The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, the authors present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasising challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture.

Autonomous and collaborative vehicles not only are seen as a possible solution to reducing congestion and traffic related fatalities. They also provide an excellent multi-domain test bench for engineering education at undergraduate and graduate level. Yet, the use of real scale platforms for experimental educational activities bears prohibitive costs and complexity. While several small scale autonomous platforms have been developed in recent years to address this issue, still they require a significant investment of time and money, which is not always ideal for undergraduate education. Furthermore, none of the available platforms are specifically developed for platooning experiments. In this paper, we detail the results of an undergraduate student's project where a pair of relatively low-cost, off-the-shelf small scale RC cars have been used to implement and test a well known platooning algorithm from the literature. Furthermore, a Virtual Potential Field (VPF) based lateral controller has been included in order to allow the cars to navigate a prescribed closed-circuit track. Self-location of each car has been obtained via a ceiling-mounted motion capture system. Results have shown that, even using a relatively low sampling rate of 10 Hz, accuracies in the order of 1 cm can be obtained when platooning at 0.5 m/s along a circuit of 4 by 3 m. As further improvements to the platform, apart from higher sampling rates for the control law, the inclusion of onboard perception is being explored, in order to eliminate the need for an external motion capture system.

A numerical model of a power system can be used to get accurate insights into the impact of policies and investment decisions regarding the transformation of the energy system, while also helping in identifying bottlenecks in implementing decisions. Spatial aggregation, especially for generation and load, must be carefully approached to obtain such a valid model of a power system. The two main contributions of this paper are introducing a valid model of the Dutch high-voltage power system based on open data and open-source software, and proposing a method for spatially aggregating generation and load capacities to high-voltage nodes of the power system. The representative model will enable interdisciplinary research on policy-making and investment decisions specific to the Netherlands.