The electrification of heating and transportation, together with the increasing penetration of distributed renewable energy resources, is significantly transforming load dynamics in low-voltage distribution grids. Although forecasting models are typically evaluated using statistical error metrics, the physical implications of forecast inaccuracies on grid operation remain insufficiently understood. This study investigates how node-level net load forecasting errors propagate to grid-state variables, namely line loading and voltage deviations. Using a real German low-voltage grid topology and customer-level data, cluster-based global forecasting architectures employing machine learning and deep learning are coupled with non-linear power flow simulations to quantify the operational impact of forecast inaccuracies. The results show that improvements in forecasting accuracy translate almost proportionally into reductions in line-loading and voltage-deviation errors. In particular, models that accurately capture net load valleys during periods of high PV generation also achieve superior grid-state prediction performance, highlighting the operational relevance of valley-oriented forecasting metrics. By linking predictive performance directly to physical grid constraints, the proposed framework provides a systematic method for assessing the operational relevance of forecasting models. Beyond exploratory analysis, the study further derives explicit relationships that enable system operators to translate forecasting error metrics into expected grid-state deviations under comparable network conditions.

The heating sector transition represents one of the most critical challenges for achieving climate neutrality, while rising temperatures simultaneously reduce heating demand and generate substantial cooling demand. This study examines the role of reversible heat pumps in municipal energy transitions under climate change conditions using the sector-coupled optimization model DISTRICT with temperature-dependent heat pump performance modeling. Two representative German municipalities are analyzed: a small city with decentralized heating supply and a large city with an existing district heating network, enabling comparative assessment of cost-optimal technology pathways for integrated heating-cooling systems. In decentralized supply areas, reversible heat pumps emerge as the dominant solution by 2040. The levelized costs of cooling and heating analysis is found to range between 0.08 and 0.13 e/kWh for reversible heat pumps, which represent one of the most economical heating and cooling supply technologies in the optimization model from 2035 onwards. In existing district heating areas, the combination of renewable district heating generation with dedicated air conditioning systems turns out to be cost-optimal, with no expansion of district heating networks to new regions. The findings demonstrate that reversible heat pumps can play a central role in simultaneously providing heating and cooling supply, while municipal heating transformation planning should recognize infrastructure-dependent optimal pathways rather than universal technology solutions.

The transition to decarbonized energy systems has fueled a controversial debate over the necessity of traditional “baseload” power. Skepticism remains regarding the reliability and economic feasibility of power systems relying mainly on cheap variable renewable energy (VRE) sources. Addressing this, the German Academies' project “Energy Systems of the Future” (ESYS) analyzed the role of baseload power plants within a decarbonized, continental-scale energy system. Their findings indicate that a secure, net-zero European electricity system is technically robust and economically viable when based on VRE paired with extensive flexibility, storage, and grid interconnections, without requiring new baseload capacity. The integration of new low-carbon baseload technologies, such as nuclear fission or fusion, natural gas with carbon capture and storage (CCS), or geothermal energy, has a marginal impact on overall system costs. While low-cost baseload technologies could be efficiently integrated to achieve high utilization, their future role is contingent on achieving cost reductions beyond current realities.

Growing renewable energy sources in the electric grid require energy-efficient and flexible consumers. Buildings account for a large share of global electricity demand, and their heating, ventilation, and air conditioning (HVAC) systems offer considerable potential for energy savings through improved control, as well as for load shifting due to their large thermal capacity. While model predictive control (MPC) has been widely studied for improving building performance, real-time implementations that quantify both efficiency and flexibility under practical operational and implementation constraints for complex HVAC systems remain largely unexplored. Therefore, this study presents a real-time-capable MPC application using mixed-integer linear programming (MILP) for a complex all-electric HVAC system with thermally activated concrete slabs, evaluated using a validated simulation model. The controller jointly optimizes electricity consumption under varying electricity tariffs while responding to real-time and 24-h ahead grid signals. Results show average electricity savings of 20.4 % compared to the conventional control, with even greater savings during transitional seasons. In addition, the approach improved compliance with temperature limits. Significant flexibility potential was demonstrated, with the 24-h ahead signal increasing average positive flexibility by 38.0 % compared to the real-time signal. However, estimating flexibility costs proved challenging due to the long thermal memory of the slabs and complex heat transfers. Moreover, real-time constraints limited solver convergence, resulting in potentially suboptimal operation. The findings demonstrate the feasibility of real-time MPC for energy-efficient and grid-responsive HVAC operation under real-world constraints, while highlighting the key limitations and trade-offs encountered in achieving this.

