The number of photovoltaic (PV) installations is increasing. Within 2024, an additional 16.2 GW of PV capacity was installed in Germany. In total, the majority of installed systems are connected to the low-voltage grid and mounted on rooftops. This development marks a structural shift from passive, radial grids to networks characterized by volatile renewable generation. As a result, low-voltage grids experience increasing bidirectional power flows and more variable net injections at the grid connection points. Since PV is expected to remain a central pillar of the transition toward a carbon-free energy system and in meeting the 1.5°C climate target, associated issues-such as grid congestion and voltage band violations-are expected to intensify. Consequently, accurate forecasting of the grid state is essential for distribution system operators (DSOs) to enable proactive and preventive grid management. This work presents a real-time system for nowcasting the state of low-voltage distribution grids using open-source approaches, developed and validated with reference to a real German distribution grid in Hittistetten. The grid model is kindly provided by the local DSO, Stadtwerke Ulm/Neu-Ulm Netze GmbH, to the Smart Grids Research Group as a representative test area, with measurement data available from various grid components. The objective is to demonstrate a forward-looking methodology that enables DSOs to maintain reliable and stable grid operation under increasing PV penetration. The core of the system is a solar radiation forecast based on data from the Meteosat Second Generation (MSG) satellite. This feeds into a physical PV power model to generate feed-in forecasts for the grid area. A complementary load forecast is generated using modified standard load profiles. These feed-in and load forecasts are combined and processed through a load flow algorithm to determine the future grid state. Because the load profiles are based on annual aggregates, the load forecast is further refined using real-time measurements of transformer utilization. The complete model chain is comprehensively validated using measurement data from the operational grid. All components of the model show strong correlation with observed data and low prediction errors. The system provides accurate PV generation forecasts and, at an aggregated level, reliable grid status predictions for two out of three transformers in the test network. This study demonstrates that data-driven nowcasting of the low-voltage grid state is not only technically feasible but also represents a key contribution to ensuring grid stability amid the evolving dynamics of the energy system, ultimately enabling DSOs to perform active grid control.

As solar photovoltaics (PV) expand rapidly worldwide, electricity markets and regulatory frameworks must adapt to integrate increasing shares of variable renewable energy. This paper provides a structured qualitative comparison of PV participation in energy, capacity, and balancing markets across eight countries: Germany, Switzerland, France, Italy, Denmark, Finland, Norway, and Morocco. It analyses market participation rules, remuneration mechanisms, exposure to wholesale price signals, and access to flexibility and balancing services. The results show that although similar policy and market instruments are used across countries, their design and relevance differ substantially depending on market structures, renewable resource endowments, and regulatory priorities. The comparison highlights increasing efforts to promote system-friendly operation and stronger market coordination, while underlining the need to preserve investment security under rising PV penetration.

With the global transition to sustainable energy, Distributed Energy Resources (DER) are becoming increasingly important in smart grids. To facilitate energy transition and advance communication with DER, stakeholders in the smart grid should consider appropriate communication standards. This paper evaluates two key protocols widely adopted for DER integration in smart grids: IEC 61850 and IEEE 2030.5. It comprehensively assesses the testability, performance, and stability of protocols across various scenarios, including network impairments, data model size, and data transition intervals. Key focuses include the definition and analysis of use cases, construction of simulated testing environments, and structured design and implementation of test cases. The methodology employs the Smart Grid Architecture Model (SGAM) to guide use case analysis, while the Holistic Test Description (HTD) method was applied for structured test design, combined with system testing approaches to ensure high test reusability. The paper concludes with an assessment framework and virtualized testing environments for protocol evaluation, highlighting the role of secure communication and data exchange in accelerating the energy transition towards sustainable systems.

This paper presents a structured testing method to assess the capability of photovoltaic (PV) inverters connected via a tele-control interface through the German SmartMetering-Infrascture with control function to provide automatic Frequency Restoration Reserve (aFRR) services. The investigation focuses on communication interfaces, system architecture, and performance metrics under various communication network conditions. The experimental results highlight the impact of communication delays rather than protocol selection on the responsiveness and accuracy of control commands.

Solar photovoltaic (PV) generation is a cornerstone of sustainable energy production, but predicting its capacity across countries remains challenging due to factors like climate, terrain, and population density. To address this, a recent study proposed a novel approach using transfer learning, which is particularly valuable when historical data for newly established PV plants is limited. The study evaluated four PV plants in South Korea and Germany, selected for their diverse geographical and climatic conditions. The proposed CL-Transformer model outperformed established machine learning models such as LSTM, CNN-LSTM, and Transformer, consistently demonstrating superior predictive capabilities. Notably, when trained on Korean data and applied to both South Korea and Germany, the model achieved an average R (2) (adj) improvement of 23.5 %. When trained on German data, the improvement was even more pronounced at 67.3 %. Additionally, transfer learning experiments revealed up to a 50.6 % enhancement in R (2) (adj) across different plant scales. By integrating external weather variables and satellite data, this hybrid model provides valuable insights for accurate capacity prediction and strategic planning in deploying new PV plants, contributing to greater stability and efficiency in the power industry.

