Simulating energy systems integration scenarios enables a comprehensive consideration of interdependencies between multimodal energy grids. It is an important part of the planning for the redesign of the current energy system infrastructure, which is essential for the foreseen drastic reduction of carbon emissions. In contrast to the complex implementation of monolithic simulation architectures, emerging distributed co-simulation technologies enable the combination of several existing single-domain simulations into one large energy systems integration simulation. Accompanying disadvantages of coupling simulators have to be minimized by an appropriate co-simulation architecture. Hence, in the present paper, a new simulation architecture for energy systems integration co-simulation is introduced, which enables an easy and fast handling of the therefore required simulation setup. The performance of the new distributed co-simulation architecture for energy systems integration is shown by a campus grid scenario with a focus on the effects of power to gas and the reversal process onto the electricity grid. The implemented control strategy enables a successful co-simulation of electrolysis coupled with photovoltaics, a hydrogen storage with a combined heat and power plant and a variable power consumption.
The integration of variable and unpredictable renewable energy sources into the current power networks introduces considerable changes in system operations. This poses enormous threats to the stability of the power system. Hence, it is essential to analyse the necessary adjustments in operation strategies in preparation for increased amounts of variable generation in existing power systems. The present study describes the dynamic modelling and integration of solar photovoltaic and wind power generation systems into a transient stability analysis toolbox. In view of the inherent connection of renewable energy generators to the electrical network through converter systems, the main contribution in the present study is the development of high‐level control functions to model converter interfaces with reference to standard grid operation codes. The dynamic models and corresponding control functions are tested using a network representing the transmission grid of the Baden‐Württemberg state in Germany as part of the assessment process to analyse the capability of the control functions for grid stability support. The simulation results show that the proposed converter control functions can equip renewable energy generators with equivalent features from a functional point of view to those of synchronous generators.
The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. Parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF‐problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.
In the present paper, we introduce the Smart Energy System Control Laboratory (SESCL) as a fully-automated and user-oriented research infrastructure for controlling and operating smart energy systems in the context of a microgrid-under-test setting. SESCL’s high level of automation and capacity to fully function in a grid-decoupled way allow for the study and evaluation of yet-to-be-developed tools and algorithms for energy technologies and grid control strategies on the edge of system stability, but in a safe environment. In the context of various European Smart Grid Laboratories, the new concept and specifications of SESCL are outlined in depth. The key advantages of SESCL are highlighted as (i) the provisioning of a fully-automated busbar matrix to provide a very flexible and adjustable microgrid topology; (ii) the capability of load shedding or integration of grid participants, as well as changing the microgrid topology on demand; (iii) and the possibility to control and modify setpoints and operating parameters of grid participants during runtime. Inspired by real-world events in island grids, the islanding of a microgrid is utilized as a use case to illustrate the capabilities of the SESCL research infrastructure.
This paper presents an extendable Matlab-based phasortime domain toolbox for modeling, simulation and analysis of unsymmetrical power system transients in large networks. Unlike most of the existing transient stability simulators which represent the transmission network on a per phase positive sequence basis, the new simulation function introduced in this paper is based on the symmetrical component technique which employs the three sequence networks. This representation allows consideration of network imbalances in order to include a wide range of disturbances during transient stability studies. The main aim of this paper is to describe the model details of the power system components required for unsymmetrical transients analysis and the solution methodology in the introduced simulation function. The performance of the simulation function is tested using standard IEEE test network models and the promising results are positively compared to respective results in DIgSILENT PowerFactory in terms of accuracy.
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