Abstract
Low voltage (LV) grids face a challenge of effectively managing the growing presence of new loads like electric vehicles and heat pumps, along with the equally growing installation of rooftop photovoltaic panels. This paper describes practical applications of sensitivity factors, extracted from smart meter data (i.e., without resorting to grid models), to i) link voltage problems to different costumers/devices and their location in the grid, ii) manage the flexibility provided by distributed energy resources (DERs) to regulate voltage, and iii) assess favorable locations for DER capacity extensions, all with the aim of supporting the decision-making process of distribution system operators (DSOs) and the design of incentives for customers to invest in DERs. The methods are tested by running simulations based on historical meter data on six grid models provided by the EU-Joint Research Center. The results prove that it is feasible to implement advanced LV grid analysis and management tools despite the typical limitations in its electrical and topological characterisation, while avoiding the use of computationally heavy tools such as optimal power flows.

Abstract
In a photovoltaic power plant (PVPP), the DC-AC converter (inverter) is one of the components most prone to faults. Even though they are key equipment in such installations, their fault detection techniques are not as much explored as PV module-related issues, for instance. In that sense, this paper is motivated to find novel tools for detection focused on the inverter, employing machine learning (ML) algorithms trained using a hybrid dataset. The hybrid dataset is composed of real and synthetic data for fault-free and faulty conditions. A dataset is built based on fault-free data from the PVPP and faulty data generated by a digital twin (DT). The combination DT and ML is employed using a Clarke/space vector representation of the inverter electrical variables, thus resulting in a novel feature engineering method to extract the most relevant features that can properly represent the operating condition of the PVPP. The solution that was developed can classify multiple operation conditions of the inverter with high accuracy.

Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

Abstract
This paper presents a novel solution integrated into the Eneida DeepGrid® platform for real-time voltage and active power estimation in low voltage grids. The tool utilizes smart grid infrastructure data, including historical data, real-time measurements from a subset of meters, and exogenous information such as weather forecasts and dynamic price signals. Unlike traditional methods, the solution does not require electrical or topological characterization and is not affected by observability issues. The performance of the tool was evaluated through a case study using 10 real networks located in Portugal, with results showing high estimation accuracy, even under scenarios of low smart meter coverage. © The Institution of Engineering and Technology 2023.

Abstract
Artificial intelligence (AI) brings great potential but also risks to the electricity industry. Following the EU's current regulatory proposal, the EU Regulation for Artificial Intelligence (AI Act), there will be direct, potentially adverse effects on companies of the electricity industry in Europe and beyond, as well as those active in the development of AI systems. In this paper, we develop a replicable framework for estimating compliance costs for different electricity market agents that will need to comply with the numerous requirements the AI Act imposes. The electricity systems of Austria, Greece and Switzerland are used as case-studies. We estimate annual, aggregated costs for electricity market agents ranging from less than one million to almost 200 million Euros per country, depending on compliance costs scenarios. Results suggest that a profit growth of 10% through AI utilization is needed to offset the highest added compliance cost of the AI Act on electricity market agents. Eventually, we further show how to assess the regional differences of these costs added to system operation, providing spatially disaggregated compliance costs estimates that consider the structural differences of the electricity industry within 26 Swiss cantons.

Abstract
Electric power systems are currently experiencing a profound change, as increasing amounts of Renewable Energy Sources (RESs) displace conventional forms of generation. This development has gone hand-in-hand with an increasing share of distributed power generation being connected directly to the Distribution Network (DN), and the widespread of other types of Distributed Energy Resources (DERs), such as Energy Storage Sytems (ESSs), Electric Vehicles (EVs), and active (flexible) consumers. As these trends are expected to continue, this will require a profound revision of the way Transmission System Operators (TSOs) and Distribution System Operators (DSOs) interact with each other to fully benefit from the growing flexibility that is available at the DN level. In this work we propose a new tool for the coordinated operational planning of transmission and distribution systems, considering the existence of shared resources that can be simultaneously used by TSO and DSOs for the optimal operation of their networks. The tool uses advanced distributed optimization techniques, namely the Alternating Direction Method of Multipliers (ADMM) in order to maintain data privacy of the several agents involved in the optimization problem, and keep the tractability of the problem. The proposed tool is applied to modified IEEE test systems, and the results obtained highlight the benefits of the proposed coordination mechanism to solve problems occurring simultaneously at the transmission and DN-levels. © 2023 IEEE.