improve system design. We propose ANALYSE, a machine-learning-based software suite to let learning agents autonomously find attacks in cyber-physical energy systems, consisting of the power system, ICT, and energy markets. ANALYSE is a modular, configurable, and self-documenting framework designed to find yet unknown attack types and to reproduce many known attack strategies in cyber-physical energy systems from the scientific literature.

The increasing integration of renewable energy resources requires so-called smart grid services for monitoring, control and automation tasks. To develop innovative solutions and algorithms, simulation environments are used for evaluation. Especially in smart energy systems, we face a variety of heterogeneous simulators representing, e.g., power grids, analysis or control components. The co-simulation framework mosaik can be used to orchestrate the data exchange and time synchronization between individual simulators. So far, the underlying communication infrastructure has often been assumed to be optimal, so that the influence of e.g., communication delays has been neglected. This paper presents the first results of the project cosima, which aims at connecting the communication simulator OMNeT++ to the co-simulation framework mosaik to analyze the resilience and robustness of smart grid services, e.g., multi-agent-based services with respect to simulation performance, scalability, extensibility and usability. This facilitates simulations with realistic communication technologies (such as 5G) and the analysis of dynamic communication characteristics occuring by simulating multiple messages. We could show, how the simulation performance of this coupling improves by using the new discrete event scheduling of mosaik and how the simulation behaves in scenarios with up to 50 agents.

Deep streams of data from Earth-imaging satellites arrive in databases every day, but advanced technology and expertise are required to access and analyze the data. Now a new system, developed in research based at the University of California, Berkeley, uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems. Berkeley-based project could support action worldwide on climate, health, and poverty. More than 700 imaging satellites are orbiting the earth, and every day they beam vast oceans of information -- including data that reflects climate change, health, and poverty -- to databases on the ground. There's just one problem: While the geospatial data could help researchers and policymakers address critical challenges, only those with considerable wealth and expertise can access it.

A new way of using machine learning to examine satellite images could help people around the world. More than 700 imaging satellites orbit the earth, but only governments and companies with wealth and expertise can access the data they produce. Now, researchers said in a recent paper that they have invented a machine learning system using low-cost, easy-to-use technology that could bring satellite analytical power to researchers and governments worldwide. "To plan infrastructure like roads and bridges or to target food aid, we need to know where people live and what their needs are," Jonathan Proctor, a co-author of the paper, told Lifewire in an email interview. "Satellite imagery and machine learning can help measure socio-economic conditions in places where other measurements are insufficient."