Monitoring and diagnostics of metal‐oxide surge arresters (MOSAs) have been a research topic for decades. During these years, many monitoring and diagnostic methods have been developed. However, only a few studies perform a comparative analysis of these methods with respect to their performance in different operating conditions. This study briefly explains the most commonly analysed MOSA monitoring techniques and performs a comparative analysis of these techniques on an experimental basis, considering different operating conditions of the arrester. The experimental setting is made to simulate different operating conditions regarding operating voltage root mean square value, voltage harmonics, and temperature conditions. The results show the reliability of MOSA indicators in the analysed conditions, suggesting which indicators and methods should be used for the purpose of MOSA diagnostics.

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.

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO₂ savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability.

The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation.