Abstract
Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

Abstract
The adoption of battery-powered electric vehicles in the EU is expected to grow to 30-40 million by 2030. This, together with the large adoption of other Distributed Energy Resources (DERs), represents a great challenge for Distribution System Operators (DSOs) in multiple perspectives, such as providing the needed charging infrastructure and ensuring that everyone is served with the expected Quality of Service (QoS), by having a secure and reliable system operation capable of mitigating grid congestion and voltage violation events. One of the mechanisms to mitigate these events can be the usage of these DER, such as Electric Vehicles (EVs), as flexibility sources for the improvement of the planning and operation of power distribution systems. This paper proposes harmonising the coordination of the prequalification process for flexibility provision (product and grid prequalification) among System and Market Operators from Portugal, Spain and France, enabling the participation of flexibility providers in multiple markets from cross-border countries through a harmonised and non-redundant prequalification process. © 2022 CIRED workshop on E-mobility and power distribution systems. All rights reserved.

Abstract
Interoperability among diverse devices, from traditional substation control rooms to modern inverters managing components like Distributed Energy Resources (DERs), is a primary challenge in modern power systems. It is essential for streamlining decision-making and control processes through effective communication, ultimately enhancing energy management efficiency. This paper introduces the open-source Legacy Protocol Converter (LPC) grounded in the IEEE 2030.5 standard, which incorporates advanced features for improved adaptability. The LPC bridges legacy equipment using standard protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus with a light-weight asynchronous Neural Autonomic Transport System (NATS) communication system. In light of the limitations inherent in traditional synchronous RESTful systems-specifically those compliant with IEEE 2030.5 that are incapable of facilitating multiple endpoints-the adoption of asynchronous NATS is implemented. This approach can notably enhance communication flexibility and performance. The implementation is containerized for efficient service orchestration and supports the reusability of solutions. The LPC is engineered for seamless integration of DERs with Energy Management System (EMS), aggregation platforms, and Hardware-in-the-loop (HIL) testing environments. In this paper, the LPC has been tested and further developed in various use cases such as multi-physics optimization involving HIL and fast frequency services, e.g., virtual inertia and load shedding, each in a different architectural setup. The findings validate the applicability of LPC not only for devices within modern power systems, but also for heat pumps in the thermal energy sector, facilitating sector coupling. Moreover, the paper provides additional insights into LPC's functionality, reaffirming its efficacy as a scalable, robust, and user-friendly solution for bridging legacy systems through the enhanced IEEE 2030.5 standard designed for the monitoring and control of DERs.

Abstract
The application of Artificial Intelligence (AI) in the energy sector offers new opportunities for developing flexible, efficient, and sustainable infrastructures. Nevertheless, real-world deployment is still constrained by the lack of large-scale, integrated environments that can evaluate advanced algorithms under realistic operating conditions while ensuring regulatory compliance. This paper presents EnerTEF (which stands for Energy Testing and Experimentation Facility), a federated platform for testing and experimentation in the energy sector designed to address this gap. We introduce a unified TEF architecture that enables full-stack evaluation of intelligent systems, including predictive modeling, optimization, learning under data distribution shifts and federated learning across geographically distributed sites. The framework integrates high-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing to support transparent, explainable and robust AI development. EnerTEF demonstrates how such a framework can be deployed in critical energy domains through three real-world scenarios including short-term hydropower generation forecasting, coordination between distribution network operators and distributed energy resources and real-time optimization of self-consumption for municipal buildings. Results show that EnerTEF effectively enables the development of novel AI models, improves cross-context generalizability and supports innovation for complex energy infrastructures, ultimately creating a practical, scalable path for addressing different energy-related problems and heterogeneous data.

Abstract

Abstract
The short-circuit current is one of the most important security operational parameters. With the increased penetration of DERs, it is crucial to frequently and periodically monitor it, ideally every 24 hours and with high granularity (e.g., 30 minutes). This paper develops a short-circuit computation methodology to calculate the complete short-circuit current in the TSO/DSO interface nodes (extra high voltage/high voltage (EHV/HV) substations), which could be used for operational planning purposes, considering the active contributions to the short-circuit current originating from both transmission and distribution networks. A TSO-DSO coordination procedure is presented to obtain the day-ahead short-circuit currents forecast. Moreover, two real cases are provided as examples for validation of the demonstrated procedures. © The Institution of Engineering and Technology 2023.