In copper-plate based electricity markets, uniform prices neglect local grid constraints, creating distorted incentives for battery energy storage systems that can amplify grid congestion.Despite growing interest in grid-supportive storage operation, no prior work has integrated real-world transmission system operator signals into multi-objective battery scheduling.This work proposes a two-stage approach that jointly maximizes market profit and minimizes the grid signal tracking error for battery energy storage systems, leveraging publicly available transmission system operator grid signals.We use the S-Metric Selection Evolutionary multi-objective optimization algorithm to approximate the Pareto front between profitability and grid-supportive behavior, from which a preferred trade-off solution is refined via ε-constraint single-objective optimization to derive a deployable setpoint schedule.Applied to representative scenario days, the results show that purely price-optimized scheduling amplifies adverse incentives during grid congested periods.Extending grid-aware control to renewable-rich periods causes double-digit percentage profit losses and is not advisable.In contrast, restricting interventions to rare periods when grid congestion is highly probable—comprising fewer than 2% of days annually—substantially reduces the grid signal tracking objective while incurring only approximately 0.1% annual profit loss.These findings demonstrate that selective TSO congestion signal integration enables grid-supportive battery operation with negligible economic impact.

Studying urban district energy transitions requires a flexible and detailed building-specific energy performance analysis. This article presents an open-source Geographic Information System (GIS)-based framework for conducting white-box Urban Building Energy Modeling (UBEM) analysis via a bottom-up approach. The proposed methodology uses available geospatial data to construct 3D building envelopes and equip them with detailed physics-based Heating, Ventilation, and Air Conditioning (HVAC) systems. For each building, both the building envelope and energy system are modeled using the open-source building simulation software EnergyPlus, which is freely available and computationally efficient. The framework is applied to a typical district with 241 buildings in south-west Germany, where its capabilities are showcased by providing dynamic insights into load and HVAC system performance. Three scenarios are studied to: 1) calculate the buildings and entire district demands, 2) simulate the traditional HVAC system comprised of gas-fired boilers and cooling coils, and 3) represent the future HVAC system implementing heat pumps for both heating and cooling purposes. It is concluded that scenario 3, compared to scenario 2, increases the grid dependency by 80.5% and reduces the environmental impacts by 13.9%. Moreover, the reported low computational costs across all scenarios certify the framework applicability for further urban planning and district-scale energy system design.

This paper presents a high-resolution, publicly available measurement data set for IEC 61851 controlled AC charging of 16 different Electric Vehicles (EVs) from different manufacturers, comprising over 100 complete charging sessions. The data set includes phase currents, voltages, active power, and reactive power sampled at 50 Hz, enabling precise capture of transient phenomena such as overshoots and settling dynamics during setpoint transitions. Additionally, raw voltage and current waveforms are recorded at 5 kHz to reveal inverter behavior and power electronics characteristics relevant for grid impact assessment. Using these measurements, the study characterizes charging behavior and quantifies controllability in terms of delay, rise, and fall dynamics following setpoint changes, as well as steady-state compliance with IEC-defined current limits. Our results show substantial deviations from commanded setpoints, with certain EVs exhibiting steady-state errors up to 10 %, demonstrating that a single generic EV model is insufficient for accurate representation of heterogeneous EV fleets in optimization and aggregation applications. Therefore, we compare different charging models to the measurements and identify which model best represents each EV, providing guidance for selecting an appropriate charging model for a given application.

Voltage control in modern power systems has become increasingly complex due to the high penetration of renewable generation. Numerous solutions have been proposed from both the transmission and distribution sides, involving generators and system operators. However, the contribution of loads has remained limited, mainly to demand shifting and basic demand response strategies. This work introduces a novel approach that leverages digital twins to enhance the active participation of loads in supporting voltage control. Unlike traditional methods, the proposed framework builds digital twins exclusively from measurable data, enabling virtually any converter-interfaced load connected to a grid, regardless of whether the network is fully known or not, to contribute effectively to voltage regulation. The methodology is first demonstrated through a parametric study, which evaluates the impact of different load behaviors and control strategies on network voltage stability. To further validate the approach, hardware-in-the-loop (HIL) experiments are conducted, confirming the feasibility of real-time implementation. Four voltage control use-cases are developed and tested for a controllable thermal load, showing that even individual loads can provide meaningful support to grid voltage regulation. The results highlight the potential of data-driven digital twins to unlock new, scalable, and flexible contributions from loads, reinforcing the stability of future power systems with high renewable penetration.

