We continuously strive to advance research in power system simulations. You can find a subset of our insights gathered over the years across different publications.
The integration of renewable energies and sector coupling advances lead to an increasingly complex supply environment in distribution grids. Whereas active grid monitoring and operational control were historically uncommon in distribution grids, their significance is now markedly increasing. Recent research points, among others, to supervised learning-based approaches as an effective solution for distribution system state estimation. A fundamental limitation of those approaches is the reliance on synthetic training data for training and testing. This thesis models a synthetic variant of a real distribution grid and investigates the transferability of a synthetically trained model to the measurement data of the real grid. Real-world measurement data inevitably contains errors and missing signals, presenting a significant challenge for state estimation. Thus, the thesis investigates the detection and reconstruction of anomalies within the measurement data. Furthermore, the limited transparency of machine learning models hinders their adoption in the field. Consequently, an extension of the supervised learning model to a stochastic state estimation that quantifies estimation uncertainty and delivers estimation ranges is proposed.
Distribution grid operation faces new challenges caused by a rising share of renewable energy sources and the introduction of additional types of loads to the grid. With the increasing adoption of distributed generation and emerging prosumer households, Energy Management Systems, which manage and apply flexibility of connected devices, are gaining popularity. While potentially beneficial to grid capacity, strategic energy management also adds to the complexity of distribution grid operation and planning processes. Novel approaches of time-series-based planning likewise face increasingly complex simulation scenarios and rising computational cost. Discrete event modelling helps facilitating simulations of such scenarios by restraining computation to the most relevant points in simulation time. We provide an enhancement of a discrete event distribution grid simulation software that offers fast implementation and testing of energy management algorithms, embedded into a feature-rich simulation environment. Physical models are specified using the Discrete Event System Specification. Furthermore, we contribute a communication protocol that makes use of the discrete event paradigm by only computing flexibility potential when necessary.
The transition to electric vehicles poses challenges for power system planning, particularly in distribution grids. This paper introduces a novel method for simulating electric vehicle driving trips at scale to model realistic driving behaviours. By incorporating empirical mobility patterns into the simulation environment, the approach enables detailed analysis of temporal and spatial charging demand. Integration with the SIMONA framework facilitates co-simulation with the electric power system, allowing power flow calculations and load management strategies. Validation against real-world data demonstrates high accuracy in predicting parking duration across various states while identifying deviations affecting energy consumption profiles. An exemplary scenario highlights significant grid impacts from electric vehicle charging infrastructure. The presented open-source MobilitySimulator supports forecasting charging demands, optimizing grid investments, and evaluating smart charging strategies to mitigate negative grid effects and enhance renewable integration. This scalable methodology advances research on grid interactions with electric vehicles and sustainable power systems planning.
The increasing amount of distributed energy generation and controllable loads is changing the use of distribution grids, which potentially results in grid congestions and necessitates grid expansion. But this transformation creates also flexibility, that can avoid grid congestions through the use of energy management systems. To integrate comprehensive grid models, fast simulation capabilities, and flexible incorporation of new methods, we present a two-stage hierarchical energy management system using an existing discrete-event distribution grid simulation within a co-simulation framework. In the bottom layer local energy management units control connected PV plants, loads and battery storages. Flexibility options are aggregated from the underlying assets and passed to a central energy management unit. The collected flexibility options are allocated by this entity to mitigate grid congestions while the individual behaviour of local energy management units is accounted for.
High power charging of up to one megawatt for electric heavy goods vehicles is a challenge for the electrical grid and the grid operators in planning adequate solutions. We present a method that is capable of determining the power demand for charging infrastructure on motorways in Europe. This enables grid operators to analyse different scenarios of future charging demand on motorways. The charging demand itself is derived by a co-simulation approach that combines the simulation heavy good vehicle traffic and the simulation of the energy system. This paper focus on the part of determining the power demand of heavy goods vehicles. A case study shows the application of the approach and exemplary results for some service areas on motorways.
The integration of large-scale heavy-duty electric vehicle (HDEV) charging infrastructure presents significant challenges for existing power distribution grids. This paper evaluates the grid impact of megawatt-level HDEV charging using a coupled simulation approach. A detailed mobility model from previous work simulates HDEV arrivals, parking durations, and energy requirements at highway rest areas over a year, generating realistic charging profiles. These profiles, serve as input for quasi-static time series power system simulations. The analysis employs SimBench benchmark distribution grids (Rural, Semi-Urban, Commercial, Urban). Three distinct scenarios for connecting the HDEV charging infrastructure are investigated across 36 combined simulation cases. The paper focuses on the impact on transformer utilization, line utilization, and voltage magnitudes, comparing scenarios with and without HDEV charging. Results indicate that while transformer capacity appears generally sufficient to accommodate the additional load, line utilization emerges as a critical bottleneck. The findings highlight that significant grid reinforcement, primarily upgrading distribution line capacities, will be essential for the widespread deployment of HDEV charging.