Energy system modeling is an essential tool for informing decarbonization strategies. While extensive work has examined the supply side of energy system models, comparatively little attention has been given to the demand side, where societal needs are translated into energy use. Understanding this side is crucial because assumptions about future energy demand strongly influence system size, cost, and feasibility. Therefore, this paper addresses the overlooked demand side by conducting a structured review and comparative analysis of sectoral demand models used in German energy system studies. We focus on the linkage of models, endogenization of behavioral and policy drivers as well as the transparency of models. We find substantial variability in modeling approaches and in how factors such as technology adoption, behavioral dynamics, and policy instruments are represented. Across 29 studies, transparency remains a major limitation: many models are closed-source or poorly documented, restricting reproducibility and critical evaluation. We identify opaqueness in model linkage and whether behavioral and policy elements are treated endogenously or exogenously. We recommend improving openness, documentation, and explicit treatment of socio-political assumptions to strengthen the credibility and policy relevance of demand-side modeling for climate-neutral energy system planning.

Most energy system studies overlook topological sensitivity in demand response validation, risking topology-specific artifacts. Addressing this, we introduce a methodology using minimal example grids to isolate topological effects. Results across two topologies with two load configurations reveal that maximum peak load under feeder-specific scarcity signals differs between 6% and 55%. The results show that conclusions drawn from a single representative grid may not generalize to other topological configurations. As this effect is underrepresented in energy system analysis, this study supports the need for a common framework to evaluate energy system models in respect to topology.

In 2023, Germany's electricity trade balance shifted from net exports to net imports for the first time since 2002, resulting in an increasing discussion of these imports in the public debate. This study discusses different data driven approaches for the analysis of Germany's cross-border trade, with a focus on the methodological challenges to determine the origin of imported electricity within the framework of European electricity market coupling. While scheduled commercial flows from ENTSO-E are often used as indicators, generally these do not correspond to bilateral exchanges between different market actors. In particular, for day-ahead market coupling only net positions have an economically reasonable interpretation, and scheduled commercial exchanges are defined through ex-post algorithmic calculations. Any measure of the origin of electricity imports thus depends on some underlying interpretation and corresponding method, ranging from local flow patterns to correlations in net positions. To illustrate this dependence on methodological choices, we compare different approaches to determine the origin of electricity imports for hourly European power system data for 2024.

Electricity markets are undergoing transformative changes driven by integrating renewable energy and emerging technologies, and evolving market conditions such as shifting demand patterns, regulatory reforms, and increased price volatility. To address the complexity of electricity markets and their interactions, we present ASSUME, an open-source agent-based simulation framework that incorporates multi-agent deep reinforcement learning for modeling adaptive market participants. ASSUME offers a modular architecture for representing generator and demand-side agents, bidding strategies, and diverse market configurations. ASSUME has been proven effective in multiple research studies, demonstrating its ability to analyze complex bids, demand-side flexibility, and other market scenarios. By incorporating adaptive strategies through deep reinforcement learning, ASSUME supports dynamic strategy exploration, enabling a deeper understanding of electricity market behaviors. With its flexible architecture, documentation, tutorials, and broad accessibility, ASSUME ensures usability across different user groups, minimizing technical overhead and freeing up human resources for deeper insights into operational, economic, and policy-related challenges in this critical sector.

