Edge intelligence, i.e., the execution of Machine Learning (ML) algorithms in computing resources at the edge, provides unprecedented benefits for applications in different verticals regarding data privacy, bandwidth, costs, and latency. Non-Intrusive Load Monitoring (NILM) is an application in the smart grid technology domain that could benefit from the advancements in edge intelligence to ensure consumer data privacy and decrease implementation costs. This paper proposes a federated learning-based transformer architecture for the NILM of energy-intensive residential devices, i.e., Heat Pumps (HP). We evaluate the architecture on an open-source dataset, showcasing that the performance does not deteriorate significantly compared to the centralized and is robust against the distribution shifts between the training and inference datasets and the increasing heterogeneity level between clients in the training data.

The purpose of this longitudinal study was to examine the development of spelling in a large sample (N = 503, boys: N = 219) of Greek-speaking children with (N = 41) and without (N = 462) reading difficulties. Children were initially tested in Grades 2–4 and then at five consecutive measurement points over a 3-year period, focusing on how initial reading ability, grade, and gender may moderate the rate of spelling growth. Individual growth curve modeling revealed continuous growth of spelling performance in the total sample, although the growth rate decreased over time for children first tested in Grades 3–4. Spelling growth rate was also significantly slower among children with reading difficulties between Grades 2–4 and 3–5. The two reading groups displayed similar growth rates between Grades 4 and 6. Spelling growth rates did not vary significantly with gender. Overall, our study highlights the persistence of spelling difficulties even after 6 years of systematic teaching in children with reading difficulties. The severe and persistent spelling deficits of Greek-speaking children with reading difficulties may be attributed to the rich morphological system of the Greek language, the intermediate Greek orthographic transparency (in the direction of writing), and their limited experience with print.

This paper presents an integrated methodology for decision making in smart grid investments that assesses the investment plans of stakeholders in local energy communities (LECs). Considering the energy flow exchanges of the LECs and interpreting them in terms of technical benefits and costs, this methodology indicates the most sustainable and profitable solution covering the LEC energy transition plans. A set of specialized tools capturing the energy, environmental, financial, and social impacts are integrated under a common platform called the IANOS Energy Planning and Transition (IEPT) suite. The tools evaluate a set of well-defined key performance indicators that are gathered using a cost–benefit analysis (CBA) module offering multilateral assessment. By upgrading the functionalities of specialized tools, i.e., the energy modeler INTEMA, the life cycle assessment and costing tool VERIFY, and the smart grid-oriented CBA tool, the IEPT suite evaluates the viability of different smart grid investment scenarios from a multi-dimensional perspective at the LEC level. The functionalities of the proposed suite are validated in the LEC of Nisyros island, Greece, where three smart grid-based investment scenarios of different self-consumption levels are evaluated and ranked in terms of benefits and profitability. The results highlight that for a 20-year horizon of analysis, the investment scenario where a 50% self-consumption target is achieved was more financially viable compared to the 80% and 95% scenarios, achieving values of BCR and NPV equal to EUR 2.12 and EUR 4,400,000, respectively.

The high penetration of distributed energy resources, especially weather-dependent sources, even at the edge of the distribution grids, has increased the power system uncertainties and drastically shifted the operational status quo for the system operators. For the operators to ensure the uninterrupted electricity supply of the end-consumers, the fast and accurate response to fault events is of critical importance. This paper proposes a data-driven fault location identification and types classification application based on the continuous wavelet transformation and convolutional neural networks optimally configured through Bayesian optimization. This application leverages the proliferation of high-resolution measurement devices in distribution networks. It can locate the exact position of the short-circuit faults and classify them into eleven different types. Its intrinsic models grasp the spatial characteristics and the converted in frequency domain temporal ones of the three-phase voltage and current timeseries measurements stemming from the field devices, thus increasing the operators’ visibility of their networks in real-time. We conduct simulations through synthetic data, which we provide in an open-source repository, that replicate a wide range of fault occurrence scenarios with eleven different types, with the resistance ranging from 50Ω to 2kΩ and with duration from 20ms to approximately 2s, under noise conditions injected by devices and load variability. The results showcase the efficacy of the proposed method reaching an accuracy of 91.4% for fault detection, 93.77% for correct branch identification, 94.93% for fault type classification, and RMSE value of 2.45% for location calculation.

Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

Both FARCROSS and TRINITY EU research projects aim to increase cross-border electricity flow and regional cooperation. The integration of SmartValve and T-SENTINEL systems offers benefits such as enhancing grid security and reliability, managing thermal constraints, and maximizing utilization of existing infrastructure. The combined system can achieve a more efficient and less costly coordinated network security process, increase cross-border capacities, and promote regional electricity market integration, benefiting the local communities with significant CO2 emissions avoidance and reduced electricity prices. Overall, the integration of SmartValve and T-SENTINEL can provide significant improvements in flexibility, making cross-border connections more robust and adaptive to the evolution of the electrical power industry.

