The widespread integration of photovoltaic (PV) systems into modern power grids poses several operational challenges, primarily due to the stochastic nature of weather conditions, which leads to uncertainty in power generation. In this context, accurate forecasting of PV output becomes critical, especially for electricity markets and grid management. This paper investigates the relationship between non-conventional weather variables (i.e., ground-based cloud observations) and PV power generation, and evaluates the impact of incorporating such data into a machine learning model to improve forecasting performance. The analysis is conducted using real power output and ground-based meteorological data collected at Forschungszentrum Jülich, in Germany.

Non-invasive parameter estimation of power electronics enables condition and health monitoring, enhances control strategies, and thus, guarantees reliable and stable operation of power converters. In this work, we propose the application of Physics-Informed Gaussian Processes (PIGP) for parameter estimation of power electronic converters. We provide a detailed model-building scheme allowing non-invasive characterisation of power converters. In particular, we show that the proposed approach can be applied independently of the particular noise level without the need for data pre-processing. Tested with a DC-DC buck converter in a simulation scenario, we show that the proposed approach is immune to measurement noise and can be executed with low sampling rates. This allows fast, possibly online, execution and thus tracking of converter parameter changes due to thermal effects or aging during operation.

This paper presents a real-time simulation framework for the evaluation of V2G scheduling applications in distribution networks. Mathematical models of electric vehicles (EVs), uni- and bi-directional charging stations (CSs), and Battery Energy Storage System (BESS) are introduced for power management analysis. A power management (PM) algorithm for peak load shaving via BESS, and Vehicle-to-Grid (V2G) technology is presented to exercise the framework. The overall framework is validated by considering the impact of high penetration of EVs on the campus network of Forschungszentrum Jülich, and the V2G-based mitigation solution.

In this paper, we present an MQTT gateway for Hardware-in-the-Loop (HIL) testing of energy systems control solutions. The proposed solution is the result of a workflow that automatise the mapping process between the set points obtained from the controllers, and the corresponding parameters in the simulation model. In the workflow, different MQTT topics are created for each controlled parameter in the simulation model, then the communication flows are generated in the open-source platform Node-RED. The validity of the proposed solution is investigated through its implementation in an HIL co-simulation framework, where a power system is coupled with a low-temperature district heating network and its model predictive control-based controller acting as a device under test.

In this paper, we present a Hardware-in-the-Loop (HIL) co-simulation framework to test multi-modal energy systems. The framework has been tested using as an example a section of the Living Lab Energy Campus (LLEC) of the Forshungszentrum Jülich (DE). A master algorithm has been developed to orchestrate the two components of the co-simulation platform: the real-time simulation of the power network running on OPAL-RT, and the real-time simulation of the low-temperature district heating (LTDH) network using Functional Mock-up Units (FMU) on a custom cluster. The master algorithm also coordinates the exchange of information between the real-time simulators and the device under test. The device under test is composed of a cloud-based model predictive control (MPC) -that operates on the heat pumps in the LTDH network - and an MQTT broker. The results show the co-simulation is successful and the framework can validate the control algorithm.