Energy Systems Research

Multiple Regression Models for Statistical Study of the Dependence of Innovative Activities Related to Renewable Energy on Socio-Economic Factors

2025-04-10T10:14:24+03:00

A statistical study was conducted to explore how socio-economic factors influence innovative activities in the renewable energy sector based on the development of two multiple regression models. The study focuses on the Republic of Belarus, employing data from the World Bank and National Statistical Committee of the Republic of Belarus. Multiple socio-economic indicators were analyzed, including gross national income, renewable energy consumption, social contributions, and inflation rate. The first model considered the number of patent applications as the dependent variable, while the second focused on the share of shipped innovative products. Factors that have the greatest impact on innovation activity were identified. The determination coefficients for the multiple regression models were 0.863 and 0.907, respectively. Based on the developed models, we found dependencies, which show the ways to increase the level of innovation activities given the identified significant factors. The findings support the importance of renewable energy as a driver of innovation and highlight the need for strategic investment in energy and social infrastructure. The study also emphasizes the potential for integrating geographic information systems to optimize energy planning and support innovation development within the broader context of sustainable economic growth.

Towards the Applications of Blockchain in Distribution Networks: A Brief Review

2025-04-14T09:25:30+03:00

Modern energy systems are undergoing rapid changes, primarily driven by the rise of distributed energy resources (DERs) and microgrids, which enable decentralized power generation. Sophisticated management systems must be implemented as this transformation attempts to deliver safe, dependable, and efficient energy distribution. Blockchain technology offers a practical solution by enabling decentralized, transparent, and secure energy transfers. Because data cannot be altered or removed once entered into the blockchain, blockchain ensures transparency and confidence in energy exchanges through irreversible transactions. This study investigates the integration of blockchain technology into distribution networks, focusing on its application in peer-to-peer energy trading, microgrid management, and virtual power plants. An in-depth examination of blockchain applications in global marketplaces highlights important companies, regulatory environments, and technological advancements. As demonstrated in the study, energy management based on blockchain technology can optimize energy distribution, guarantee transparent financial transactions, and improve grid stability. To achieve its full potential, blockchain must address problems with scalability, regulatory compliance, and interoperability. This study also investigates how blockchain technology can be smoothly incorporated into distributed energy networks to enhance security, efficiency, and decentralization while resolving significant transaction processing and energy management challenges.

The Structure of Ontologies for the Scientific Knowledge Portal in the Field of Energy Research

2024-12-23T07:51:22+03:00

The paper examines the structure and composition of ontologies developed to fill the ontological knowledge portal in the field of energy research. The ontological knowledge portal has a scientific focus and is a component of the IT infrastructure of systems research in the energy sector. The portal is built using the advanced technology for constructing portals of scientific knowledge, developed at the A.P. Ershov Institute of Informatics Systems SB RAS in Novosibirsk. The structure of the ontologies meets the requirements of the technology employed to develop portals. The novelty of the work lies in the use of the methodology of systems approach to describe the subject area of energy research. The study takes account of hierarchical modeling used in the research on complex energy systems. The fractal approach applied in structuring the ontologies ensures a consistent increase in the degree of detail, starting with top-level meta-ontologies. Examples of the ontologies, reflecting knowledge in research areas, methods, and research objects, are presented.

Multi-Criteria Optimization of Multi-Energy System Sizing through a Genetic Algorithm

2025-01-16T09:19:40+03:00

Increasing the reliability and enhancing environmental and economic efficiency of power supply to remote consumers are essential for many regions. An effective solution is to establish multi-energy systems (MES) using renewable energy sources. Sizing the capacity of multi-energy systems (MES) has to meet a great number of criteria, which is related to numerous objectives. Accomplishing it is also complicated by the need for a detailed hourly assessment of the MES operation due to the stochastic nature of renewable energy. We propose an approach to optimizing the capacity of MES plants based on the NSGA-II genetic algorithm and the VIKOR multi-criteria method. The first stage involves using the algorithm to build a set of non-dominated MES configuration options, from which the most effective ones are chosen using the VIKOR method. The CRITIC method is employed to improve the objectivity of the criterion weight assessment, which relies on the analysis of alternatives based on criteria. The application of the approach is demonstrated in the remote settlement of Samarga, located in a moderate climate. The paper presents the best MES sizing solutions, which were selected according to the multi-criteria assessment using the VIKOR method. The objective functions of the resulting solutions correspond to the criteria weights determined using the CRITIC method.

The Identification of a Promising Research Topic in Applying Generative Artificial Intelligence in Petroleum Engineering

2025-04-04T05:06:58+03:00

This study aims to identify a promising research topic related to the use of generative artificial intelligence in the petroleum industry. It involves the collection of publications on generative artificial intelligence in the Scopus abstract database related to engineering and computer science, the systematization of the publications using the GSDMM algorithm, and the search for publications on the OnePetro platform that are close to the research objectives identified by Scopus. The analysis focused on 12 424 Scopus bibliometric records. The texts of the title and abstract fields were used to cluster the records. As a result, 21 clusters were obtained. For each cluster, stacked histograms of the difference in the occurrence of terms for this cluster and the other clusters were constructed using the program Scimago Graphica. A promising research topic could be adversarial attacks that compromise generative models by manipulating input data. This topic is underrepresented in petroleum literature, but has significant research potential because much has been written about it in publications from other subject areas. The findings of this study provide the petroleum industry professionals with the opportunity to broaden their search for publications on generative models and deepen their expertise in this research area.

A Review of Methods for Energy Security Research. Indicative Analysis

2025-02-05T05:59:17+03:00

In order to effectively model modern energy systems and the energy sector, it is essential to evaluate the existing body of research on this subject across a variety of entities ranging from localized to large-scale energy systems. Within the framework of the study on fuel and energy supply reliability and energy security, it is most interesting to consider and analyze the works modeling the interlinked operation of large energy systems in order to determine the indicators of energy security and reliability of fuel and energy supply to the consumer. The paper aims to examine various strategies for assessing the energy security through indicative analysis. To achieve the goal, several modern studies on the given topic were selected to make a constructive analysis on the efficiency and accuracy of the energy security assessment. The paper presents a review of existing approaches to assessing the level of energy security based on indicative analysis. National and international practices of applying indicators that reflect the level of energy security for various entities from regions to interstate associations are analyzed. The study notes that the indicative analysis is the most common and simple method for analyzing energy security. The analysis provides quantitative values of the energy security level and further, following the comparison of the obtained values of indicators with normative ones, qualitative conclusions are drawn. The main advantage of this method is that it yields numerical values of indicators, making it easy for decision makers to interpret them and track the dynamics of the energy sector development, identifying priority areas for modernization and advancement. The primary drawback of this approach to assessing the energy security level is its often superficial analysis, which fails to foster in-depth conclusions regarding operational conditions of energy systems.

Specific Features and Challenges of the Gas Industry in the Irkutsk Region

2025-04-28T09:51:04+03:00

The paper provides a brief description of the current level of the gas industry development in the Irkutsk region, featuring the structure and scale of its subsystems, resource potential, and natural gas production. Specific features and challenges of gas supply and transition to gas are considered, highlighting promising areas for the advancement of the gas industry in the region.