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The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error‐prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention‐based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double‐sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open‐set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state‐of‐the‐art open‐set classifier.

Increasing digitization in station automation enables new applications for intelligent functionalities in substations of electrical power systems. The use of IEC 61850 as a uniform communication standard is an important requirement in this context, but also changes the vulnerability to cyber-attacks. Real-time cyber-physical cosimulation platforms are necessary to investigate cyber security threats and countermeasures in digital substations. Essential to the real-time capability and validity of the co-simulation is the compliance with requirements of IEC 61850 - especially the arising transfer times between the substation components. We present here a queue-based delay model for the used Software-Defined-Network architecture to derive a maximum permissible size of the virtual network. This model can capture various influences during the information exchange between OpenFlow-based switches and a centrally hosted controller. The dependency of the transfer time on the number of switches, considered data rates and handover probabilities between controller and switch limits the network size of the virtualization. We present here the adaption of well-known queueing delay models to our IEC 61850 substation use case and their usage in presented co-simulation approach, by taking the network topology, estimated network traffic and simplifications for single-controller architecture with uniformed Open-Flow Switches into account. In this context, the presented architecture of the co-simulation has a structurally limited area of validity, which is introduced and described here analytically. The work also complements already existing delay models by an input prioritization of packet-in events. We can approximate a theoretical limit of 16 sampled values streams in the current setup of co-simulation in a simple and completely interconnected star topology, which will have to highest impact on the transfer time delay.

The automated classification of grid disturbances based on phasor measurements is a key technology for the reliable operation of power transmission systems. The predominant use of simulated training data limits the applicability of existing classification approaches due to the missing consideration of measurement errors or data quality issues. To mitigate these shortcomings, this study presents a robust disturbance classification procedure incorporating denoising recurrent autoencoders within a novel two-stage training approach. The developed disturbance classification procedure is evaluated for different noise characteristics and dataset combinations created with an optimization based error model. Experimental results based on a generic power transmission system show superior performance of the proposed two-stage design compared to a conventional, one-stage model training.

The interaction between communication technology and the power grids is a major issue in research studies to investigate the effect of cyberattacks, communication failures and other errors to the physical behavior of the power grid. Especially the interconnected digitalized substations of the modern power grids are of interest due to their high-risk potential and dependence of communication technology. Disturbances and negative influences on these key control entities can affect the entire underlying power supply as a critical infrastructure. Recent publications and research projects are trying to investigate the effects by using simulation tools within co-simulation approaches. In this paper, we present a novel approach using a conventional real-time power grid simulation and to combine it with a container-based virtualization of the communication infrastructure. The communication infrastructure contains all the important and necessary components, hosts, devices, and communication aspects to set the network traffic and interactivity of substation automation setups regarding the IEC 61850 standard. This paper will show the feasibility of combining online real-time grid simulation to a container-based virtualization on the process level. The evaluation is done by analyzing occurred time delays and latency about requirements given by IEC 61850 standard. A validation against these transmission time requirements is presented to provide an indication of the feasibility of our approach.

The automated classification of grid disturbances based on phasor measurement units (PMU) is a key application for a fast and reliable monitoring and control of future power systems. The predominant use of dynamic simulations for the training of the classification models can lead to severe misclassifications during the application phase due to measurement induced error signals. As an advancement to standard white noise approaches, an optimization-based error model is introduced for the synthesis of PMU measurement signals with specific noise characteristics. This approach allows a flexible creation of more sophisticated error signals. Extensive simulation studies are performed for a disturbance classification model based on a recurrent neural network using a large electrical transmission grid.