The integration of renewable energy sources into power grids is crucial for reducing carbon emissions, yet it introduces challenges related to frequency stability due to reduced inertia and renewable intermittency. Although many solutions have been explored, the role of loads in frequency control remains underdeveloped. This work presents a novel Digital Twin framework for frequency control, incorporating six load frequency control schemes implemented on a thermal load. Additionally, it proposes a methodology for constructing frequency-control-oriented Digital Twins of power systems. The framework uses real-time measurements to estimate grid parameters—via the swing equation—and continuously tune control gains for enhanced performance. Complementarily, an online inertia estimation technique is integrated to enable fully adaptive strategies, further improving frequency control. The proposed approach is validated through computational and Hardware-in-the-Loop experimentation, showing robust performance under real-world conditions such as measurement noise and delays. The results indicate that integrating Digital Twins with load-based frequency control significantly enhances the power system’s resilience, offering a promising direction for future improvements in grid stability.

Recently, there has been a surge of interest in novel concepts for jointly operating devices in urban areas in clusters, motivated by their potential to support decarbonization, enhance power system flexibility, and promote energy justice. Such clusters encompass multiple devices or buildings but operate on a smaller scale than cities. Examples include Renewable Energy Communities and Positive Energy Districts. These Novel District Concepts (NDCs) integrate interdisciplinary urban planning and social sciences terminology into the energy domain. However, these concepts’ precise definitions and practical implementation lack consistency, leading to conceptual ambiguities in the literature.The present paper reviews clustering approaches from both the energy domain and the urban planning and social sciences disciplines to analyze rules for defining device clusters. The findings reveal that while numerous papers claim novelty using Novel District Concepts terminology, many rely on established energy-domain methodologies, such as clustering techniques structured around electricity grid hierarchies. In contrast, clustering approaches from urban planning and social sciences, which employ spatial and social criteria, remain underutilized and lack systematic evaluation for energy system applications.The present paper’s key contribution lies in systematically identifying and differentiating clustering rules, establishing a robust foundation for subsequent cluster-based research, and ensuring methodological consistency. By integrating concepts from urban planning and social sciences with established energy-domain approaches, this paper delineates clear boundaries and grounds them contextually. The present paper’s structured methodology provides a comprehensive workflow for distinguishing diverse clustering rules, mitigating the risk of misapplied terminology, and facilitating future evaluations of their applicability to specific energy-system tasks.

The integration of heat pumps into energy systems is critical for decarbonisation, with growing installations necessitating their alignment with power grid demands. Heat pump penetration poses challenges for low-voltage grids, such as voltage drops and overloading, while technologies like the SG-Ready interface offer the potential for flexibility and grid-oriented control. However, gaps persist in experimental validation and high-resolution analyses of heat pump behaviour. This study evaluates the dynamic response of two heat pumps using field measurements with 0.2s resolution, focusing on SG-Ready state changes. By openly sharing our dataset, this study advances research on practical applications of grid-oriented control. Results reveal rapid, predictable responses during blocking events but significant variability during start-up, limiting simultaneity in grid operations.

The growing decentralization of energy systems requires scalable, flexible coordination of distributed generation, energy storage, and demand-side flexibility among local energy communities. This work builds upon the agent-based scheduling framework MASSIVE, extending its capabilities to operate in real-world settings. Within the extensive framework, agents participate in the local electricity market by submitting bids based on operational constraints and preferences of local energy components or aggregates, such as a campus. Optimized setpoints derived from market clearing are sent as control signals to physical or simulated assets. To enable the transmission to be modular, interoperable, and responsive in real time, we extend the MASSIVE framework with a lightweight, MQTT-based layer. We validate the applicability of these control signals through a series of experiments involving real hardware and technical and safety constraints. Additionally, a geographically distant battery system was incorporated in real time and it effectively followed market-driven setpoints. The results confirm that a decentralized, agent-based market coordination model facilitates flexible integration of physical energy systems. Plug-and-play functionality, heterogeneous control strategies, and interconnection across regions are collectively offered by the framework, thereby providing a robust path to smart energy communities.

This paper presents the Smart2DC Microgrid Laboratory at the Karlsruhe Institute of Technology (KIT), a cuttingedge facility for DC microgrid research. The laboratory is equipped with modular power electronics, real-time simulation capabilities, and advanced control systems, enabling investigations into decentralized energy supply, demand-side management, and energy efficiency. Distinctive experimental setups-including a DC-powered residential test building, electric vehicle charging infrastructure, and integrated hydrogen systems-facilitate studies on renewable energy integration, energy storage optimization, and next-generation DC technologies. Designed as an openscience platform, the Smart2DC Microgrid Laboratory fosters innovation through research, education, and collaboration with industry.