The ongoing energy transition has led to a paradigm shift in distribution power systems infrastructure and operations owing to the ever-increasing volume of intermittent renewable energy generation and electricity consumption across several recently developed sectors. Integrating previously separated energy sectors and electricity market liberalization transforms the distribution system environment into a complex system that includes several participants with conflicting interests. Therefore, innovative approaches to optimal and efficient energy management of available resources are paramount to achieving sustainability goals through economic, environmental, social, and technical factors. This dissertation presents an agent-based hierarchical energy management architecture that draws from research and practically applies to the liberalized electricity market. The architecture is designed to solve a multi-objective optimization routine for distribution grid operation in a distributed way. An online feedback mechanism is a vital feature of the optimization algorithm, which is solved in collaboration with the agents representing the various participants in a distribution grid ecosystem. A unique combination of Tchebycheff's decomposition and the Gradient projection method is used to solve the constrained and multi-objective optimal power flow problem. Adding penalty functions to the objective function effectively handles the system constraints. The formulation of industrial and residential demand profiles incorporates socio-behavioral aspects of the connected prosumers, reflecting their economic, behavioral, or environmental goals. The energy management architecture and its optimization function are then applied in a co-simulation framework to study the impact of industrial and residential flexibility on the connected distribution grid's economic, environmental, and technical aspects. The results provide valuable indicators for distribution grid planning and operation under future renewable generation and flexible load integration scenarios.
The ongoing integration of DER in combination with the ramp-up of assets with flexibility potential such as storages and electric vehicles is challenging grid planning processes. The flexibility potential of such assets varies according to the flexibility strategies of energy management systems and cannot be addressed by conventional grid development and planning approaches. We present a methodology to determine the simultaneity factor of households with energy management systems that can be included in existing grid planning approaches used to evaluate the hosting capacity in distribution grids.
In recent decades, distribution system restructuring has led to a fundamental shift towards distributed and integrated energy systems. Moreover, electricity market privatization requires distributed control of connected resources to help maintain data privacy. In future distribution systems, each participant will control its local resources optimally while supporting the efficient operation of the power distribution grid by adhering to its system and balance constraints. Consequently, this article proposes an agent-based, integrated, and hierarchical energy management framework for the distribution system operators and involved participants such as industrial or residential prosumers.
The goal of such a framework is to optimize the desired targets of each participant locally while maintaining the system security of the distribution grid. The developed framework uses a combination of MATLAB and AKKA to construct the agent-based and hierarchical energy management structure. Finally, we use a distributed optimization routine based on a gradient-descent method to showcase the framework’s operation.
In the energy transition context, the use of steady state time series is a promising approach to account for temporal interdependencies and flexibilities in modern distribution power system analysis, planning, and operation processes. This paper proposes a distributed backward-forward sweep power flow algorithm executed in a discrete-event, agent-based simulation framework. The algorithm shows a fast convergence, allows for concurrent execution and, scales up to large-scale multi-voltage level grids with arbitrary topology. An agent-based simulation model integrates the developed algorithm to generate detailed grid utilization, asset, and system participant time series.We demonstrate the capabilities of our approach by performing several simulations, leveraging the proposed algorithm, on nine different benchmark grid models. The selected models comprise single voltage and medium voltage levels as well as combined multi-voltage level grids. The evaluation of the numerical results.
The ongoing transformation of the entire power system with a simultaneous convergence of different, previously separated energy sectors poses new challenges, especially for the energy transport infrastructure at the distribution level. Due to its important role for society, careful planning and stable, secure operation of this infrastructure are essential. Accordingly, the introduction of necessary innovative operational concepts or the exploration of new planning approaches in the context of the energy transition cannot take place in the real system.
One possibility to evaluate new, innovative methods without endangering the real system is simulation. However, the complexity of the electrical infrastructure at the distribution level, the large number of heterogeneous technical systems and the influence of individual, human-centric behavior pose several challenges for existing approaches.
In the context of the present work, an agent-based modeling approach is combined with a discrete-event simulation approach to form the agent-based discrete-event simulation model SIMONA. Based on experiences gained with existing preliminary work, an existing agent-based model is revised, remodeled, and implemented from scratch, discrete-event simulation logic is introduced, and further necessary adjustments are made. The new model enables large-scale simulations of the electrical distribution grid while considering individual behavior of connected grid assets, innovative grid operation concepts and flexible system participant behavior.