Power Hardware-in-the-Loop (PHiL) becomes more and more an important part at the investigation of dynamics and stability issues of the electrical grids. As derived from Hardware-in-the-Loop (HiL) approach, one of the central PHiL requirements is to feedback measurement signals into the simulation. In PHIL applications, the operational behavior of a connected device is integrated within real-time simulations. With the integration of the Hardware under Test (HuT) into a PHiL test bench, stability issues may arise. Therefore, this paper focuses on the stability investigation and the description of typical PHiL setup and condititions. The frequency domain modelling reveals phenomenon of instability as well as concepts of Interface Algorithm (IA) and Compensation Methods (CM). To investigate the behavior of different devices as HuT, also the HuT is modelled and simulated in the real time environment. In the next step, the analog loopback integrates HuT models into PHiL simulation by using HYPERSIM from OPAL-RT as simulation software (time domain). Analog loopback introduces time delay into the PHiL simulation that would also occur in real PHiL applications with connected devices to build a more realistic environment. The simulation with analog loopback consists of implemented IAs which feedback operational behavior of HuT models into PHiL test bench. In selected simulation approaches, the mentioned technique loops back currents of various loads and inverters. Therefore it is possible to evaluate the feedback of devices with different described methods and their impact on stability and signal fidelity.

In the course of digitalization various technologies which could deeply transform present processes are put forth. As such an innovation, blockchain is attributed to have potential to make energy industry procedures more efficient and to simplify them. The aim of this paper is to examine the applicability of this technology using the change of power supplier as an example. Based on Ethereum a proper blockchain solution for the use-case is developed using smart contracts to handle all processes of the example. Beside qualitative evaluation of manipulation security and privacy, a test scenario is built to measure the speed in which the developed solution allows supplier changes.

The state of the art in science is to take into account forecast uncertainties in optimization either in the form of variant calculations or in stochastic optimization models. This paper presents an alternative approach for the consideration of forecast uncertainties within model predictive control. The approach defines a guaranteed flexibility range, which is responsible for the compensation of expected forecast errors. It is proven that the risk can be reduced with the help of quantiles based flexibility bands. To implement the approach, a controllable resource is required in the energy system. In this case it is an energy storage device. The result shows that with the help of the new approach it is possible to minimize the forecast uncertainties in the local energy system and thus reduce the impact on the overall system. With this algorithmic approach, the planning reliability in the overall energy system is to be increased.

The scope of this survey is the phasor based event detection under cyber security constraints in modern transmission control centers. A general concept for a physical and cyber event detection is developed as a combination of state-of-the-art disturbance classification and cyber-attack detection methods. This requires the incorporation of heterogeneous data sources and advanced data fusion and analysis techniques. An enhanced and robust recognition of the current grid situation is proposed to distinguish between different scheduled and unscheduled grid events. Furthermore, Digital Twins are considered as new promising technology for control centers and potential benefits for physical and cyber event detection are described.

Due to increasing contribution of renewable energy sources to the overall energy production, future control of energy consumption should achieve the principle 'generation following demand'. Within this approach, also known as demand side management, consumers are prompted to alter demands through an incentive signal. This work addresses the use of variable price as such incentive signal to influence power consumption of private households in a controllable manner. The mathematical modelling is based on simulations of optimal power flow and includes several optimisation constraints and criteria, according to which both direct and inverse models are constructed. Therefore, the problem of power signals and price signals is considered as fully-coupled. The analysis is performed for a model, representing typical conditions and scenarios of a residential network with household customers, including solar and wind power plants. The optimisation criteria are constructed with the emphasis on distribution system operators. The results of computations, performed with simplified and extended optimisation models, show a pronounced effect on the overall power consumption, reduction of the extreme values and minimisation of the peaks. Finally, further steps are proposed to extend the model to target various optimisation constraints.

Climate change and limited availability of fossil fuel reserves stress both the importance of deploying renewable energy sources (RES) for electricity generation and the need for a stronger integration of regional electricity markets. This analysis focuses on North African (NA) countries, which possess vast resources of renewable energy but whose electricity supply is still largely dependent on fossil fuels. An analysis of cost-optimized deployment scenarios for RES is conducted in five NA countries in 2030 and 2050. Three electricity models are combined to derive results covering trans-regional to sub-national level, including a detailed analysis of grid capacities and future transmission challenges. Further, opportunities for integration of European and NA electricity markets are evaluated. Results confirm that, by 2050, high RES shares - close to 100% - are possible in NA. Wind energy is the dominant technology. Concentrated Solar Power (CSP) plants also play an important role with rising RES shares due to the possibility to store thermal energy. Electricity exports to Europe gain particular importance in the period after 2030. Substantial transmission grid reinforcements on AC-level and the construction of a high voltage DC overlay grid are prerequisites for the forecasted scenarios.

