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Policy and Economic Analysis of Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 6287

Special Issue Editor

Dr. Piotr Kosowski

E-Mail Website
Guest Editor
Department of Petroleum Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: machine learning; artificial intelligence; statistical modeling; energy; petroleum economics; capital budgeting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Machine Learning (ML) and Artificial Intelligence (AI) across various industries has ushered in a new era driven by data, and the energy sector is no exception. The application of AI and ML in energy systems holds vast potential to accelerate the energy transition, creating a sophisticated coordination layer across energy generation, transmission, and consumption. These technologies can lead to significant cost reductions, enhanced performance, increased efficiency, and improved coordination and management of energy resources.

In addition to technological advancements, economic analysis plays a crucial role in understanding and optimizing the impact of AI and ML on the energy sector. This Special Issue focuses on both the policy and economic aspects of energy systems, emphasizing the transformative role of AI and ML. We invite submissions that explore innovative AI and ML applications, addressing various aspects of energy systems along with their economic implications. Topics of interest include, but are not limited to, the following:

  • Data science applications in the energy industry;
  • The role of AI in energy transformation and decarbonization;
  • Energy forecasting using ML techniques;
  • Development and optimization of smart grids;
  • Detection and management of anomalies and failures in energy systems;
  • Advanced energy modeling driven by AI;
  • The impact of AI and ML on energy geopolitics and security;
  • Enhancing renewable energy integration with AI and ML;
  • Maximizing energy efficiency through intelligent systems;
  • Intelligent prevention and detection of energy theft;
  • AI-based intelligent control of energy systems;
  • Innovations in intelligent energy generation;
  • Effective data collection and utilization in the energy sector;
  • Societal impacts of AI and ML in energy contexts;
  • Cutting-edge research involving AI and ML in energy;
  • Leveraging big data for energy industry advancements;
  • Designing materials, devices, and energy systems based on data insights;
  • The role of the Internet of Things (IoT) in energy management;
  • Applications of virtual reality in the energy sector;
  • The interplay of AI and human factors in energy industries;
  • Advances in energy robotics;
  • Economic analysis of AI and ML in energy systems;
  • Cost–benefit analysis of AI and ML applications in energy;
  • Financial models for AI-driven energy projects;
  • Market dynamics influenced by AI and ML in energy.

This Special Issue aims to provide a comprehensive overview of the latest developments in AI and ML applications within the energy sector, highlighting their potential to revolutionize policy-making, economic strategies, and technological advancements. We encourage researchers, policymakers, and industry experts to contribute their findings and insights to this exciting compilation.

Dr. Piotr Kosowski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • energy transformation
  • decarbonization
  • smart grids
  • energy forecasting
  • energy modeling
  • energy geopolitics
  • energy security
  • energy efficiency
  • energy theft
  • intelligent energy management
  • energy generation
  • Internet of Things
  • big data
  • social aspects
  • energy robotics
  • virtual reality
  • human factors
  • renewable energy
  • economic analysis
  • cost-benefit analysis
  • financial models

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Published Papers (8 papers)