Extreme periods in highly renewable energy systems will be shaped as much by internal system interactions as by meteorological anomalies, yet most studies assess them in isolation. This gap is critical for sector-coupled systems, where cross-sector dynamics can trigger stress even under moderate weather conditions. Using six decades of simulations of a net-zero, sector-coupled networked European energy system, we evaluate different stress indicators comparing their ability to identify extreme events against their modeling requirements. System-aware indicators, such as Consumers Cost and Positive Residual Energy Demand, are computationally demanding but reproduce benchmark extremes, defined by Unmet Energy Demand, more consistently. Purely meteorological indicators (Wind Capacity Factors and Heading Demand) are easy to calculate but often highlight less relevant periods. Our classification of historic weather years supports efficient scenario selection and reveals how sector coupling fundamentally reshapes stress patterns.

Electricity forecasting at the household level is vital for optimizing residential energy consumption and empowering grid flexibility provision. At the same time, many residents are reluctant to share their energy data, encouraging local and privacy-preserving model deployment. The growing availability of IoT (Internet of Things) devices in modern residences provides a unique opportunity to tap into the computational potential of these devices and enable advanced data-driven energy optimization. Achieving this, however, required deploying effective yet computationally efficient forecasting pipelines, given the computational constraints faced by HEMS (Home Energy Management System) that control residential devices. These systems typically operate with modest CPUs and minimal RAM, lacking high-performance GPUs or modern multi-core processors, thus presenting a computationally constrained environment. Therefore this study evaluates efficient machine learning algorithms, within computationally restricted environments for key household-level time series applications namely, forecasting heat pump (HP) and household consumption and photovoltaic (PV) power generation. The employed models in this study are XGBoost, CatBoost, LightGBM, Random Forest, Stochastic Gradient Descent (SGD) Regressor, and Kolmogorov-Arnold networks (KAN), for day-ahead forecasting. The models were evaluated based on overall accuracy, computational efficiency, and their ability to predict peak periods accurately, creating a Pareto front of models excelling in precision and runtime speed. Although performance can vary by case, the analysis suggests that the SGD Regressor consistently offers superior speed, KAN generally achieves lower RMSE scores, and tree-based models demonstrate strong performance in capturing peak demand. This study provides practical insights for deploying machine learning models in computationally limited settings, supporting efficient forecasting in residential energy systems with the broader objective of enhancing grid flexibility.

Accurate short-term forecasts are crucial for modern power systems, as renewable energy variability and fluctuating demand challenge grid stability and operations. These forecasts enhance scheduling, congestion management, and market participation but are limited by the absence of open-source, regionalized data. To address this, we integrate global weather prediction data into the open-source Python library atlite, enabling highresolution, multi-day forecasting of renewable generation and temperature-dependent demand. In a case study, we demonstrate this approach by generating an hourly regionalized nowcast for the German power system, estimating its day-ahead flexibility requirements and regional curtailment using publicly available data. Our fully open-source and easily adaptable method ensures compatibility with large-scale energy system models such as PyPSA-Eur, allowing broad applications across Europe and beyond. It marks a first step towards bridging the gap between long-term capacity expansion planning using historical data and the need for real-time operational planning and decision-making.

Achieving carbon neutrality requires defossilizing both the energy system and industry, which are closely linked through shared resources and energy carrier exchange. However, many studies treat them separately, overlooking important feedbacks. This study uses a techno-economic model for German industry to compare coupled and sequential (soft-linked) optimization with the energy system model PyPSA-Eur. Coupled optimization yields similar overall costs (0.3% lower) but different resource use: industry relies more on direct electrification, uses less biomass and hydrogen, and achieves negative emissions ( - 24 Mt CO2), offsetting energy system emissions. In contrast, the soft-linked approach treats sectoral neutrality independently, requiring more costly direct air capture. This study underscores the role of cross-sectoral feedback in resource and emission allocation and hydrogen use and reveals limitations of sequential approaches in representing these feedbacks.

