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.

A detailed knowledge of system parameters is crucial in modern industrial power electronics. Applications ranging from condition monitoring and control to data-driven modeling, in short, a reliable power electronics application. In this work, we present the method of Automatic Differentiation Shooting (ADShooting) and give the construction of a lightweight Physics-Informed Neural Network (PINN) allowing non-invasive extraction of power electronics parameters. In particular, we present dedicated training and noise handling strategies, both being major aspects of the given application. Exemplary, we apply both approaches for power electronics parameter estimation of a DCDC buck converter. In particular, we investigate the dependence of sampling rates, as well as, the influence of noise. As the execution time is a limiting factor in any online application, we investigate the algorithm’s runtime in a continuous operation, indicating possible detectable physical effects and application scenarios.

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.

Real Time (RT) simulation of Power Electronics (PE) allows engineers to interface real-time controls for controller hardware-in-the-loop CHiL. CHiL verification moves a design from Technology Readiness Level (TRL) 3 to TRL 4. For RT CHiL simulation of DC protection systems, the RT simulation platform must be able to simulate common mode behavior, various grounding schemes, and fault transients at sufficiently high resolution so as not to interfere with the protection system design. This paper demonstrates this capability using a Latency Based Linear Multistep Compount (LB-LMC) simulation method implemented in Field Programmable Gate Arrays (FPGAs), in order to achieve 50ns resolution of common mode behaviors, including Line-to-Ground (LG) overvoltages resulting from LG faults and fault recovery in ungrounded DC systems. This resolution in RT cannot be achieved with today's commercial off-the-shelf (COTS) RT CHiL platforms. Furthermore, this capability can be expanded to other grounding schemes and larger PE networks, enabling RT CHiL validation of protection schemes for networks of power electronic converters at TRL 4.

The paper presents a Hardware-In-the-Loop (HIL) platform for testing and evaluating networked control algorithms for microgrid applications. The developed platform supports the testing of control approaches with high communication bandwidth requirement and includes real-time simulation and emulation of both power and communication grids. The platform operability is demonstrated using a one-zone Medium Voltage DC (MVDC) shipboard power system with a distributed voltage control implemented using a Field Programmable Gate Array (FPGA) based control platform.

This paper presents a two-stage optimization approach to mitigate the rapid voltage fluctuations and minimize the power losses of distribution systems due to the high penetration of photovoltaic (PV) generation. The first stage is a day-ahead optimal strategy which aims to minimize the total voltage deviations and power losses within the constraints of the daily maximum allowable number of operations of the on-load tap changers (OLTCs) and shunt capacitors (SCs). The second stage is a real-time inverter reactive power control to compensate for the uncertainties of PV output and load demand. As a part of the real-time control, an artificial neural network (ANN) approach is used to estimate the system states. In both stages, the optimization problems are formulated as nonlinear optimization problems and solved with direct search algorithms. The real-time optimization method is tested using a Hardware-In-the-Loop (HIL) simulation platform. A modified IEEE 34-node test feeder is applied to demonstrate the effectiveness of the proposed approach.

In this work we present a FPGA implementation of the Latency Insertion Method (LIM) to perform simulation of power electronics systems with high switching frequencies (e.g. SiC based power converter with fsw= 100 - 200kHz). We describe how LIM is used to model a buck and a three phase AC/DC converter and how a FPGA solver based on this approach is implemented. Simulation test performed on a Virtex-7 are presented. All real-time simulations are performed with time steps of 40ns and smaller.

Simulation is a fundamental tool for the design of next generation power systems. At the same time, existing simulation methods are inadequate to tackle the complexity of such systems. In this paper we present a simulation approach based on the combination of Quantized Discrete Event System Specification (QDEVS) and the Latency Insertion Method (LIM). The combination of these two approaches leads to the definition of a simulation method which is highly parallelizable, capable of updating individual models asynchronously, and with performance automatically adaptable to different levels of desired detail. After the description of the method we illustrate the performance of the proposed simulation method using a microgrid example.

The geographically distributed simulation concept enables connecting laboratories over long distances with the goal of sharing simulation resources and integrating multiple (Power) Hardware-in-the-Loop setups. The main obstacle in applying this concept is the impact of the communication medium on fidelity and stability of the simulation. This paper presents advantages and challenges of developing a simulator-to-simulator interface based on the time-frequency representation of interface quantities. The proposed approach is first analyzed using a simple electrical circuit as case study, and then conclusions are verified on a larger system including voltage-source converters that realize a high-voltage dc point-to-point link which connects two ac systems. To assess the approach in a realistic framework, an Internet-distributed simulation platform that integrates two remote real-time digital simulators, OPAL-RT (located at University of South Carolina, USA) and OPAL-RT (located at RWTH Aachen University, Germany) is developed and both linear and nonlinear system models are simulated.

Real-time simulation and its use in Hardware in the Loop and Power Hardware in the Loop testing may not be realized with one single hardware platform. Instead, multiple systems are needed to realistically address the analysis and testing needs of the smart grid. This is particularly the case when the target system is the future electrical distribution grid, with its multi-physic, hybrid nature, its pervasive power electronics and communication. The approach adopted at the authors' laboratory is presented to exemplify the interface and integration challenges when such platform is extended to realize relevant scenarios.