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Research

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16 pages, 280 KiB  
Article
Harnessing a Renewable Resource for Sustainability: The Role of Geothermal Energy in Italy’s Business Sector
by Angelo Arcuri, Lorenzo Giolli and Cosimo Magazzino
Energies 2025, 18(7), 1590; https://doi.org/10.3390/en18071590 - 22 Mar 2025
Viewed by 361
Abstract
Addressing critical challenges such as climate change, environmental degradation, and resource depletion requires a swift transition to efficient and environmentally friendly energy solutions. Among these, geothermal energy is recognized for its dependability, low environmental impact, and versatility. This study investigates the role of [...] Read more.
Addressing critical challenges such as climate change, environmental degradation, and resource depletion requires a swift transition to efficient and environmentally friendly energy solutions. Among these, geothermal energy is recognized for its dependability, low environmental impact, and versatility. This study investigates the role of geothermal energy in Italy’s business sector, examining its impact on companies and social perception. It specifically evaluates how communicating geological, hydrological, and atmospheric risks associated with geothermal projects influences firms’ likelihood of experiencing social acceptance challenges. Additionally, this research quantifies the impact of geothermal energy adoption on companies’ energy costs and CO2 emissions. The analysis further explores the long-term implications of expanding the use of this renewable resource through sensitivity analysis, focusing on its effects on emissions and costs. The findings indicate that firms communicating geothermal-related risks are less likely to experience social acceptance challenges compared to those that do not. Moreover, this study shows that the use of geothermal energy positively impacts firms’ business and environmental performance by reducing energy costs and CO2 emissions. Sensitivity analysis demonstrates that increasing the proportion of geothermal energy usage amplifies these benefits, thereby enhancing firms’ competitiveness. This research provides a comprehensive framework for promoting geothermal energy integration in business operations, offering valuable insights to support the global shift toward sustainable energy systems. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
15 pages, 16970 KiB  
Article
The Development of Photovoltaics in the Countries of Central and Eastern Europe in the Context of Regulatory Changes: A Case Study of the Czech Republic and Poland
by Maciej Dzikuć, Arkadiusz Piwowar and Maria Dzikuć
Energies 2025, 18(4), 817; https://doi.org/10.3390/en18040817 - 10 Feb 2025
Viewed by 562
Abstract
The Czech Republic and Poland are struggling with problems related to the development of photovoltaics. Both analyzed countries had periods of dynamic development of this renewable energy source (RES). However, neither the Czech Republic nor Poland have developed mechanisms that would lead to [...] Read more.
The Czech Republic and Poland are struggling with problems related to the development of photovoltaics. Both analyzed countries had periods of dynamic development of this renewable energy source (RES). However, neither the Czech Republic nor Poland have developed mechanisms that would lead to the stable development of photovoltaic installations in the long term. The analyses presented in this article demonstrate the implementation of extreme solutions by these countries, which led either to stagnation in the development of photovoltaics or to an excessive increase in the installed capacity, including RES, which then had to be stopped due to, for example, the failure of the power grids to match the increasing capacity. This article conducted research based on secondary data and using the Foresight method. The aim of this manuscript is to present the conditions related to the development of photovoltaics in the Czech Republic and Poland. This article also points to the barriers limiting the development of this type of RES and the potential of solutions related to, e.g., energy storage, which will allow for maintaining stable development of photovoltaics in the future and will prevent excessive overloading of power grids. The research results indicate that in the context of further development of photovoltaics in the study area, what is important are, e.g., changes in legal regulations and financial incentives that will enable the development of micro-installations within energy communities to a greater extent, including co-financing for energy storage. Other factors were also noted, including interconnection capacity within the energy systems of these countries, as well as externally. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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16 pages, 4954 KiB  
Article
Prediction Accuracy of Stackelberg Game Model of Electricity Price in Smart Grid Power Market Environment
by Zhichao Zhang, Xue Li, Yanling Zhao, Zhaogong Zhang and Bin Li
Energies 2025, 18(3), 501; https://doi.org/10.3390/en18030501 - 22 Jan 2025
Viewed by 703
Abstract
With the deepening of power market reform and the increasingly fierce competition in the power market, the accurate prediction of electricity price has become an important demand for power market participants to make scientific decisions, optimize resource allocation, and reduce risks. Electricity price [...] Read more.
With the deepening of power market reform and the increasingly fierce competition in the power market, the accurate prediction of electricity price has become an important demand for power market participants to make scientific decisions, optimize resource allocation, and reduce risks. Electricity price forecast can provide a key reference for the power market, help market participants make wise decisions, promote competition and efficient operation and cope with complex market fluctuations, provide a scientific basis for various entities to optimize resource allocation, reduce risks and improve benefits, and promote the sustainable development of the power industry. This study presents a dynamic retail price prediction method for smart grid based on the Stackelberg game model. Firstly, the correlation test is used to verify the strong correlation between electric load and electricity price. Secondly, the parameters of the Stackelberg model are determined, and the load and electricity price are tested using the white noise test. Finally, by comparing the BP neural network model and quantifying the model parameters, the superiority of the model is verified. The results show that the Stackelberg game model has higher prediction accuracy than the BP neural network model in electricity price prediction. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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15 pages, 576 KiB  
Article
Renewable Energy Expansion in West Pomerania: Integrating Local Potential with Global Sustainability Goals
by Jarosław Jaworski and Jakub Dowejko
Energies 2025, 18(1), 103; https://doi.org/10.3390/en18010103 - 30 Dec 2024
Viewed by 696
Abstract
The expansion of renewable energy sources (RES) is essential to achieving regional sustainability in alignment with global climate goals. This study investigates the dynamics and projected growth of RES in West Pomerania, Poland, a region with significant potential due to its geographical characteristics [...] Read more.
The expansion of renewable energy sources (RES) is essential to achieving regional sustainability in alignment with global climate goals. This study investigates the dynamics and projected growth of RES in West Pomerania, Poland, a region with significant potential due to its geographical characteristics and supportive policy frameworks. Historical data from 2010 to 2023 were used to perform a time series analysis that evaluated the annual growth rate (AGR) of various RES technologies, including wind, solar, biomass, and biogas. The analysis revealed a consistent upward trend in RES capacity, particularly in wind and solar energy, demonstrating effective resource mobilisation in the region. Subsequently, a forecasting model was employed to project the growth of the RES capacity through 2033 based on historical trends and technological advancements. The results indicate significant anticipated increases in RES capacity, highlighting West Pomerania’s potential to reduce its reliance on fossil fuels. This growth supports increased energy security and environmental sustainability. This study addresses a notable gap in the literature by linking regional renewable energy development with broader policy frameworks, such as the European Green Deal, and exploring the specific challenges of grid integration and economic disparities in the context of local energy transitions. These findings highlight the importance of sustained investment and policy support to scale renewable infrastructure while aligning regional initiatives with international sustainability goals. By bridging this gap, this study concludes that the West Pomerania strategy can serve as a model for other regions aiming to enhance their renewable energy portfolios and effectively meet the climate goals of the EU. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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23 pages, 6166 KiB  
Article
From Fossil Fuels to Renewables: Clustering European Primary Energy Production from 1990 to 2022
by Piotr Kosowski
Energies 2024, 17(22), 5596; https://doi.org/10.3390/en17225596 - 9 Nov 2024
Viewed by 1455
Abstract
This study examines the structural shifts in primary energy production across European countries from 1990 to 2022, focusing on changes in energy sources and their implications for energy security and sustainability. Set against a backdrop of evolving geopolitical landscapes, economic pressures, and climate [...] Read more.
This study examines the structural shifts in primary energy production across European countries from 1990 to 2022, focusing on changes in energy sources and their implications for energy security and sustainability. Set against a backdrop of evolving geopolitical landscapes, economic pressures, and climate policies, including significant recent impacts such as the conflict in Ukraine, this research highlights the critical importance of a stable and diversified energy supply. The analysis utilizes the k-means clustering method, examining countries for which data are available in the Eurostat database and considering primary energy sources as defined by the Standard International Energy Product Classification (SIEC), including solid fossil fuels, natural gas, crude oil, nuclear energy, renewable energy sources, peat, and non-renewable waste. By categorizing European nations into clusters based on their energy production profiles, the study reveals substantial transitions from fossil fuel-based systems to those increasingly dominated by renewable energy sources. While some countries have made significant progress in integrating renewables, others remain heavily dependent on traditional energy sources such as coal and natural gas. The findings underscore the growing role of natural gas as a bridge fuel and the relatively stable contribution of nuclear energy in certain regions. A key outcome is the observed disparity between energy production and consumption across Europe, with many large economies facing a persistent deficit in domestic energy production, resulting in a high reliance on energy imports, particularly of natural gas and oil. This dependency poses significant challenges to energy security, especially given recent geopolitical disruptions and market fluctuations. The paper also discusses the environmental implications of these energy trends, emphasizing the vital role of renewable energy in achieving the European Union’s decarbonization goals. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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Review