The transition to a climate-neutral energy system demands large-scale renewable generation expansion, which requires substantial amounts of bulk materials like steel, cement, and polymers. The production of these materials represents an additional energy demand for the system, creating an energy-material feedback loop. Current energy system models lack a complete representation of this feedback loop. Material requirements of energy system transformation have been studied in a retrospective approach, not allowing them as a consideration in system design. To address this gap, we integrate bulk material demand and production as endogenous factors into energy system optimization using PyPSA-Eur. Our approach links infrastructure expansion with industrial energy needs to achieve a minimum-cost equilibrium. Applying this model to Germany's transition to climate neutrality by 2045, we find that accounting for material needs increases annual bulk material demands by 3-9 %, shifts preferences from solar to wind and from local production of hydrogen to ship imports, and shows distinct industrial process route choices. These findings suggests that energy-material feedbacks should be considered in energy system design when moving to more domestic production of energy technologies.

To address the challenges posed by increasing shares of variable renewable power generation in the electric grid, flexibility procurement platforms are being actively developed. These platforms enable prosumers to offer flexible power for use in mitigating predicted grid congestion. However, the optimal design of such flexibility markets remains unclear and requires thorough analysis. A critical parameter is the lead time between the acceptance of offered flexible power and its delivery, directly influencing flexibility availability and cost. Despite its importance, the impact of lead time on flexibility provision cost has not been evaluated in the literature. In this study, we analyze this cost effect of varying lead times on flexibility provision by simulating a 48-hour moving horizon model predictive control for multiple distributed energy systems on a market platform, delivering flexibility under different lead time scenarios. Additionally, the deliveries are analyzed under varying demand durations, electricity tariffs, daytimes, and seasons to evaluate their response to diverse influencing factors. The findings are presented using a newly developed flexibility heatmap, illustrating lead time dependent flexibility deliveries and their associated costs. The results indicate that with a lead time of 3h, the cost of providing flexibility using current combined heat and power systems is minimized, achieving cost reductions of up to 77%. Transitioning to advanced heat pumps and battery storage technologies increases the available flexibility ninefold. However, such systems require a lead time of 16h to deliver flexibility at minimized costs, highlighting the growing importance of lead time in flexibility provision.

Electricity price forecasting is crucial for energy market stakeholders, facilitating informed decisions in trading, investment, and operational planning to optimize portfolios and ensure grid stability. Machine learning (ML) models have proven effective for this task, but their deployment often demands substantial computational resources and expertise in both energy markets and ML techniques. Automated Machine Learning (AutoML) addresses these challenges by automating key steps like feature engineering, preprocessing, model selection, and hyperparameter tuning. This study evaluates the performance of three leading AutoML frameworks-Auto-sklearn, TPOT, and FLAML-for day-ahead electricity price forecasting in Germany, focusing on the year 2024. The evaluation includes testing during two distinct periods characterized by high and low price volatility. By incorporating domain-specific features, these frameworks are benchmarked against a Multi-Layer Perceptron (MLP) to assess their viability in comparison to traditional neural networks. The findings aim to provide insights into the scalability and effectiveness of AutoML for complex, data-intensive forecasting tasks, offering practical solutions for navigating the challenges of Germany's dynamic electricity market.

Future energy systems with high shares of variable renewable energy and reduced reliance on conventional power plants will require enhanced flexibility. Small-scale battery storage systems have emerged as a promising solution due to their widespread adoption and significant potential. However, existing methods for assessing flexibility often overestimate flexibility potentials by overlooking grid constraints at the low-voltage level, which can significantly limit availability. This research introduces a novel method to evaluate the flexibility potentials of battery storage systems with high spatial and temporal resolution, accounting for low-voltage grid constraints. Leveraging open-source data for Germany, the study examines regional variations in flexibility potential, considering grid topology, storage capacity, and demand patterns. Findings highlight considerable spatial disparities between urban and rural areas, along with substantial temporal variability. Importantly, low-voltage grid constraints can significantly impact flexibility potentials, reducing them by more than 27 % on average during peak hours in specific network clusters. This emphasizes the critical need to incorporate network limitations into flexibility assessment.

