We continously strive to learn new lessons. You can find a subset of our insights gathered over the years accross different publications.
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
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.
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.