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26 pages, 740 KiB  
Review
A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations
by Hassan Qudrat-Ullah
Energies 2025, 18(9), 2239; https://doi.org/10.3390/en18092239 - 28 Apr 2025
Viewed by 126
Abstract
The growing global energy demand and the pursuit of sustainability highlight the transformative potential of artificial intelligence (AI) and machine learning (ML) in energy systems. This thematic review explores their applications in energy generation, transmission, and consumption, emphasizing their role in optimizing renewable [...] Read more.
The growing global energy demand and the pursuit of sustainability highlight the transformative potential of artificial intelligence (AI) and machine learning (ML) in energy systems. This thematic review explores their applications in energy generation, transmission, and consumption, emphasizing their role in optimizing renewable integration, enhancing operational efficiency, and enabling data-driven decision-making. By employing a thematic approach, this study categorizes and analyzes key challenges and opportunities, including economic considerations, technological advancements, and social implications. While AI/ML technologies offer significant benefits, their adoption in developing nations faces challenges, such as high upfront costs, skill shortages, and infrastructure limitations. Addressing these barriers through capacity building, international collaboration, and adaptive policies is critical to realizing the equitable and sustainable integration of AI/ML in energy systems. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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18 pages, 2790 KiB  
Review
Analysis of Energy System Transformations in the European Union
by Agata Jaroń and Anna Borucka
Energies 2024, 17(23), 6181; https://doi.org/10.3390/en17236181 - 7 Dec 2024
Viewed by 921
Abstract
Achieving climate neutrality has become an environmental goal for all European Union (EU) Member States. Thanks to numerous projects and subsidies, EU countries are able to achieve the goal of decarbonizing energy sources. The aim of the presented study is to assess the [...] Read more.
Achieving climate neutrality has become an environmental goal for all European Union (EU) Member States. Thanks to numerous projects and subsidies, EU countries are able to achieve the goal of decarbonizing energy sources. The aim of the presented study is to assess the validity of Member States’ actions to reduce CO2 emissions based on data provided by Eurostat. The presented study combines, using regression, data on financial outlays dictated by environmental protection, the share of renewable energy, and total CO2 emissions in individual Member States. This study has shown that, despite differences in the use of energy sources, Member States are able to achieve economic growth in symbiosis with the environment. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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28 pages, 8401 KiB  
Review
Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction
by Pavlos Nikolaidis
Energies 2024, 17(20), 5219; https://doi.org/10.3390/en17205219 - 20 Oct 2024
Viewed by 1075
Abstract
With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the [...] Read more.
With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems)
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