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Policy and Economic Analysis of Energy Systems: 2nd Edition
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Policy and Economic Analysis of Energy Systems: 2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 10 June 2026 | Viewed by 6001

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: ML; AI; statistical modeling; energy; petroleum economics; capital budgeting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to share the success of our Special Issue on “Policy and Economic Analysis of Energy Systems”.

In the first volume, we successfully published 11 papers:

https://www.mdpi.com/journal/energies/special_issues/7H07LBVUH0

I am now preparing to launch the second volume of this Special Issue, entitled “Policy and Economic Analysis of Energy Systems: 2nd Edition”.

The integration of machine learning (ML) and artificial intelligence (AI) into 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 for energy resources and their enhanced performance, increased efficiency, and improved coordination and management.

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;
  • The development and optimization of smart grids;
  • The 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;
  • The 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;
  • The 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 250 words) can be sent to the Editorial Office for assessment.

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

  • AI
  • ML
  • energy transformation
  • decarbonization
  • smart grids
  • energy forecasting
  • energy modeling
  • energy geopolitics
  • energy security
  • energy efficiency
  • energy theft
  • intelligent energy management
  • energy generation
  • big data
  • social aspects
  • energy robotics
  • virtual reality
  • human factors
  • renewable energy
  • economic analysis
  • cost–benefit analysis
  • financial models

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Related Special Issue

Published Papers (5 papers)