Human live is nearly impossible without an energy system. A malfunction poses severe risks to essential services like water and food supply. Every adaption needs accreditation before it is applied. Obviously, practical experimentation is not the method of choice. Modeling and simulation, which is experimenting with a virtual copy, are established alternatives. However, steadily increasing complexity and heterogeneity challenge known approaches.
This thesis contributes to efficient, practical and validated modeling and simulation of an increasingly complex and interdisciplinary energy system. The contribution is twofold and made by further development of the simulation framework SIMONA: A generic system participant model, based on agent theory and discrete event simulation, eases the modeling process. Moreover, it allows for an efficient simulation. It enables representation of individuality and rationality of a huge number of participants and therefore provides means to examine their collective behavior. Secondly, equivalent and validated transformer models provide efficient coupling of partial models for grid levels within the decomposition principle applied by SIMONA.
With the expansion of renewable energies, more grid transparency is necessary in order to continue to guarantee a stable grid operation. In transmission grids, state estimation has been successfully used to estimate the grid state based on available measurement data. However, distribution grids are not completely permeated with sensor technology, primarily due to historical and cost reasons. Installing sensor technology at each node to be observed is economically not feasible, which makes it difficult to impossible to transfer state estimation technologies to the distribution grids. Therefore, an intelligent solution approach to create transparency is needed. In this paper, we analyze the pros and cons of existing approaches for distribution system state estimation. We also show how Artificial Neural Networks have
been applied for state estimation and propose an approach that combines proven solutions with Transfer Learning to make this Neural State Estimation applicable to any distribution grid.
In recent years, the distribution grid planning process has faced the big challenge to integrate renewable energy sources in its planning methodology while preserving a secure and stable provision of electricity. With the currently observable efforts to electrify human mobility all around the world, another new challenge arises for the planning and operation of distribution grids. To address these challenges and to leverage the opportunities that are accompanied by them, new methods for the planning of distribution grids as well as planning decision-supportive approaches and algorithms are needed. The presented approach contributes to the described demands by means of a coupled approach, using both distribution grid time series as well as a genetic algorithm to support decision making in the planning process considering not only new assets for grid reinforcements and extensions but also smart-grid and operational opportunities.
Academic studies and long-term planning demand for highly sophisticated simulation of distribution system’s usage considering operational actions and repercussions of market driven measures when applied on a large scale. This paper presents enhancements to the SIMONA tool enabling a large-scale distribution system simulation of a lifelike 50,000 nodes model.
The research presented in the paper was part of the research project “Agent.GridPlan”. SIMONA was foreseen to be used as an evaluation tool for a genetic optimizer, that was used to find the best voltage level integrated grid expansion measures. It was the first time, SIMONA has proven it’s large scale applicability. Alongside of handling the sole computational burden, several model adjustments and enhancements had to be made: Tapable three winding transformer support, wide area transformer control systems as well as improvements in connection of consecutive time steps in power flow calculation
With the expansion of distributed generation and innovative loads being connected on a low to high voltage level, the supply task for the distribution grid becomes increasingly volatile, which requires an adequate consideration in the planning process. However, since the primary objective of the distribution grid was the supply of loads in the past, the conventional planning process was simplified to dimensioning cases and the individual voltage levels were planned separately. Therefore, innovative and interacting network participants can only be accounted for with a basic estimation and the interdependency of voltage levels is neglected.
Facing these challenges, a simulation framework based on the concept of multi-agent systems is developed in this thesis to determine the time dependent behaviour of all network participants and enabling the modelling of innovative market incentive concepts. A single agent, with individual objective functions and environmental conditions, represents every grid user. The resulting time series constitute a profound basis for a demand oriented distribution grid planning process, considering the probability of occurrence of network-loading situations.
The recent changes and developments in the electric power system impose new challenges to the distribution system operators not only in the operation, but also in the planning of the grids. The volatile feed-in of distributed generation based on renewable energy sources as well as new and intelligent loads require an appropriate consideration in the distribution grid planning process. With the conventional planning method being dependent on extreme scenarios, the consideration is very limited. Therefore, a new simulation system based on the concept of a multi-agent system is developed and presented in this thesis, permitting not only the consideration of the volatile feed-in characteristics of renewable energy sources but also of the dependencies between the grid users and their environment. Every grid user is modelled as an agent of its own, guaranteeing the preservation of its individual character. The results of the simulation, time series for all relevant system variables, define the new input parameters in the distribution grid planning process. The probabilities of occurrence of loading situations can be derived from the time series. This allows for the first time for a detailed determination of the conditions in the up to now rarely measured medium and low voltage grids. As a consequence, new assumptions for the planning process are derivable, permitting a demand- and futureoriented grid planning and avoiding overdimensioning of the grids.
Science for a greener tomorrow.
