This work focused on prescribing, designing, implementing, and evaluating a pilot project conducted in the Greek power system that addressed balancing and congestion management issues that system operators (SOs) face within the clean energy era. The considered pilot project fully focused on the development of the F-channel platform, including the idea behind this application, the steps that were taken in the process, and the outcomes of the performed activities fitting into the overall picture of the OneNet project. The specified F-channel platform is a web-based, client-server application that uses artificial intelligence (AI) techniques and cloud computation engines to improve the management of the active power for the TSO-DSO coordination. The flexibility of the grid’s resources was identified, and an integrated monitoring system based on the precise forecasting of variable generation and demand was implemented. The focus areas were congestion management, frequency control, and voltage control services, for which corresponding network models were created in close cooperation with system operators. The obtained results are essential for the remaining demonstration results because they offer an incredibly accurate basis for further research into their use in congestion management and other weather-related enhanced transmission and distribution system planning and operation practices.

It is challenging for the European power system to exactly predict RES output and match energy production with demand due to changes in wind and sun intensity and the unavoidable disruptions caused by severe weather conditions. Therefore, in order to address the so-called “flexibility challenge” and implement the variable RES production, the European Union needs flexible solutions. In order to accommodate quicker reactions, compared to those performed today, and the adaptive exploitation of flexibility, grid operators must adjust their operational business model, as the electrical grid transitions from a fully centralized to a largely decentralized system. OneNet aspires to complete this crucial step by setting up a new generation of grid services that can fully utilize distributed generation, storage, and demand responses while also guaranteeing fair, open, and transparent conditions for the consumer. Using AI methods and a cloud-computing approach, the current work anticipates that active management of the power system for TSO–DSO coordination will be improved by the web-based client-server application F-channel. In the current work, a user’s experience with the platform for a Business Use Case (BUC) under the scenario of severe weather conditions is presented. The current work aims to increase the reliability of outage and maintenance plans for the system operators (SOs) by granting them a more accurate insight into the conditions under which the system may be forced to operate in the upcoming period and the challenges that it might face based on those conditions. In this way, the methodology applied in this case could, via AI-driven data exchange and analyses, help SOs change the maintenance and outage plans so the potential grave consequences for the system can be avoided. The SOs will have accurate forecasts of the relevant weather parameters at their disposal that will be used in order to achieve the set targets. The main results of the presented work are that it has a major contribution to the optimal allocation of the available resources, ensures the voltage and frequency stability of the system, and provides an early warning for hazardous power system regimes.

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks.