The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

In the past few years, technical progress has determined traditional electric power system conversion to Smart Grids, and consequently empowered a worldwide renaissance in the forgotten PQ field. Since the installation of PQ monitors is currently associated with high investments and operational costs, it is inappropriate to install a monitor on each grid busbar. Hence, grid operators must establish the best cost–benefit scenario for monitors installation and achieve maximal observability with a limited number of analyzers. Firstly, this paper presents a review of the state-of-the-art of displacement strategies, and discusses them regarding the tendencies of node selection criteria, the test schemes used, and the grid size. Secondly, the relevant fundamental issues which must be solved in the future in order to eliminate restrictions in PQ monitors’ allocation planning are presented and discussed. These issues concern the treatment, interpretation, and assessment of PQ events, measurement chain technical features, PQ’s role in the grid planning stage, communication technologies, and other remote monitoring aspects, and integration with other Smart Grid applications. The provided insights are based on experience which has been obtained during the PQ measurement campaign in the Lithuanian DSO grid. Finally, a PQ system development strategy—both short-term and long-term perspectives—in the Lithuanian distribution grid is presented, including monitors allocation criteria.

Nowadays, voltage sag continues to remain a critical PQ issue in the industry. Since it is not possible to install a voltage analyzer on every node, optimal monitoring locations must be determined. However, during the PQ monitoring campaign of the Lithuanian DSO grid, the execution of this task was inhibited by the lack of knowledge and literature about the fundamentals of voltage sags propagation. Therefore, the first part of this paper investigates the propagation paths of voltage sags by using a voltage sag matrix method on the created test grid, which for the first time is BRELL-based. This paper is the first to not only investigate voltage sags propagation paths (both downward and upward) in detail, but also to investigate them complexly, encompassing all four types of short-circuits, both voltages (phase-to-phase and phase-to-ground), and interconnections with other PQ events (interruption, transient, unbalance). The result has established a generalization of voltage sags propagation mechanisms by inductive reasoning and laid the foundation for the further development of PQ theory. The second part of this paper focuses on the Lithuanian DSO grid and is one of the first to investigate voltage sags/interruptions through the prism of both relationship with reliability indexes (SAIFI and SAIDI) and primary causes. For this purpose, we created our own scientific methodology, applying known probabilistic and statistical methods along with newly proposed approaches, in particular for ACR success evaluation and estimation of functions similarity.

Poor PQ is a partial case of power system impact on society and the environment. Although the significance of good PQ is generally understood, the topic has not yet been sufficiently explored in the scientific literature. Firstly, this paper discusses the role of PQ in sustainable development by distinguishing economic, environmental, and social parts, including the existing PQ impact assessment methods. PQ problems must be studied through such prisms as financial losses of industrial companies, damage to end-use equipment, natural phenomena, interaction with animals, and social issues related to law, people’s well-being, health and safety. Secondly, this paper presents the results of the survey of Lithuanian industrial companies, which focuses on the assessment of industrial equipment immunity to both voltage sags and supply interruptions, as well as a unique methodology based on expert assessment, IEEE Std 1564-2014 and EN 50160:2010 voltage sag tables, matrix theory, a statistical hypothesis test, and convolution-based sample comparison that was developed for this purpose. The survey was carried out during the PQ monitoring campaign in the Lithuanian DSO grid, and is one of the few PQ surveys presented in the scientific literature. After counting the votes and introducing the rating system (with and without weights), the samples are compared both qualitatively and quantitatively in order to determine whether the PQ impact on various end-use equipment is similar or not.

The dependences of the surface roughness and the phase structure of compound thin films on substrate temperature and flux of incoming particles are investigated by a proposed mathematical model. The model, which describes physically deposited thin compound film growth process is based on the Cahn–Hilliard equation and includes processes of phase separation, adsorption, and diffusion. In order to analyze large temperature range and assuming deposition of energetic particles, the diffusion is discriminated into thermal diffusion, radiation-enhanced diffusion, and ion beam mixing. The model is adapted to analyze surface roughness evolution during film growth. The influences of the substrate temperature and incoming flux particles on the surface roughness are determined by a series of numerical experiments. The modelling results showed that the surface roughness increased as the substrate temperature rose. Besides, a similar relationship was discovered between substrate temperature and size of nanoparticles formed in binary films, so the increase in the surface roughness with the substrate temperature was attributed to the increase in size of nanoparticles.

This research introduces a novel hybrid forecasting framework for solar energy prediction in high-latitude regions with extreme seasonal variations. This approach uniquely employs General Type-2 Fuzzy Logic (GT2-FL) for data preprocessing and uncertainty handling, followed by two advanced neural architectures, including BiLSTM and SCINet with Time2Vec encoding and Variational Mode Decomposition (VMD) signal processing. Four configurations are systematically evaluated: BiLSTM-Time2Vec, BiLSTM-VMD, SCINet-Time2Vec, and SCINet-VMD, each tested with GT2-FL preprocessed data and raw input data. Using meteorological data from Lithuania (2023–2024) with extreme seasonal variations where daylight hours range from 17 h in summer to 7 h in winter, F-BiLSTM-Time2Vec achieved exceptional performance, with nRMSE = 1.188%, NMAE = 0.813%, and WMAE = 3.013%, significantly outperforming both VMD-based variants and SCINet architectures. Comparative analysis revealed that Time2Vec encoding proved more beneficial than VMD preprocessing, especially when enhanced with fuzzification. The results confirm that fuzzification, BiLSTM architecture, and Time2Vec encoding provide the most robust forecasting capability under various seasonal conditions.