This paper presents a real-time Adaptive Protection Scheme (APS) for Medium-Voltage (MV) distribution networks with high penetration of inverter-based Distributed Energy Resources (DER). The framework combines offline and online operational phases for improving overcurrent protection coordination and decision stability under realistic operating conditions. This paper proposes a robust real-time selection algorithm for the protection groups, embedding explicit anomaly detection and anti-flapping mechanisms on top of a previously validated adaptive engine. The online algorithm includes a variational autoencoder (VAE)-based current measurement filter and outlier detector, and a support vector machine (SVM) for mapping filtered real-time current measurements to the pre-defined protection settings groups. A multi-criteria anti-flapping logic is then applied to enforce membership-based confidence, temporal dwell times, and M-of-N persistence before issuing setting group changes. The adaptive protection scheme is IEC 61850-compliant and can be demonstrated in real-time simulation and hardware-in-the-loop (HIL) setups. Extensive short circuit studies on the IEEE 33 bus test feeder across multiple distributed energy resources (DER) penetration levels and two fault resistance regimes (solid, 0 S2; and 30 S2) show a 99.9% successful fault clearing rate, with improved selectivity, sensitivity, and overall robustness of the protection scheme while significantly reducing spurious setting oscillations under DER ramps, noisy measurements, and sensor dropouts.

Increased electrification of various critical infrastructures has been recognized as a key to achieving decarbonization targets worldwide. This creates a need to better understand the risks associated with future power systems and how such risks can be defined, assessed, and mitigated. This paper surveys prior work on power system risk assessment and management and explores the various approaches to risk definition, assessment, and mitigation. As a result, the paper proposes how future grid developments should be assessed in terms of risk causes, what methodology may be used to reduce the risk impacts, and how such approaches can increase grid resilience. While we attempt to generalize and classify various approaches to solving the problem of risk assessment and mitigation, we also provide examples of how specific approaches undertaken by the authors in the past may be expanded in the future to address the design and operation of the future electricity system to manage the risk more effectively. The importance of the metrics for risk assessment and methodology for quantification of risk reduction are illustrated through the examples. The paper ends with recommendations on addressing the risk and resilience of the electricity system in the future resilient implementation while achieving decarbonization goals through massive electrification.

The purpose of an Advanced Distribution Management System (ADMS) is to consolidate the key operational functions of a SCADA system, Outage management System (OMS) and Distribution Management System (DMS) into a unified platform. This includes several key functions: SCADA operation, incidents and outages management, teams and field works management including switching operations and advanced applications for network analysis and optimization. The new generation of ADMS also implements a predictive operation strategy to enhance real-time operator responsiveness. The innovative aspects related to the new generation of ADMS built on top of an open architecture will be presented in this paper.

Flexibility markets are emerging across Europe to improve the efficiency and reliability of distribution networks. This paper presents a methodology that integrates local flexibility markets into network maintenance scheduling, optimizing the process by contracting flexibility to avoid technical issues under the topology defined to operate the network during maintenance. A meta-heuristic approach, Evolutionary Particle Swarm Optimization (EPSO), is used to determine the optimal network topology.

Abstract This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

The increasing penetration of inverter-based resources (IBR) in medium voltage (MV) networks presents significant challenges for traditional overcurrent (OC) protection systems, particularly in ensuring selectivity, reliability, and fault isolation. This paper presents an adaptive protection system (APS) that dynamically adjusts protection settings based on real-time network conditions, addressing the challenges posed by distributed energy resources (DER). The methodology builds on ongoing research and development efforts, combining an offline phase, where operational scenarios are simulated using historical data, clustered with fuzzy c-means (FCM), and optimized with evolutionary particle swarm optimization (EPSO), and an online phase. To overcome the static nature of conventional schemes, a machine learning (ML)-based classifier is integrated into the APS, enabling real-time adaptation of protection settings. In the online phase, a centralized substation protection controller (CPC) leverages real-time measurements, communicated via IEC 61850 standard protocols, to classify network conditions using a support vector machine (SVM) classifier and activate the appropriate protection settings. The proposed APS has been validated on a Hardware-in-the-Loop (HIL) platform, demonstrating significant improvements in fault detection times, selectivity, and reliability compared to traditional OC protection systems. As part of a continued effort to refine and expand the system’s capabilities, this work highlights the potential of integrating artificial intelligence (AI) and real-time/online decision-making to enhance the adaptability and robustness of MV network protection in scenarios with high DER penetration.

