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In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources.

Abstract The online classification of grid disturbances is an important prerequisite for an automated and reliable operation of power transmission systems. Most of the state‐of‐the‐art approaches assume that all classes are already known in the training phase and cannot handle new disturbance events, which appear in the application phase and lead to severe misclassifications. To mitigate this shortcoming, the disturbance detection is investigated as an open classification task and a novel recurrent Siamese neural network architecture is introduced to identify and locate known and unknown disturbance events from phasor measurements. Extending preliminary work, a probabilistic distance‐based classification approach with an integrated rejection mechanism is presented, which enables to learn class‐dependent decision boundaries and margins to reduce the open‐set risk. A detailed performance analysis is presented including multiple benchmark methods in different closed‐set and open‐set classification tasks for a simulated power transmission system. Additionally, a limited and full observability of the grid with phasor measurements are addressed in the experiments.

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

Abstract 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.

The dynamic operation of power transmission systems requires the acquisition of reliable and accurate measurement and state information. The use of TCP/IP-based communication protocols such as IEEE C37.118 or IEC 61850 introduces different gateways to launch cyber-attacks and to compromise major system operation functionalities. Within this study, a combined network intrusion and phasor data anomaly detection system is proposed to enable a secure system operation in the presence of cyber-attacks for dynamic control centers. This includes the utilization of expert-rules, one-class classifiers, as well as recurrent neural networks to monitor different network packet and measurement information. The effectiveness of the proposed network intrusion and phasor data anomaly detection system is shown within a real-time simulation testbed considering multiple operation and cyber-attack conditions.

The scope of this survey is the uncovering of potential critical events from mixed PMU data sets. An unsupervised procedure is introduced with the use of different outlier detection methods. For that, different techniques for signal analysis are used to generate features in time and frequency domain as well as linear and non-linear dimension reduction techniques. That approach enables the exploration of critical grid dynamics in power systems without prior knowledge about existing failure patterns. Furthermore new failure patterns can be extracted for the creation of training data sets used for online detection algorithms.