The complexity in the power system topology, together with the new paradigm in generation and demand, make achieving an adequate level of supply quality a complicated goal for distribution companies. The electrical system power quality is subject to different regulations. On one hand, EN-50160 establishes the characteristics of the voltage supplied by public electricity networks, therefore affecting distribution companies. On the other hand, the EN-61000 series of standards regulates the electromagnetic compatibility of devices connected to the network, therefore affecting the loads. Power companies and device manufacturers are both responsible and affected in the issue of quality of supply. Despite the regulations, there are certain aspects of the supply quality that are not solved. One of the most important is the location of the disturbance’s origin. This paper presents a review of the main techniques to locate the disturbance’s origin in the electric network through two approaches: identification of the disturbance’s cause and the location of the origin.

Due to a greater social and environmental awareness of citizens, advantageous regulations and a favourable economic return on investment, the presence of photovoltaic (PV) installations in distribution grids is increasing. In the future, not only a significant increase in photovoltaic generation is expected, but also in other of the so-called distributed energy resources (DER), such as wind generation, storage, electric vehicle charging points or manageable demands. Despite the benefits posed by these technologies, an uncontrolled spread could create important challenges for the power system, such as increase of energy losses or voltages out-of-limits along the grid, for example. These issues are expected to be more pronounced in low voltage (LV) distribution networks. This article has two main objectives: proposing a method to calculate the LV distributed photovoltaic generation hosting capacity (HC) that minimizes system losses and evaluating different management techniques for solar PV inverters and their effect on the hosting capacity. The HC calculation is based on a mixture of deterministic methods using time series data and statistical ones: using real smart meters data from customers and generating different combinations of solar PV facilities placements and power to evaluate its effect on the grid operation.

In low-voltage grids with a wide spread of domestic and/or small commercial consumers, mostly single-phase, problems can appear due to unbalanced power consumption between the different phases. These problems are mainly caused due to voltage unbalances between phases and the increase in distribution losses. This phenomenon occurs more frequently at the end of highly radial grids and can be stressed by the installation of renewable generators next to the consumers. Amongst the various techniques that have been proposed to solve this problem, this article explores the use of a D-STATCOM, presenting and testing a new method for the optimal location of this type of D-FACT. The developed method starts from a detailed analysis of the existing voltage unbalances in a distribution network and identifies the optimal location of the D-STATCOM (i.e., the one that reduces these unbalances while reducing energy losses). The developed method has been successfully tested for one year at four real European locations with different characteristics and different kinds of users.

Aligned with the objectives of the energy transition, the increased penetration levels of electric vehicles as part of the electrification of economy, especially within the framework of local energy communities and distributed energy resources, are crucial in shaping sustainable and decentralized energy systems. This work aims to assess the impact of escalating electric vehicles’ deployment on sustainable local energy community-based low-voltage distribution networks. Through comparative analyses across various levels of electric vehicle integration, employing different charging strategies and system management approaches, the research highlights the critical role of active system management instruments such as smart grid monitoring and active network management tools, which significantly enhance the proactive management capabilities of distribution system operators. The findings demonstrate that increased electric vehicle penetration rates intensify load violations, which strategic electric vehicle charging management can significantly mitigate, underscoring the necessity of load management strategies in alleviating grid stress in the context assessed. This study highlights the enhanced outcomes derived from active system management strategies which foster collaboration among distribution system operators, demand aggregators, and local energy communities’ managers within a local flexibility market framework. The results of the analysis illustrate that this proactive and cooperative approach boosts system flexibility and effectively averts severe grid events, which otherwise would likely occur. The findings reveal the need for an evolution towards more predictive and proactive system management in electricity distribution, emphasizing the significant benefits of fostering robust partnerships among actors to ensure grid stability amid rising electric vehicle integration.

The undergoing energy transition to a sustainable future requires, among other things, the application of demand side management (DSM) techniques to maintain grid stability and allow a smooth performance. To successfully implement DSM strategies, near real-time monitoring of the grid is required. This can be achieved through a distribution system state estimator (DSSE). Conventional approaches to state estimation (SE) typically rely on the assumption of a balanced reference bus, which is reasonable for transmission systems but may not be applicable to low-voltage distribution networks, even more with significant distributed generation (DG) penetration. To address this problem, a branch current-based low-voltage DSSE for unbalanced three-phase systems is developed. The algorithm incorporates a virtual bus to account for highly unbalanced systems, enabling it to obtain a more accurate estimation of the grid state. The proposed method is compared to the conventional balanced reference bus method through multiple simulations under different load conditions in the IEEE European low-voltage test feeder.

The integration of renewable energy sources into electrical grids introduces significant challenges, particularly in ensuring stability and reliability in dynamic, nonlinear environments. These sources create fluctuations, uncertainties, and voltage regulation issues that traditional control systems struggle to manage, compromising the grid’s ability to deliver a stable power supply. Addressing these challenges requires advanced, adaptive control solutions capable of responding to the variable nature of renewable energy. This article explores advanced techniques, focusing on reinforcement learning and Neuroevolution, to develop innovative control strategies for electrical systems. Neuroevolution, which combines neural networks with evolutionary algorithms, optimizes control without relying on gradient-based methods, making it suitable for complex, unpredictable scenarios. These approaches enhance grid stability, improve response times, and enable realtime anomaly detection and corrective actions, offering a resilient and efficient solution to the limitations of traditional control methods.