The integration of Distributed Energy Resources (DERs) into low-voltage (LV) distribution grids poses significant challenges for grid management, particularly regarding the need for accurate information on the connection phases of installations to ensure proper load balancing and to enhance hosting capacity. This paper presents a novel voltage-based phase mapping approach using the Mahalanobis-Wasserstein (MW) distance — a metric that exploits voltage time series data to accurately assign users to their corresponding phases without requiring additional hardware or prior knowledge of the grid’s topology. The proposed method demonstrates strong resilience to missing data, a frequent issue in real-world deployments, and incorporates a confidence score to quantify the reliability of the phase assignments.

Modern distribution networks increasingly incorporate intelligent automation schemes to enhance resilience and reduce service interruptions following faults. To support these strategies, this paper investigates the use of machine learning models for fault location, aiming to quickly identify the faulted area and support safe service restoration of non-faulted areas. A comparative study is conducted using three supervised learning methods: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), applied to fault location in a distribution test system adapted to include Distributed Energy Resources (DER). Using steady-state current measurements generated from probabilistic fault scenarios based on historical data, each model is evaluated in terms of classification accuracy and computational feasibility. Results indicate that the models demonstrated high classification accuracy and efficient execution time, confirming the viability of machine learning (ML)-based approaches as effective decision-support tools for intelligent fault isolation and service restoration.

Local flexibility markets are a promising solution to aid system operators in managing the network as it faces the growth of distributed resources and the resulting impacts on voltage control, among other factors. This paper presents and simulates a proposal for an intra-day local flexibility market based on grid segmentation. The design provides a market-based solution for distribution system operators (DSOs) to address near-real-time grid issues. The grid segmentation computes the virtual buses that represent each zone and the sensitivity indices that approximate the impact of activating active power flexibility in the buses within the zone. This approach allows DSOs to manage and publish their flexibility needs per zone and enables aggregators to offer flexibility by optimizing their resource portfolios per zone. The simulation outcomes allow for the assessment of market performance according to the number of zones computed and show that addressing overloading and voltage control through zonal approaches can be cost-effective and counterbalance minor errors compared to node-based approaches.

Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

Nowadays, human operators at control centers analyze a large volume of alarm information during outage events and must act fast to restore the service. To assist operator decisions this work proposes novel machine learning-based functions aiming to: (a) classify the complexity of a fault occurrence (Occurrences Classifier) and its cause (Fault Cause Classifier) based on its alarm events; (b) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes alarm information of an occurrence and classifies it as a "simple"or "complex"occurrence, while the Fault Cause Classifier predicts the cause class of MV lines faults. The Data2Actions takes a sequence of alarm information from the occurrence and suggests a more adequate sequence of switching actions to isolate the fault section. These algorithms were tested on real data from a Distribution System Operator and showed: (a) an accuracy of 86% for the Data2Actions, (b) an accuracy of 68% for the Occurrences Classifier, and (c) an accuracy of 74% for the Fault Cause Classifier. It also proposes a new representation for SCADA event log data using graphs, which can help human operators identify infrequent alarm events or create new features to improve model performance.

The penetration of Distributed Generation into the distribution networks has been increasing during the last decades.However, the increase of generation brings additional technical challenges for the Distribution Network Operators.Thus, the integration of Distributed Generation along with the improvement of the quality of service is driving Distribution Network Operators to actively control their networks.In order to achieve an effective control of the distribution network it is necessary to estimate the voltage on the network.This paper presents a method to accurately estimate voltage on the MV Network from limited measures of voltage and current.A method to determine the best location to place those voltage and current sensors is also explained

The integration of inverter-based distributed generation challenges the implementation of an reliable protection This work proposes an adaptive protection method for coordinating protection systems using directional overcurrent relays, where the settings depend on the distribution network operating conditions. The coordination problem is addressed through a specialized genetic algorithm, aiming to minimize the total operating times of relays with time-delayed operation. The pickup current is also optimized. Coordination diagrams from diverse fault scenarios illustrate the method's adaptability to different operational conditions, emphasizing the importance of employing multiple setting groups for optimal protection system performance. The proposed technique provides high-quality solutions, enhancing reliability compared to traditional protection schemes.

