Abstract
Determining the transformer top-oil temperature (TOT) is one of the key issues in determining the transformer insulation life and reliability of the power system. Due to the non-linear nature of the model presented in the IEEE C57.91 standard to determine this temperature, a more precise method is needed to estimate the equation coefficients to estimate the TOT in the future. This paper presents a method for online thermal modelling of the transformer according to the IEEE C57.91 based on the Unscented Kalman filter (UKF). This method can be applied to transformers with a variety of cooling modes and estimates the TOT with an acceptable error. In order to evaluate the proposed method, the practical data of the 800 kVA distribution transformer with unknown equation coefficients and simulated data with known coefficients are used, and finally, by calculating the estimation error, the proper performance of the presented method is proved. It is proved that the proposed method predicts TOT even in the presence of noise with an error of less than 0.5 degrees C and a delay of less than 1.5 h. It makes the proposed method can be implemented for purposes such as load management, and insulation life estimation of the transformer.

Abstract
This work presents a new dump load controller for active power management of hydraulic generator unit using the power system parameters at the generator's terminals. An existing system with an analog controller is evaluated and a new digital open source HPP (Hydraulic power plant) power controller with extended functionality is proposed, designed and realized. A simulation of the voltage measurement sensor in LTspice is performed in order to determine the appropriate hardware parameters. Comparison between the features of the existing analog controller and the new digital controller is performed. Based on the results achieved, valuable conclusions are made. The new solution proposed offers simplified hardware, high reliability, easiness and flexibility of controller settings.

Abstract
Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.

Abstract
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (baseline) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

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
Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.