Due to the energy transition and the distribution of electricity generation, distribution power systems gain a lot of attention as their importance increases and new challenges in operation emerge. The integration of renewables and electric vehicles for instance leads to manifold changes in the system, e.g. participation in provision of ancillary services. To solve these challenges artificial intelligence provides a variety of solutions based on the increase in sensor data and computational capability. This paper provides a systematic overview of some of the most recent studies applying artificial intelligence methods to distribution power system operation published during the last 10 years. Based on that, a general guideline is developed to support the reader in finding a suitable AI technique for a specific operation task. Therefore, four general metrics are proposed to give an orientation of the requirements of each application. Thus, a conclusion can be drawn presenting suitable algorithms for each operation task.

Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.

The renewable energy resources have paved the way for distributed energy resources (DERs) integration in to the distribution grid. As a result, the load composition and their dynamics have become complex. The weather phenomena and new emerging consumer load patterns like electric vehicle contribute to time varying dynamics of these loads. In order to optimize the utilization of system assets and flexibility of DERs, the identification of time varying load dynamics is necessary. In this paper, the identification of time varying load dynamics is explored by combining system identification methods and nonlinear numerical optimization. The identified model parameters are then related to measurement data by means of artificial neural networks, which enables the identification of similar dynamics without opting to numerical optimization methods.