Abstract
The present work is inspired by the aftermarket companies of the automotive industry. The goal is to investigate how companies react to market change, by understanding the effect of a perturbation (such as a business cessation) on the rest of the companies that are interconnected through peer-to-peer relationships. An agent-based model has been developed that simulates a multilayer network involving different types of companies: suppliers, aftermarket companies; retailers and consumers. The effect of the cessation is measured by the resilience of the multilayer network after suffering the perturbation. The multilayer network is inspired in a business model of the automobile industry’s aftermarket and each type of company has some defined characteristics. The agent-based model produces the network dynamics due to the changes in its configuration throughout time. No learning mechanism is introduced in this work. We demonstrate that the number of links, the volume of sales and the total profit of a node in the network has an impact on its survival throughout time. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The integration of Distributed Energy Resources (DERs) into low-voltage (LV) distribution grids poses significant challenges for grid management, particularly regarding the need for accurate information on the connection phases of installations to ensure proper load balancing and to enhance hosting capacity. This paper presents a novel voltage-based phase mapping approach using the Mahalanobis-Wasserstein (MW) distance - a metric that exploits voltage time series data to accurately assign users to their corresponding phases without requiring additional hardware or prior knowledge of the grid's topology. The proposed method demonstrates strong resilience to missing data, a frequent issue in real-world deployments, and incorporates a confidence score to quantify the reliability of the phase assignments. © 2025 IEEE.

Abstract
The rapid proliferation of uncontrolled resources poses significant voltage regulation challenges in low-voltage (LV) distribution grids. In this condition, conventional charging strategies, often based on fixed or static schedules, may lead to adverse voltage deviations under unpredictable load conditions and variable renewable generation. To address these challenges, this paper studies a hybrid deep reinforcement learning (DRL) framework based on a Proximal Policy Optimization (PPO) policy network enriched by a Graph Convolution Variation (GCV) feature extractor to improve voltage regulation issues in LV grids. In addition to ensuring that electric vehicles (EVs) achieve their required state-of-charge (SoC), the framework dynamically adjusts charging rates in real time to maintain LV-grid voltage within acceptable limits. Extensive simulation results, including detailed analysis and comparisons with the static charging method, demonstrate significant improvements in voltage regulation, and enhanced overall grid performance. The obtained results demonstrate the effectiveness of controlling EVs' charging controls in an intelligent manner to address the voltage regulation issue in low-voltage grids. © 2025 Elsevier B.V., All rights reserved.

Abstract
The topology of low-voltage (LV) distribution grids is often partially known or inaccurately documented by grid operators, including line and cable characteristics, hindering the effective integration and management of Distributed Energy Resources (DERs). This paper presents a data-driven method to reconstruct LV grid topologies using only voltage measurements from customers' smart meters. The approach relies on an adapted genetic algorithm (GA) that iteratively explores candidate configurations, guided by a score function that evaluates both the physical plausibility of estimated line impedances and their consistency with noisy voltage data, which is progressively corrected throughout the process, i.e., the method also filters out errors affecting the initial measurements. The method requires no prior information on grid connectivity and demonstrates robustness to measurement noise, making it well suited for real-world deployment. © 2025 IEEE.

Abstract
Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a cost-effective investment. Meanwhile, the growing share of non-dispatchable renewable generation demands more flexible energy systems. Local flexibility markets (LFMs) are emerging as effective mechanisms to procure this flexibility, granting ECs a new revenue stream. Since sizing with flexibility becomes a highly complex problem, we propose a 2-stage methodology for estimating DERs size in an EC with collective self-consumption, flexibility provision and cross-sector (CS) assets such as thermal loads and electric vehicles (EVs). The first stage computes the optimal DER capacities to be installed for each member without flexibility provision. The second stage departs from the first stage capacities to assess how to modify the initial capacities to profit from providing flexibility. The impact of data clustering and flexibility provision are assessed through a case study.

Abstract
Grid scale Battery Energy Storage Systems (BESS) have a key role for future power systems operation and stability. However, cyclic degradation, intensified by multi-service operation, remains a major challenge, directly affecting battery lifespan and profitability. This study examines BESS participation in energy markets and in automatic frequency restoration reserve (aFRR) markets, assessing the impact of cyclic degradation costs on BESS planning and operation. The methodology involved modelling the daily dispatch of an 8.1 MW lithium-ion battery for participation in day-ahead, intraday and reserve markets, incorporating a degradation cost minimization model. The simulations were conducted using the historical data from Iberian electricity and Portuguese ancillary services market, such as energy prices, historical reserve requirements and AGC forecasts. The results show that reserve market participation is highly profitable and can be successfully complemented with day-ahead and intraday market participation. Also, incorporating cyclic degradation cost into planning extends BESS lifespan in all cases. However, this approach is beneficial only in arbitrage scenarios, while in reserve market participation, it reduces profits. The findings highlight the importance of balancing BESS degradation minimization with profitability, particularly in reserve market participation. Future research could apply this model to different battery technologies and real-world systems to validate the simulated results.