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

This paper presents an interdisciplinary, novel approach for incorporating day-ahead solar forecast obtained using numeric models into a real-time simulation framework for low-voltage microgrid analysis. The solar forecast data were integrated into the grid simulation at the information, communication, and function levels, utilising the data model and communication structure defined in the international standard IEC 61850. Given the forecast of solar power and a reference trajectory defined by the upper-level grid management system over a sliding predictive time window, a model predictive control scheme has been implemented to compute control setpoints for solar curtailment in a simplified simulation scenario, regardless of the power flow over lines. In the virtualised environment with decentralised intelligent controllers in containers, the control setpoints are communicated between the IEC 61850 client and server and implemented as measures for solar peak-shaving. The test results showed that the model predictive control scheme outperformed trivial linear control methods, suggesting that the container-based virtualisation concept has the potential to be further exploited for practical use cases at the utility and energy market level, and the IEC 61850 standard could be a feasible solution in terms of grid communication and forecast integration.

Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.

With the number of PV systems installed in Germany rising again since 2018, the provision of system services is increasingly coming into focus. On sunny off-peak days, the use of PV systems to provide balancing power will be indispensable in the future power grid [1]. This paper is based on a joint project, which is funded by the Ministry of Science, Research and the Arts of Baden- Wiirttemberg, with the goal to develop a solution that will enable a large proportion of all photovoltaic (PV) systems to participate in the balancing power market [2]. In the context of the European harmonization of energy markets, the balancing energy market is therefore also undergoing change. In principle, PV plants have a very high potential for providing negative balancing power. Small PV systems with an active power limitation (peak power capping) of 70% can additionally provide considerable amounts of positive control power. Technically, cost-effective control of small-scale PV systems via the German smart meter infrastructure is a very attractive option based on the findings presented in this paper. Regulatory hurdles, such as prequalification or market conditions in the balancing energy market can also be overcome in the foreseeable future, in the view of the proj ect partners. However, stronger support at the political level is necessary for their timely elimination. An important step for a cost-efficient technical solution for the control of small PV plants results from the implementation obligation from the German federal law for the digitalization of the energy transition [3]. This conversion to smart metering represents an essential metrological basis for participation in the balancing energy market.As an additional benefit, the introduced intelligent metering system (iMSys) can also be used to control PV systems. The communication channel available for iMSys can essentially be shared here for controlling local plants without much additional effort. This paper proposes a concept that connects all systems involved to realize such an optimised plant controllability. For this purpose, a local control device (so-called CLS-gateway) connects the PV inverter with the remote control system of the balancing service provider (BSP). For communication, the secured channel provided by the iMSys is used, which is encrypted by the highly secured Smart Meter PKI. For the communication between the field device and the BSP control system, the MQTT protocol based on the publish-subscribe scheme is used [4]. The data models used are based on IEC 61850-7-4/420, but in contrast to the standard, they are mapped in JSON and not with MMS.

As a result of the energy transition, an increasing number of Decentralized Energy Systems (DES) will be installed in the distribution grid in the future. Accordingly, new methods to systematically integrate the growing DES in distribution power systems must be developed utilizing the constantly evolving Information and Communication Technologies (ICT). This paper proposes the Automated Data Model Integration of DES (ADMID) approach for the integration of DES into the ICT environment of the Distribution System Operator (DSO). The proposed ADMID utilizes the data model structure defined by the standard-series IEC 61850 and has been implemented as a Python package. The presented two Use Cases focus on the Supervisory Control and Data Acquisition (SCADA) on the DSO operational level following a four-stage test procedure, while this approach has enormous potential for advanced DSO applications. The test results obtained during simulation or real-time communication to field devices indicate that the utilization of IEC 61850-compliant data models is eligible for the proposed automation approach, and the implemented framework can be a considerable solution for the system integration in future distribution grids with a high share of DES. As a proof-of-concept study, the proposed ADMID approach requires additional development with a focus on the harmonization with the Common Information Model (CIM), which could significantly improve its functional interoperability and help it reach a higher Technology Readiness Level (TRL).

Due to the currently high prices for balancing power, the provision of system services from PV plants is once again increasingly coming into focus. PV plant operators could generate additional revenue by participating in the balancing power market, and transmission system operators would benefit from an increased supply of balancing power. The use of PV plants to provide balancing power on sunny off-peak days could even become indispensable in the future. However, a lack of costeffective technical solutions for control as well as regulatory hurdles have so far prevented PV systems from participating in the control power market on a larger scale. A joint research project promises to remedy this situation, in which a technical prototype for the cost-effective control of small PV plants has already been successfully implemented.