The high penetration rate of distributed energy resources even at the edge of the distribution grids drastically changes the operational status quo, mainly caused due to their high intermittent nature. In order to ensure the uninterrupted electricity supply of the end-consumers, the fast and accurate response to fault events is of critical importance for the operators. This paper proposes a data-driven fault location identification and type classification application based on the ConvLSTM models, which leverages the proliferation of advanced measurement devices in the distribution networks and can locate the exact position of the fault and classify it in eleven different types. These models grasp the spatiotemporal characteristics of the three-phase voltage and current timeseries measurements stemming from the field devices, increasing the visibility of the operators for their networks in real-time conditions. The results conducted through the use of synthetic data showcase the efficacy of this application with accuracy in faulty feeder detection reaching 96% and in the fault type exceeding 88%.

The massive penetration of Renewables into the energy mix and the existence of IoT-enabled Distributed Energy Resources (DERs) in the emerging smart grid, while a blessing towards de-carbonization, increase considerably the operations and planning functions of the grid. Cloud processing of IoT/DER data facilitates the deployment of various Demand-Response (DR) and other DER asset scenarios, the organization of distribution grids into Local Energy Markets (LEM) and the efficient computation of load forecasting and power flow. Cloud computing enables a plethora of service provisions to the grid including frequency response. The decentralized nature of DERs at the edge of the distribution grid requires nodal approaches for the computation of power grid congestion constraints and power flow solutions. We present here a cloud-edge continuum approach, anchored on the new generation of communications infrastructure, which expedites the computation time of the load and DER forecasting and optimal power flow calculations. The proposed approach allows the LEM operator to respond to Fast Frequency Response service procurement signals issued by the balancing authority requiring even sub-second latency for service settlement. The proposed cloud-edge architecture has been tested on the IEEE European Low Voltage Benchmark model and provides scalability and elasticity for various DR/DER configurations.

Based on the original framework of the Smart5Grid EU-funded project, the present paper examines some fundamental features of the related platform that can be able to affect 5G implementation as well as the intended NetApps. Thus we examine: (i) the specific context of smart energy grids, enhanced by the inclusion of ICT and also supported by 5G connectivity; (ii) the cloud native context, together with the example of the cloud native VNF modelling, and; (iii) the MEC context as a 5G enabler for integrating management, control and orchestration processes. Each one is assessed compared to the state of the design and the implementation of the Smart5Grid platform. As a step further, we propose a preliminary framework for the definition of the NetApps, following to the way how the previous essential features are specifically incorporated within the project processes.

As the complexity of electric systems increases, so does the required effort for the monitoring and management of grid operations. To solve grid performance issues, smart grids require the exchange of higher volumes of data, high availability of the telecommunication infrastructure, and very low latency. The fifth generation (5G) mobile network seems to be the most promising technology to support such requirements, allowing utilities to have dedicated virtual slices of network resources to maximize the service availability in case of network congestions. Regarding this evolving scenario, this work presents the Smart5Grid project vision on how 5G can support the energy vertical industry for the fast deployment of innovative digital services. Specifically, this work introduces the concept of network applications (NetApps), a new paradigm of virtualization that are envisioned to facilitate the creation of a new market for information technology (IT), small and medium enterprises (SMEs), and startups. This concept, and the open architecture that facilitates its implementation, is showcased by four real-life 5G-enabled demonstrators: (1) automatic fault detection in a medium voltage (MV) grid in Italy, (2) real-time safety monitoring for operators in high voltage (HV) substations in Spain, (3) remote distributed energy resources (DER) monitoring in Bulgaria, and (4) wide area monitoring in a cross-border scenario between Greece and Bulgaria.

This paper primarily aims to demonstrate how TSO, and DSO shall act in a coordinated manner to procure, and activate innovative flexibility services in a reliable, and efficient way. The objective of this paper is to demonstrate the financial, and system reliability benefits the TSO can exhibit by leveraging flexibility, under different coordination frameworks between the operators, in order to alleviate overvoltages caused by Ferranti effect. For this purpose, a service-oriented flexibility market is introduced. A case study from the Greek power system is used to conduct a cost-benefit analysis of the proposed market solution to the Business as Usual voltage regulation practice, under a spectrum of different technoeconomical parameters. The results indicate that the proposed flexibility solution can have significant economical benefits for TSO to regulate voltage through DSO or TSO connected assets.

Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance.

ICT advancements (low latency new generation networks) transform the Distributed Energy Resources (DERs) at the edge of the electrical grid gradually into grid assets and drive the energy system towards a decentralized transactive operation. Local Energy Markets (LEMs) have been proposed as a means to facilitate and orchestrate the vast penetration of DERs. We propose here a LEM operation which concludes into fair pricing with monetary gains for both prosumers and consumers participating in the LEM. For the Day-Ahead LEM operation we apply a chance-constrained optimization algorithm in order to tackle the uncertainties governing the Day-Ahead LEM operation. The proposed LEM architecture is validated upon an IEEE low voltage European test feeder benchmark.