Energetika

Title

2025-01-29T17:38:07+02:00

2025-01-29T17:40:14+02:00

Digital transformation in energy systems: a comprehensive review of AI, IoT, blockchain, and decentralised energy models

2025-03-27T12:03:11+02:00

Digital transformation (DT) in the energy sector is pivotal in meeting energy transformation challenges. DT is reshaping energy production, distribution, and consumption by integrating advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and digital twins. While existing research has extensively documented individual technological applications, there remains a significant gap in understanding how these technologies interact synergistically in real-world implementations [11]. Comprehensive analyses comparing digital transformation outcomes across different socioeconomic contexts are limited, particularly regarding the scalability of swarm electrification models. These technologies collectively address the ‘three Ds’ – decentralisation, decarbonisation, and digitalisation – essential for the evolution of modern energy systems. By leveraging these innovations, the sector can significantly enhance efficiency, optimise renewable energy integration, and expand access to underserved regions.
One of the most impactful applications of DT is in the realm of decentralised energy systems, exemplified by swarm electrification. This concept, pioneered by Groh et al [3], utilises interconnected solar home systems (SHSs) to form scalable microgrids that evolve from standalone setups to full integration with national grids. These systems empower communities by facilitating energy sharing, reducing operational costs, and creating new income streams. Case studies from Kenya, Madagascar, Yemen, Germany, and Bolivia demonstrate the real-world success of swarm electrification in bridging the energy access gap while advancing sustainability goals.
AI plays a pivotal role in digital energy systems by enabling predictive maintenance, optimising energy flows, and improving system reliability. Algorithms analyse vast datasets in real time to forecast energy demand, detect anomalies, and automate grid management. IoT further complements AI by providing the physical infrastructure to gather and transmit data, enabling real-time monitoring and control of energy assets. Together, AI and IoT support the development of smart grids and energy communities, fostering greater flexibility and resilience in energy networks.
Blockchain technology is emerging as a transformative tool for energy trading and distribution. By enabling peer-to-peer (P2P) energy markets, blockchain enhances transparency and reduces transaction costs. This decentralisation of energy trading allows consumers to become prosumers, actively participating in energy production and exchange. Projects such as Esmat et al. decentralised platforms exemplify how blockchain empowers individuals and communities to take ownership of their energy futures while ensuring security and scalability.
Despite these advancements, the implementation of digital technologies in energy systems is facing significant challenges. High initial costs, the complexity of integration, and cybersecurity risks pose barriers to widespread deployment. Furthermore, the digital divide in underserved regions limits equitable access to these transformative solutions. Environmental concerns related to the energy consumption of digital infrastructures, such as data centres and blockchain networks, also require attention. Addressing these issues necessitates a multi-stakeholder approach involving policymakers, industry leaders, and researchers to create enabling environments for innovation.
This review provides a comprehensive analysis of the role of DT in advancing energy systems, focusing on AI, IoT, blockchain, and swarm electrification. It synthesises insights from over 100 scholarly sources, including real-world case studies, and evaluates the social, economic and technological impact of digitalisation on energy systems. The study adopts a mixed-method approach, integrating literature analysis, quantitative modelling, and case study evaluations to provide actionable insights for policymakers and industry practitioners.
The findings of this review highlight the transformative potential of DT in addressing energy challenges, particularly in achieving the United Nations Sustainable Development Goal 7 [2]: universal access to affordable, reliable, and modern energy. By adopting digital innovations, energy providers can enhance operational efficiency, integrate renewable energy sources, and support community-based energy initiatives. The concept of swarm electrification exemplifies how decentralised approaches can complement centralised grids, ensuring scalability and adaptability to local needs.
Policy recommendations emphasise the need for financial incentives, capacity-building programmes, and regulatory frameworks to facilitate digital adoption. Investment in human capital is particularly critical, as skilled personnel are required to implement and manage complex digital systems. International cooperation and knowledge sharing are essential to ensure digital transformation efforts align with global sustainability goals.
In conclusion, DT represents a paradigm shift in energy systems, offering solutions to some of the sector’s most pressing challenges. Realising its full potential requires overcoming technical, financial, and institutional barriers. This review underscores the importance of a collaborative, multidisciplinary approach to harnessing the power of digital technologies for sustainable energy transitions.