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Research

35 pages, 7939 KB  
Article
Techno-Enviro-Economic Assessment of Long-Term Strategic Capacity Expansion for Dubai’s Clean Energy Future Using PLEXOS
by Ahmed Yousry and Mutasim Nour
Energies 2026, 19(1), 173; https://doi.org/10.3390/en19010173 - 28 Dec 2025
Viewed by 1197
Abstract
With global energy systems shifting toward sustainable solutions, Dubai faces the challenge of meeting rising energy needs while minimizing environmental impacts. This study explores long-term (LT) strategic planning for Dubai’s power sector through a techno-environmental–economic lens. Using PLEXOS® modelling software (Version 9.20.0001) [...] Read more.
With global energy systems shifting toward sustainable solutions, Dubai faces the challenge of meeting rising energy needs while minimizing environmental impacts. This study explores long-term (LT) strategic planning for Dubai’s power sector through a techno-environmental–economic lens. Using PLEXOS® modelling software (Version 9.20.0001) and official data from Dubai’s main utility provider, a comprehensive model examines medium- and LT energy pathways. The analysis identifies solar photovoltaic (PV) technology as central to achieving Dubai’s goal of 100% clean energy by 2050. It also highlights the need to cut emissions from natural gas (NG) infrastructure, targeting a goal of 14.5% retirement of NG energy generation capacities by the mid-century. Achieving zero-emission goals will require complementary technologies such as carbon capture (CC), nuclear energy, and energy storage as part of a broader decarbonization strategy. This study further assesses the economic effects of climate policy, showing that moderate carbon pricing could increase the Levelized Cost of Energy (LCOE) by an average of 6% across the forecast horizon. These findings offer valuable guidance for decision-makers and stakeholders, particularly the Dubai Electricity and Water Authority (DEWA), in advancing a carbon-neutral energy system. By 2050, Dubai’s total installed generation capacity is projected to reach 53.3 GW, reflecting the scale of transformation needed to meet its clean energy ambitions. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)
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21 pages, 2017 KB  
Article
Uncovering CO2 Drivers with Machine Learning in High- and Upper-Middle-Income Countries
by Cosimo Magazzino, Umberto Monarca, Ernesto Cassetta, Alberto Costantiello and Tulia Gattone
Energies 2025, 18(21), 5552; https://doi.org/10.3390/en18215552 - 22 Oct 2025
Cited by 2 | Viewed by 928
Abstract
Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different [...] Read more.
Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)
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34 pages, 7348 KB  
Article
Unsupervised Profiling of Operator Macro-Behaviour in the Italian Ancillary Service Market via Stability-Driven k-Means
by Mahmood Hosseini Imani and Atefeh Khalili Param
Energies 2025, 18(20), 5446; https://doi.org/10.3390/en18205446 - 15 Oct 2025
Cited by 1 | Viewed by 849
Abstract
The transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit [...] Read more.
The transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit operators’ macro-behaviour in the Italian Ancillary Services market (MSD). Focusing on the Northern zone (NORD) during the pivotal period of 2022–2024, a stability-driven k-means clustering framework is applied to a dataset of capacity-normalized features from the day-ahead market (MGP), intraday market (MI), and MSD. The number of clusters is determined using the Gap Statistic with a 1-SE criterion and validated with bootstrap stability (Adjusted Rand Index), resulting in a robust and reproducible 13-group taxonomy. The use of up-to-date data (2022–2024) enabled a unique investigation into post-2021 market phenomena, including the effects of geopolitical events and extreme price volatility. The findings reveal clear operator-coherent archetypes ranging from units that mainly trade in the day-ahead market to specialists that monetize flexibility in the MSD. The analysis further highlights the dominance of thermoelectric and dispatchable hydro technologies in providing ancillary services, while illustrating varying degrees of responsiveness to price signals. The proposed taxonomy offers regulators and policymakers a practical tool to identify inefficiencies, monitor concentration risks, and inform future market design and policy decisions. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)
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14 pages, 678 KB  
Article
Trust, Equity, Transparency and Inclusion in Nuclear Energy Governance: Empirical Synthesis of the Q-NPT Framework
by Hassan Qudrat-Ullah
Energies 2025, 18(20), 5423; https://doi.org/10.3390/en18205423 - 15 Oct 2025
Viewed by 1016
Abstract
The Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework offers a governance model for nuclear energy that foregrounds trust, equity, transparency, and stakeholder inclusion. This paper provides an empirical synthesis of Q-NPT by integrating quantitative and qualitative evidence from recent nuclear energy studies and [...] Read more.
The Qudrat-Ullah Nuclear Peace and Trust (Q-NPT) framework offers a governance model for nuclear energy that foregrounds trust, equity, transparency, and stakeholder inclusion. This paper provides an empirical synthesis of Q-NPT by integrating quantitative and qualitative evidence from recent nuclear energy studies and situating the framework within global policy contexts. The findings indicate that legitimacy in nuclear governance depends not only on technical and regulatory compliance but also on social trust, distributive fairness, and active stakeholder inclusion. The analysis further demonstrates Q-NPT’s applicability to emerging technologies—including microreactors and blockchain-based fuel management—highlighting its adaptability to contemporary governance challenges. Together, these insights advance Q-NPT from conceptual articulation toward an evidence-informed, socially robust foundation for legitimate and ethical nuclear energy governance. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)
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29 pages, 3935 KB  
Article
China’s Smart Energy Policy Evaluation Based on Policy Modelling Consistency Index
by Rongjiang Cai, Tao Zhang, Xi Wang, Shufang Zhao, Hang Yang and Qixiang Geng
Energies 2025, 18(20), 5339; https://doi.org/10.3390/en18205339 - 10 Oct 2025
Cited by 1 | Viewed by 1464
Abstract
Against the backdrop of China’s “dual carbon” goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. Traditional qualitative evaluations struggle with subjectivity; therefore we apply the quantitative PMC Index to systematically assess smart energy policies. This research systematically analyzes 16 [...] Read more.
Against the backdrop of China’s “dual carbon” goals of achieving carbon peaking by 2030 and carbon neutrality by 2060. Traditional qualitative evaluations struggle with subjectivity; therefore we apply the quantitative PMC Index to systematically assess smart energy policies. This research systematically analyzes 16 representative Chinese smart energy policies using the PMC model, combined with content analysis. An integrated analytical framework was constructed to examine PMC applications across different energy policy fields. Results demonstrate that China’s smart energy policies achieved excellent performance, with an average PMC score of 7.48 out of 10. Furthermore, 68.75% of policies (11 out of 16) reached the ‘excellent’ level (PMC ≥ 8.0), with Policy “P6” achieving the highest score of 8.88 points. Top-performing policies exhibited strong strategic coordination, clear objectives, and comprehensive supporting measures. The findings reveal a well-structured policy cluster with clear objectives and strong coordination. This mature policy package provides a solid institutional foundation for China’s energy system transformation toward smart and green development, offering valuable insights for energy policy optimization and quantitative assessment methodology improvement. Full article
(This article belongs to the Special Issue Policy and Economic Analysis of Energy Systems: 2nd Edition)
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