More and more active components, i.e. renewable generators, controllable loads, and storages, were integrated to low-voltage distribution grids. They were often interfaced with power electronics and their active participation in the power system may lead to voltage instabilities and power quality problems. Therefore, one research topic was to analyse the dynamic interactions between these active components, and to develop appropriate control architecture and techniques, which are able to avoid the corresponding instabilities and improve the power quality in the low voltage grids. One challenge of such dynamic simulation was to simulate the interacted system with time resolution of millisecond and to ensure the synchronization between them. In this paper, the suitable dynamic models of single-phase power electronic load and three-phase power electronic generator were conducted. These dynamic models were simulated and validated by the voltage-step-response, respectively. The voltage unbalance was used to quantify the interactions between the dynamic models and grid. In the steady state, the power electronic load induced less voltage unbalance comparing to the conventional passive load, because the power electronic load had better power factor. In certain dynamic processes, with the higher penetration of PE components in the LV grid, we observed the significant increase of voltage unbalance, which was induced by the interactions between the controllers of the active components. Such effects should be considered in the design of low-voltage gird system and its components.

The energy transition main targets are increase of energy efficiency and promotion of renewable technologies, hence the number of components with power electronics is growing in the electrical system. The research project VEREDELE examines these effects and resulting interactions in order to identify possible challenges and develop suitable solutions. Especially active components, such as electric vehicles, pose major challenges for the modelling and simulation of these devices. The Power-Hardware-in-the-Loop (PHIL) test bed that built up is a solution to this problem and allows the integration of real devices (loads and feeds) into a grid simulation. A model library developed for system tests, which makes it possible to generate any system states to monitor the real behaviour of the equipment and to conclude on potential critical grid states. Furthermore, the characteristics of the signal source of the simulation system examined in this paper and an evaluation of the usability of the system for the intended investigations is given.

The scope of this survey is the automated extraction of critical events from mixed PMU data sets without prior knowledge about existing failure patterns. An unsupervised procedure is introduced that identifies arbitrary disturbance types (e.g. voltage sags, frequency drops, oscillations) using an ensemble outlier detection approach. For that, different techniques for signal analysis are used to generate features in time and frequency domain. That approach enables the exploration of critical grid dynamics in power systems. Furthermore new failure patterns can be extracted for the creation of training datasets used for online detection algorithms.

The scope of this survey is the compression of PMU data by eliminating redundant information with statistical methods. With that the storage demand in wide area monitoring systems for archiving massive phasor data can be significantly reduced and enables the efficient integration of PMU based applications in modern control centres. Within this paper a general concept is introduced for data reduction in space and time dimension combining different techniques from dimensionality reduction. For this purpose several unsupervised statistical learning methods (e.g. PCA, ICA, NMF) are tested using a real PMU measurement dataset.

Within the venture “REGEES” (REGenerative rEnewable Electricity System) a new approach for a Coordinated Market Grid Operation Management (CMGOM) was developed. It envisages the extension of the balancing process of Balance Responsible Party (BRP) including information of current or estimated grid situation. This paper discusses two different approaches to calculate the restrictions for BRPs by the DSO and the resultant consequences for BRPs optimization. The approaches are applied and assessed in test cases that show their benefits and disadvantages.

An approach for an optimization problem to combine acquisition of a balancing group and grid restrictions given by distribution system operators is introduced in this paper. It is embedded into an extended scheduling system which enables communication between balance responsible parties and system operators. As the optimization problem should be used inside a DayAhead or IntraDay process it was formulated with linear restrictions and with a linear objective function to keep a short computation time. The optimization problem is very modular, hence it can be used for different use cases and is of course easily expandable. We give a proof of concept for the optimization problem and discuss advantages and disadvantages.

Zusammenfassung Durch die Beeinflussung des elektrischen Verbrauchs mittels Preiszeitreihen (Demand Response) können zusätzliche Flexibilitätspotentiale für die Energieversorgung erschlossen werden. Durch die Einführung von Demand Response ergeben sich jedoch neue Anforderungen an die Lastprognose. Dieser Beitrag befasst sich mit der Modellierung von preisbeeinflusstem Verbrauchsverhalten (PVV) und stellt einen neuen Prognoseansatz vor.