This paper presents a novel hybrid algorithm for determining the optimal Phasor Measurement Units (PMU) configuration in power networks to ensure full topological and numerical observability through a multi-phase process. In the first phase, a graph-theoretic Heuristic Node Selector (HNS) is developed to rapidly establish topological observability via Core-Tree construction and node dominance evaluation. Unlike most existing studies that implicitly assume topological observability implies numerical observability, the second phase applies a Genetic Algorithm to refine and extend the initial solution from HNS, ensuring complete numerical observability while minimizing number of PMUs. This hybrid method significantly reduces the search space and improves convergence. The HNS procedure is further extended in this work to explicitly handle Zero Injection Buses (ZIB) through rule-based topological modifications, enabling a modified version of the algorithm applicable to real networks with complex structures. Real-world implementation practices from European Transmission System Operators are considered through the adoption of a “one PMU per feeder” configuration. The proposed method is validated on standard IEEE test systems and Serbian transmission networks. Results demonstrate high scalability, adaptability to various network topologies (with and without ZIB nodes), and efficient PMU allocation. Notably, the method consistently achieves high values of the System Observability Redundancy Index, indicating strong robustness and redundancy in measurement placement.

This paper explores the implementation of the Imbalance Netting mechanism within the European power system, with a focus on its application in Serbia. The Imbalance Netting mechanism enables the reduction of opposing automatic Frequency Restoration Reserves (aFRR) activation by optimizing requests among Transmission System Operators (TSOs). The paper presents the benefits of applying this process, including cost reduction, increased system efficiency, and improved regional cooperation. Specific simulation examples and benefits for Serbia are analyzed. The introduction of this mechanism not only enhances grid balancing but also contributes to reducing operational costs and improving the security of Serbia's power system in compliance with ENTSO-E regulations.

This manuscript explores the significance of state estimation methods in electric power systems, with a particular focus on the role of Phasor Measurement Units. In the manuscript is presented the basic principles and advantages of state estimators in the absence of PMU measurements, followed by an analysis of how PMU measurements significantly enhance the accuracy of state estimations. Through a detailed review, we highlight the key characteristics of PMUs and how their synchronized data contributes to improved state estimation quality. The paper investigates the security aspects of state estimation, specifically focusing on malicious attacks through the injection of false measurements (FDIA). Using a concrete example, it is demonstrated how an FDIA attack can significantly impact the accuracy of state estimations by introducing inaccurate data into the system. The manuscript also considers a strategy for detecting such attacks through residual analysis

On June 21, 2024, a severe incident occurred in the power system of Southeastern Europe, resulting in significant load and generation losses across multiple countries, including Albania, Bosnia and Herzegovina, Montenegro, and Croatia. This paper analyzes the sequence of events leading to the system collapse, based on data from the ENTSO-E report. Special attention is given to pre-incident assessments, including security evaluations conducted by Regional Coordination Centers (RCC), the functioning of protection systems, and communication mechanisms between Transmission System Operators (TSOs). Additionally, the system restoration process is examined, identifying key challenges and recommendations for improving the operational resilience of power grids. The paper provides a detailed insight into the technical and organizational aspects of the incident, aiming to enhance prevention methods and accelerate response strategies for future disturbances in power systems.

Artificial Intelligence (AI) is increasingly being applied in power systems, enabling faster and more efficient data analysis, resource optimization, and real-time decision-making. This paper provides an overview of modern AI techniques used in operational management, control, and planning of power grids, with a special focus on neural networks, optimization algorithms (genetic algorithm, particle swarm optimization), and fuzzy logic. The advantages of AI methods over conventional approaches are analyzed, including reduced computation time, improved prediction accuracy, and enhanced system stability. Specific challenges to broader AI implementation in power systems are identified, including limited availability of high-quality labeled data for model training, complexity of integrating AI solutions into existing SCADA/EMS and DMS systems, lack of explainability in neural network outputs, and regulatory uncertainties that limit operational deployment. The paper offers perspectives for future development and research directions, including further integration of AI into smart grids, development of hybrid models for solving complex optimization problems, and the use of neural networks for local marginal price (LMP) prediction.

Detekcija anomalija u elektroenergetskim sistemima važna je za pouzdanost i stabilnost mreže, a posebno u kontekstu integracije obnovljivih izvora energije i sve veće složenosti strukture elektroenergetskog sistema. U ovom radu se istražuje primena Extended Isolation Forest (EIF) algoritma za detekciju anomalija u raznim merenjima koje operatori prenosnih sistema dobijaju u svakodnevnom radu. EIF predstavlja poboljšanu verziju standardnog Isolation Forest algoritma, omogućavajući bolju obradu podataka visoke dimenzionalnosti i veće preciznosti u otkrivanju odstupanja. Rezultati istraživanja ukazuju na sposobnost da EIF precizno otkrije anomalije u realnim i simuliranim podacima. Prikazani pristup može doprineti razvoju naprednih sistema za online nadzor i prevenciju poremećaja u elektroenergetskim sistemima.