The increasing introduction of power electronics into electric power systems requires more effort to maintain the stability, reliability, and security of grids. Among these, the frequency stability is worsened by the displacement of conventional generators. Demand response has been considered as an alternative to ensure grid stability. The present work proposes a combined frequency control for railway systems to support grid ancillary services without risking the circulation of trains and the comfort of passengers. In contrast to current demand response algorithms, it provides primary and secondary frequency control, and virtual inertia. In addition, due to the rising dynamics complexity in grids, this work presents a performance-based methodology to assess the interaction among frequency controllers without needing detailed models. Simulation and experimental case studies are presented to exemplify and validate the proposed method. The results demonstrate the effectiveness of the methodology in allowing a fast and lowcomplexity assessment. In addition, the benefits of the proposed control are demonstrated by several performance metrics. The coherence between simulation and experimental results validates the reliability and implementability of the proposed method in a real microgrid.

Integrating flexible loads and storage systems into the residential sector contributes to the alignment of volatile renewable generation with demand. Besides batteries serving as a short-term storage solution, residential buildings can benefit from a Hydrogen (H2) storage system, allowing seasonal shifting of renewable energy. However, as the initial costs of H2 systems are high, coupling a Fuel Cell (FC) with a Heat Pump (HP) can contribute to the size reduction of the H2 system. The present study develops a Comfort-Oriented Energy Management System for Residential Buildings (ComEMS4Build) comprising Photovoltaics (PV), Battery Energy Storage System (BESS), and H2 storage, where FC and HP are envisioned as complementary technologies. The fuzzy-logic-based ComEMS4Build is designed and evaluated over a period of 12 weeks in winter for a family household building in Germany using a semi-synthetic modeling approach. The Rule-Based Control (RBC), which serves as a lower benchmark, is a scheduler designed to require minimal inputs for operation. The Model Predictive Control (MPC) is intended as a cost-optimal benchmark with an ideal forecast. The results show that ComEMS4Build, similar to MPC, does not violate the thermal comfort of occupants in 10 out of 12 weeks, while RBC has a slightly higher median discomfort of 0.68 Kh. The ComEMS4Build increases the weekly electricity costs by 12.06 EUR compared to MPC, while RBC increases the weekly costs by 30.14 EUR. The ComEMS4Build improves the Hybrid Energy Storage System (HESS) utilization and energy exchange with the main grid compared to the RBC. However, when it comes to the FC operation, the RBC has an advantage, as it reduces the toggling counts by 3.48

With the rise of renewable energy, electric vehicles, and batteries, residential buildings are evolving into prosumers, requiring energy management systems (EMSs) to optimize selfconsumption and grid support. Simulations often fail to capture real-world complexities such as fluctuating weather, hardware behavior, and communication delays. To address this, we present the Building Energy Operation Platform (BEOP), a modular and scalable framework for validating real-world EMSs. BEOP supports various EMS types, integrates hardware and software components, and allows multi-resolution performance analysis. Demonstrated through a schedule-based optimization use case, we highlight the impact of real-world factors on EMS performance and advance research in forecasting, optimization, and grid stability.

Energy flexibility is essential for aligning the energy demand with the intermittent electricity generation from renewable energy sources. In the European Union, buildings account for 40 % of the final energy consumption, thus offering significant potential for energy flexibility through load shifting, peak clipping, valley filling, and flexible load shaping. While experimental studies are crucial for providing realistic estimates of cost savings, comparing various control algorithms in the real world is inherently difficult. The present paper addresses this challenge by simultaneously controlling three architecturally identical buildings with different controllers to shift space heating in response to dynamic pricing. Model predictive control (MPC) and fuzzy logic control (FLC) are compared to a baseline control across various experiments, encompassing different objectives, price signals, and comfort levels. Over the course of a one-month experimental study, both MPC and FLC improved thermal comfort while achieving cost savings ranging from 7.8 % to 33.4 % and from 4.4 % to 8.6 %, respectively. The additional savings provided by MPC compared to FLC increase with greater price variability, indicating that MPC is particularly advantageous in markets with high price spreads. Conversely, when prices fluctuate less, the computationally more efficient FLC is sufficient. When minimizing costs, the MPC reduces the heating costs by 33.4 % but merely reduces the CO2 emissions by 2.9 %. Consequently, focusing solely on cost minimization is insufficient to achieve substantial emission reductions.

This paper introduces an innovative hybrid methodology for removing decaying Direct Current Offset from fault current signals, a prevalent challenge in phasor estimation techniques in protective relay applications. Traditional techniques of solving this problem often fall short in efficiency and accuracy, especially when the faulty signal has complex non-linear decay patterns. Our method combines the Cumulative Sum and Fast-Moving Average techniques, utilizing the former's ability to track decaying Direct Current Offset trends and the latter's proficiency in smoothing signal variations. This approach not only enhances the accuracy of decaying Direct Current Offset removal but also preserves the integrity of the underlying signal. We demonstrate the superior performance of our method, highlighting its potential to significantly improve fault signal analysis and the reliability of power system operations.