Using asynchronous programming to improve computer simulation performance in energy systems

2025-04-07T08:55:35+03:00

Due to the progressing complexity of modern energy systems, the need to forecast energy consumption and generation, optimise processes and develop new technologies in the energy sector, analyse scenarios for the development of energy systems and elaborate a strategy for their development, modelling and simulation is of particular relevance in this industry. The growing need to improve the productivity of computer simulation in the energy industry is effectively addressed by utilising modern computer architectures and advanced software tools that provide acceleration for computationally intensive tasks. Research presented in this paper focuses on enhancing the performance of computationally intensive algorithms using the Thomas algorithm by employing modern asynchronous programming techniques. The work implements classical and develops and implements asynchronous computational algorithms of the sweep method with subsequent assessment of the time and efficiency of their execution for the order of systems of linear equations (SLAEs) up to 5 × 107. The program code was developed using Microsoft Visual Studio C++ and the standard template for asynchronous programming. The numerical experiments showed the possibility of increasing of the implementation speed of the asynchronous algorithm by 1.87–2.91 times. Research results correspond with the literature data and the results previously obtained by the authors in similar studies using alternative parallel programming software. In general, the results of this study determine the potential for further improvement and development of methods and technologies for parallel implementation of computational tasks using the Tridiagonal Matrix Algorithm. These approaches can be extended to developing various computer models of energy processes and systems based on the solution of SLAEs with tridiagonal matrices on computers with multiprocessor or multi-core architectures.

Reactive power compensation: a strategic approach to electrical efficiency at SEDA Huánuco

2025-04-03T09:07:55+03:00

In the company SEDA Huánuco, high consumption of reactive energy, attributed to the intensive use of motors, was identified, negatively affecting the power factor, which ranged between 0.73 and 0.77, and posing a significant challenge. The main objective of this study was to evaluate the relationship between reactive energy compensation and the electricity billing of SEDA Huánuco S. A. in Tingo María, 2024. The hypothetical-deductive method was applied with a quantitative approach and classified as applied research with a non-experimental design and a correlational-causal level of longitudinal scope. To obtain the data, the electric energy meter code No.74677479 of the company SEDA Huánuco was selected as a sample due to its high consumption of inductive reactive energy. Its behaviour during months of high and low demand was observed to determine the optimal amount of energy to compensate. After designing and simulating tariffs, it was identified that the MT3 tariff, with two energy charges and one power charge, was the most suitable for optimising billing costs in relation to the new power factor of 0.965. In conclusion, reactive energy compensation is significantly related to electricity billing, supported by the acceptance of the alternative hypothesis (H1), leading to the installation of a 73.5 kVAr capacitor bank in parallel at 440 V, distributed in three stages: one of 10.5 kVAr, two of 21 kVAr, and two of 10.5 kVAr.

Electric drive excitation control for improved performance of hot rolling mill finishing groups

2025-04-17T14:07:24+03:00

The study presented in the paper investigates rigorously the methods for enhancing the performance of interconnected electric drives within the finishing group of a hot rolling mill. In particular, it examines the impact of supply voltage fluctuations on drive precision and the interactions mediated by the rolled metal strip. A detailed analysis of the existing power supply system identifies the primary causes of dynamic deviations in drive operation.
A combined angular velocity control system is proposed to regulate the excitation of DC electric motors. The adaptive control strategy modulates magnetic flux during grid voltage drops, thereby reducing speed fluctuations and minimising tension inconsistencies in the inter-stand gaps. Unlike conventional systems that disregard supply voltage variations, the adaptive approach significantly improves the stability of the rolling process.
A mathematical model of the system, incorporating second- and third-order elastic couplings arising from both mechanical and electromagnetic interactions among the drives, is developed. Numerical simulations conducted in MATLAB/Simulink validate the efficiency of the proposed method. Optimal values for the relative reduction in the magnetic flux are determined to minimise discrepancies in drive currents and strip elongation. The results confirm that implementing adaptive control enhances system stability, improves rolling quality, and reduces the load on the power supply, thereby supporting its adoption in rolling mills operating under unstable grid voltage conditions.

Energetika

Title

Contents

Digital transformation in energy systems: a comprehensive review of AI, IoT, blockchain, and decentralised energy models

Using asynchronous programming to improve computer simulation performance in energy systems

Reactive power compensation: a strategic approach to electrical efficiency at SEDA Huánuco

Electric drive excitation control for improved performance of hot rolling mill finishing groups