Solar power generation at solar plants is a strongly fluctuating non-deterministic variable depending on many influencing factors. In general, it is not clear which and how certain variables influence solar power supply at feed-in points in a distribution network. Therefore, analyzing the dependence structure of measured solar power supply and other variables is very informative and can be helpful in designing probabilistic prediction models. In this paper multivariate D-vine copulas are fitted to investigate the relationship between solar power supply and certain meteorological variables in the current time period of one hour length as well as solar power supply in previous time periods. The meteorological variables considered in this analysis are global horizontal irradiation, temperature, wind speed, humidity, precipitation and pressure. By applying parametric D-vine copulas useful insight is gained into the dependence structure of solar power supply and the considered meteorological variables. The main goal lies in determining suitable explanatory variables for the design of probabilistic prediction models for solar power supply at single feed-in points and analyzing their impact on the validation of conditional level-crossing probabilities.

The implementation of a Smart Metering Infrastructure (SMI) in Germany offers the opportunity to gather grid measurements in the low voltage grid and enable small scale systems like Photovoltaic(PV) systems for grid friendly control. The practical control of Decentralized Energy Resources (DER) can be realized via the CLS (Controllable-Local-System)-Gateway, which is implemented as a complementary device to Smart Meters Gateway. Advanced grid management systems can use the CLS-Gateways for low voltage grid optimization to prevent grid asset overloading and voltage band violation. This contribution presents the results from laboratory testing utilizing the Software-/Controller-in-the-Loop methodology.

With the ongoing digitalization in the energy transition and the rollout of intelligent measuring systems (iMSys) in Germany, new opportunities arise in communicating with distributed energy systems. For monitoring and telecontrolling the increasing number of distributed energy resources (DER) as well as flexible loads, the use of controlling systems in the distribution grid is gaining more importance. Therefore, different applications have been developed and basic functionalities tested in a laboratory and field environment. As a part of this progress the presented contribution focuses on the implementation of a test bench for monitoring and controlling systems named Controllable Local System (CLS) gateways. Based on the Controller Hardware-In-the-Loop (CHIL) respectively Power Hardware-In-the-Loop (PHIL) method, the test bench validates the bidirectional communication functionalities of a CLS Gateway, which is coupled with a photovoltaic inverter. The implemented test bench features a test automation which enables the conduction of multiple test scenarios concerning functionality, stability as well as resilience of a test object. Mainly this aims at investigating the suitability of such CLS gateways for field operation. Aside of the implemented test bench first test results are presented in this work. The investigated test CLS gateway has revealed a reliable transmission of measurement and control data during long-term examination. Also, frequent power interruptions can lead to failure of functionalities.

O ver the last two decades, grid-connecte d solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually.In 2019 the total worldwide installed photovoltaic e le ctricity ge neration capacity exceeded 630 GW.It is forecasted that 1 TW will be reached by 2022.This further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delive red to the customers.The installation of PV systems for selfconsumption is already now an interes ting option for many pe ople but in general limited to those who have access to a rooftop they own or can use.Enabling residents of multi apartment buildings to commonly use electricity ge nerated by a PV system (collective self-consumption) is a relative ly new development and is still facing a lot of administrative and regulatory challenges.This paper provides an overview of existing regulatory schemes in IEA PVPS countries and presents and analysis of two self-consumption case studies .

The integration of decentralized renewable energy systems into our distribution networks leads to a need of more detailed information about local network structure and state estimation down to the low voltage level [1]. This enforces the transformation of today's distribution networks into smart grids. Smart Meters with Smart Meter Gateways (iMSys) and Controllable Local Systems (CLS) are the essential new bricks of the future smart grid. In Germany the new law "Digitalization of the Energiewende"[2] sets up the rules for network operators to establish this secure energy information system based on the smart meter infrastructure. During the last two years the authors developed and demonstrated on laboratory and field level such a secure energy information system. The main innovation of the project is the direct and secure communication with decentralized energy systems such as photovoltaic inverters, battery storage systems, E-mobility charging stations or power to heat applications within this new smart meter infrastructure, which has been defined by technical rules from the German regulator for data security (BSI) [3]. The two-way communication is able to read measurement values from the field as well as change set points or activate curtailment of decentralized energy systems (see figure 1).

This contribution describes a setup for the combined system and equipment testing of micro grid and smart grid control concepts and components. The key aspects is the use of simple setup compared to typical power hardwarein-the-loop setup. This is achieved by using steady-state load flow calculations and a switched-mode amplifier. This setup was used to test a simple coordinate voltage control for distribution grids utilizing decentralised generation units. The used controllers and infrastructure comply with the German advanced metering infrastructure according to the digitalisation of the Energy Transition Act in July 2016.