This ork presents an approach to the flexibility of energy consumption in Renewable Energy Communities (RECs). A two-stage model for quantifying the flexibility provided by the domestic energy resources operation and its negotiation in a market platform is proposed. In stage 1, the optimal consumption of each prosumer is determined, as well as the respective technical flexibility of their resources, namely the maximum and minimum resource operation limits. In stage 2, this technical flexibility is offered in a local flexibility-only market structure, in which both the DSO and the prosumers can present their flexibility needs and requirements. The flexibility selling and buying bids of the prosumers participating in the market are priced based on their base tariff, which is the energy cost of the prosumers corresponding to their optimal schedule of the first stage when no flexibility is provided. Therefore, providing flexibility is an incentive to reduce their energy bill or increase their utility, encouraging their participation in the local flexibility market.

Taking advantage of the flexibility of Distributed Energy Resources (DER) can help improve distribution network efficiency, reliability and resilience. EUniversal project aims to facilitate the use of flexibility services and interlink active system management of distribution system operators with electricity markets. congestion management and voltage control have been identified has the most relevant needs, within different operation timeframes, namely: from day, weeks and years ahead. This paper considers long-term flexibility services to support maintenance actions, increasing the periods where is technically possible to perform maintenance actions maintaining security of operation. The methodology developed to schedule planned maintenance actions based on forecasted network profiles, maintenance costs, network reconfiguration capability and flexibility contracted in long-term flexible markets will be presented.

The EUniversal project, funded by the European Union, aims to establish a universal approach to the utilization of flexibility by Distribution System Operators (DSOs) and their engagement with new flexibility markets. To achieve this objective, the project team has focused on developing the Universal Market Enabling Interface (UMEI) concept. This paper presents an overview of the process of adapting grid core systems to interact with different market platforms and agents, which is a key aspect of the real-world demonstration set to take place in Portugal.

This paper proposes the coordinated operation of OLTC and flexibility services in MV and LV feeders in a distribution system with high shares of PV generation. Consumers’ flexibilities are traded in a local energy market, and a cost-effective approach is adopted. The costs of flexibility resources are minimized, and voltage sensitivity coefficients are used to guarantee the minimal use of active and reactive flexibility resources and the optimal tap adjustment. Sensitivity coefficients are obtained in MV feeder using an analytical approach, and in LV feeder using a data-driven approach which does not require the knowledge of grid topology and/or electrical data. The proposed methodology is validated in a MV-LV network and different scenarios are analyzed. Results show the proposed methodology can be an effective solution to mitigate voltage problems, combining the wider acting of OLTC with the use of local flexible services.

The present paper presents the implementation of nextgeneration centralized Protection, Automation, and Control (PAC) solution for Medium Voltage (MV) power grids, developed in the scope of the SCALE project [1]. The main goals of the project are the development, testing, and field pilot deployment of an innovative, fully digital PAC system for Substation Automation (SAS), centralizing in a single device the functionalities of several bay-level Intelligent Electronic Devices (IED). The envisioned system, comprised of a Centralized Protection and Control (CPC) device and Merging Units (MU)/Process Interface Units (PIU), constitutes a highly flexible, resilient, futureproof solution that relies both on modern IEC 61850 standards and on legacy industrial protocols to guarantee multi-vendor interoperability and continued integration with multi-generation devices inside and outside of the substation. Centralizing SAS functionalities in a single device provides access to a wide range of data and measurements that unlocks technologically advanced substation-centric network automation applications.

This paper presents an innovative digital platform for managing energy communities with self-consumption and energy trading in a local electricity market. Its architecture is based on micro-services, such as the energy transaction service, the settlement service to compute the financial compensations among community members for the energy transacted, or a resource sizing service. This approach enables the platform to be more efficient and scalable, making easier to incorporate new functionalities while maintaining a secure community and energy transactions management. The transactions and settlement procedures, adapted to the Portuguese regulation, are described, and the results of the platform operating a post-delivery pool market are presented and analyzed. This paper contributes to the understanding and improvement of renewable energy communities’ business models and management, offering insights for policymakers, researchers, and practitioners in the field.