Various factors make electricity markets increasingly complex, making their analysis challenging. This complexity demands advanced analytical tools to manage and understand market dynamics. This paper explores the application of deep reinforcement learning (DRL) and bi-level optimization models to analyze and simulate electricity markets. We introduce a bi-level optimization framework incorporating realistic market constraints, such as non-convex operational characteristics and binary decision variables, to establish an upper-bound benchmark for evaluating the performance of DRL algorithms. The results confirm that DRL methods do not reach the theoretical upper bounds set by the bi-level models, thereby confirming the effectiveness of the proposed model in providing a clear performance target for DRL. This benchmarking approach demonstrates DRL's current capabilities and limitations in complex market environments but also aids in developing more effective DRL strategies by providing clear, quantifiable targets for improvement. The proposed method can also identify the information gap cost since DRL methods operate under more realistic conditions than optimization techniques, given that they don't need to assume complete knowledge about the system. This study thus provides a foundation for future research to enhance market understanding and possibly its efficiency in the face of increasing complexity in the electricity market. Our methodology's effectiveness is further validated through a large-scale case study involving 150 power plants, demonstrating its scalability and applicability to real-world scenarios.

The shift towards a more variable energy production paradigm necessitates further development in the architecture of electricity markets. Current agent-based models, a good tool for evaluating these markets, often overlook the complexity of advanced order types or focus predominantly on optimizing clearing algorithms, thereby missing out on capturing the full breadth of market dynamics. This study fills this gap by examining the effects of incorporating regular block and linked orders on the day-ahead electricity market within an agent-based model. Utilizing the ASSUME framework (Agent-based Simulation for Studying and Understanding Market Evolution), we integrate an iterative mixed-integer linear programming market-clearing algorithm to facilitate regular block and linked orders. To evaluate the real-world applicability of our model, we implement rule-based bidding strategies—incorporating marginal pricing and start-up costs—within the context of Germany's 2020 power mix scenario. Our findings indicate that including regular block and linked orders significantly reduces operational costs, with notable benefits for certain thermal power plants. Furthermore, this integration elevates market clearing prices and influences the dispatch composition, introducing new generation units into the operational mix. This study suggests a positive role of sophisticated order types in electricity markets and their impact on market outcomes, providing valuable insights for the design of future electricity markets.

As power systems transition from controllable fossil fuel plants to variable renewable sources, managing power supply and demand fluctuations becomes increasingly important. Novel approaches are required to balance these fluctuations. The problem of determining the optimal deployment of flexibility options, considering factors such as timing and location, shares similarities with scheduling problems encountered in computer networks. In both cases, the objective is to coordinate various distributed units and manage the flow of either data or power. Among the methods for scheduling and resource allocation in computer networks, stochastic network calculus (SNC) is a promising approach that estimates worst-case guarantees for Quality of Service (QoS) indicators of computer networks, such as delay and backlog. Promising QoS indicators in the power system are given by the amount of stored energy, the serviced demand, and the demand elasticity. In this work, we investigate SNC for its capabilities and limitations to quantify flexibility service guarantees in power systems. We generate and aggregate stochastic envelopes for random processes, which was found useful for modeling flexibility in power systems at multiple time scales. In a case study on the reliability of a solar-powered car charging station, we obtain similar results as from a mixed-integer linear programming problem, which provides confidence that the chosen SNC approach is suitable for modeling power system flexibility.

Industrial demand-side management (DSM) can help maintain grid stability and provide further system services in a highly decarbonized energy system. Energy-intensive industries, such as aluminum, cement, chemical, iron, steel, pulp, and paper producers are well poised to act as the primary providers of demand-side flexibility due to the magnitude of their load and the ongoing trend toward process electrification. Access to these currently untapped flexibility potentials in the German regulatory redispatch can provide significantly more options to relieve grid congestion and to lower curtailed renewable energy. In this work, we analyze and commodify the flexibility potential the German steel industry provides and assess how this flexibility could relieve congestion in the transmission network.