The wireless Metering-Bus (M-Bus) is widely used in Germany to transmit meter data for heat cost allocation as well as cold and warm water consumption in multi-family apartment buildings. This metering data poses significant privacy risks as it can reveal inhabitants' behaviors. Consequently, the German Heating Cost Ordinance demands this transmission to be both interoperable and secure. However, the wireless M-Bus standard EN 13757 specifies security features as optional. In our work, we conducted a field study by recording sensor telegrams in four cities to assess the implementation of these security features. We analyzed the presence of encryption and the types of metering applications in use. Our findings reveal that about 48.5% of the recorded sensor devices did not have encryption enabled. Additionally, the use of encryption was found to correlate with specific manufacturers, indicating a systematic acceptance of privacy risks. To demonstrate the impact of unencrypted wireless M-Bus radio telegrams on the privacy, we recorded a wireless M-Bus based warm water meter over a period of several weeks and show that inhabitants' presence and sleep cycles can be inferred from the recordings. These findings underscore the need for mandatory security features in the operation of wireless M-Bus based metering applications to protect consumer privacy.

With a high share of renewable energy in the power grid, it becomes increasingly difficult to ensure a continuous balance between power generation and consumption, thereby endangering grid stability. A substantial opportunity to address this challenge and align the heating demand with intermittent power production is offered by space heating and domestic hot water, which account for 80% of the energy consumption in buildings. Further research on the control of building clusters is required, where peak load management of multiple buildings can ensure grid stability during peak hours and contribute to avoiding power outages. In this paper, a rule-based controller is presented, called Extended Price Storage Control+ (EPSC+), for the practical and flexible operation of heating systems in a building cluster. Under dynamic pricing, the loads of electric heating devices for the provision of space heating and domestic hot water are shifted by EPSC+ while accounting for peak load constraints. The performance of EPSC+ is evaluated in a nine-week winter simulation study with a building cluster of ten buildings in Germany. For comparison, a hierarchical model predictive controller (MPC) and a hysteresis two-point controller are employed as benchmarks. Results close to those of MPC are achieved by EPSC+by reducing the median peak load by 38.8% and median electricity costs by 15% compared with the hysteresis controller. In contrast to MPC, EPSC+ does not require models, forecasts, or optimization and is computationally inexpensive, rendering it more attainable for real-world implementation.

Workplace Charging Stations (CSs) are well-suited to improve grid stability by scheduling the charging process over the parking duration and thereby reducing the peak load. Therefore, the energy demand and parking duration of single charging sessions must be known as well as the future occupancy of the CS. Since user IDs are often unknown for privacy reasons, this paper investigates how these parameters can be predicted for future charging events. The charging behavior is examined for its characteristic features, such as location, arrival, and departure times. First, calendar, weather, lag and CS-specific features are implemented and used to train nine different machine-learning algorithms. For the observed data, the Random Forest Regressor yields the best results for parking duration and energy demand. For parking duration, a 33.7% improvement in Mean Absolute Percentage Error (MAPE) over the baseline (the mean parking duration) can be achieved. The MAPE of the parking duration forecast is 71.0% and for the energy demand, it is 84.0% which leads to the conclusion that without the knowledge of user IDs predicting the charging behavior of users is possible only to a limited extent.

The design of new control strategies for future energy systems can neither be directly tested in real power grids nor be evaluated based on only current grid situations. In this regard, extensive tests are required in laboratory settings using real power system equipment. However, since it is impossible to replicate the entire grid section of interest, even in large-scale experiments, hardware setups must be supplemented by detailed simulations to reproduce the system under study fully. This paper presents a unique test environment in which a hardware-based microgrid environment is physically coupled with a large-scale real-time simulation framework. The setup combines the advantages of developing new solutions using hardware-based experiments and evaluating the impact on large-scale power systems using real-time simulations. In this paper, the interface between the microgrid-under-test environment and the real-time simulations is evaluated in terms of accuracy and communication delays. Furthermore, a test case is presented showing the approach's ability to test microgrid control strategies for supporting the grid. It is observed that the communication delays via the physical interface depend on the simulation sampling time and do not significantly affect the accuracy in the interaction between the hardware and the simulated grid.

Achieving net-zero carbon emissions necessitates the major transformation of electrical grids into smart grids. In this context, urban districts play a crucial role in the flexible balancing of electricity demand and supply, which involves solving decentralized optimization problems. Such optimization problems rely on forecasts of local demand and supply, and require the systematic orchestration of data streams using cloud services. At the same time, it is necessary to automate both the design and operation of forecasting models in such services to keep pace with the increasing need for such locally adapted forecasts. Therefore, this paper proposes an automation level taxonomy to communicate the scope of automation in time series forecasting. Furthermore, we demonstrate a forecasting service that is used in a downstream demand-side management application and realized in the real-world project Smart East in Karlsruhe, Germany. Finally, we analyze existing forecasting services in the literature, categorize them according to the proposed automation level taxonomy, and compare them with our implementation.