With the advancement of regulations regarding the reduction in carbon emissions, renewable energy communities have come into the picture. However, many implications come with the installation of these communities from a cybersecurity point of view. The software platforms responsible for managing and controlling them handle a lot of crucial information, and therefore, tampering with these data can lead to several impacts on the operation of these communities and, in turn, the power grid as well. This paper examines the plausible impacts that can be caused by altering certain parameters of the system that make it a potential cyber attack target. The analysis is done by observing how the grid responds to these manipulations for both low-voltage as well as medium-voltage systems. These systems are designed along with integrated energy communities and are implemented in MATLAB/Simulink R2022b software. The observations are made by plotting the grid voltage and power profiles in normal as well as attacked conditions.

Smart grid technologies are evolving, especially in distribution systems. Distributed energy resources (DERs) are increasingly controlled by cloud platforms by using the public Internet, and this exposes them to cyberattacks; these attacks can cause severe issues to the power grid, and usually, there are no automatic responses, but rather, cyberdefense is delegated to manual operations despite the very rapid dynamics of the power system. This article discusses the possibility of using cybersecurity functions in electrical protection as an attack-response tool, making electrical protections able to trip after the detection of an ongoing attack; these functions have the aim of guaranteeing the continuity of the power system under cyberattacks. The article details this approach, provides a use case, and simulates the effects of the implementation of the proposed solution in the power system.

Virtual Power Plants (VPPs) have emerged as critical infrastructure for grid stability, aggregating diverse Distributed Energy Resources (DERs) to provide essential ancillary services, including frequency regulation, voltage support, and emergency response capabilities. However, the technical requirements that enable VPPs to deliver these time-critical services simultaneously create unique cybersecurity vulnerabilities that distinguish them from traditional power generation and conventional smart grid systems. This paper establishes systematic connections between VPP technical requirements and cybersecurity threats through the integrated application of NIST and MITRE frameworks. The objective is to reveal critical threats specifically pertaining to ancillary services, comprehensive attack vector classification using MITRE ATT&CK techniques adapted for VPP environments, and mitigation strategies that maintain operational performance while addressing identified vulnerabilities.

Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment.