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Transformation of Energy Markets: Description, Modeling of Functioning Mechanisms and Determining Development Trends – Second 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: closed (31 March 2024) | Viewed by 16699

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Guest Editor
Department of Statistics and Econometrics, Faculty of Management and Economics, Gdansk University of Technology, 80-233 Gdansk, Poland
Interests: social and economic convergence; sustainable development; renewable and sustainable energy; spatial statistics; composite indicators; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue addresses the global energy transformation and related changes in the functioning of energy markets. It also addresses the issue of the rapidly growing renewable energy sector and its increasingly strong links with the electricity market and the primary fuel market. Currently, the renewable energy sector is an increasingly large area of the modern economy in terms of business investment, the share of the sector’s output in GDP, energy production, and research and development. Undoubtedly, the functioning of the RE sector has had a significant impact on the functioning of the global electricity market and the primary fuels market. All of this has necessitated a new and completely different view of the changes taking place in these markets, especially in terms of electricity and primary fuel prices, as well as their institutional basis. The question arises as to what development trends will occur in the various energy markets, and consequently, how to create energy policy and carry out the energy transition, both at the national and international levels.

Topics of interest for publication include:

  • Energy transformation, energy markets;
  • Primary fuels, electricity, and renewable energy markets;
  • Institutional determinants of development of energy markets;
  • Current state and development prospects for energy markets;
  • Prosumers, low-emission economy;
  • Modeling dependencies on energy markets;
  • Forecasting prices on energy markets.

Prof. Dr. Michał Bernard Pietrzak
Dr. Marta Kuc-Czarnecka
Guest Editors

Manuscript Submission Information

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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

  • energy transformation
  • sustainable development of the energy markets
  • renewable energy sector
  • primary fuels market
  • electricity market
  • energy regulation
  • energy forecasting

Related Special Issue

Published Papers (10 papers)

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26 pages, 6134 KiB  
Article
Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology
by Enrico Maria Carlini, Corrado Gadaleta, Michela Migliori, Francesca Ferretti, Riccardo Vailati, Andrea Venturini and Cinzia Puglisi
Energies 2024, 17(12), 2824; https://doi.org/10.3390/en17122824 - 8 Jun 2024
Viewed by 549
Abstract
Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power [...] Read more.
Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained. Full article
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<p>Examples of the <span class="html-italic">k</span>-th section/border marginal cost and benefit curves resulting from the iterative process for two different scenarios for two different time horizons.</p>
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<p>Workflow of the iterative process.</p>
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<p>Demand trend for Italy (TWh) in the reference scenarios for 2030 and 2040.</p>
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<p>Generation mix for Italy (GW) in the reference scenarios for 2030 and 2040.</p>
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<p>Storage energy capacity for Italy (GWh) in the reference scenarios for 2030 and 2040.</p>
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<p>Bidding zones of the Italian power system: internal sections and external borders under study.</p>
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<p>Constraints on the maximum additional transmission capacity at each border under study.</p>
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<p>Cost-effective transmission capacity increases [MW] for each section/border of the Italian power system in each considered scenario for the 2030 horizon.</p>
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<p>Least regret application for 2030 horizon: worst regret for each option in dotted red circle.</p>
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<p>Cost-effective transmission capacity increases [MW] for each section/border of the Italian power system in each considered scenario for the 2040 horizon.</p>
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<p>Least regret application for the 2040 horizon: worst regret for each option in dotted red circle.</p>
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<p>Expected power flows (TWh) resulting from the implementation of identified additional target capacity on the base case in 2030 and 2040 contrasting scenarios. (<b>a</b>) LT 2030; (<b>b</b>) FF55 2030; (<b>c</b>) LT 2040; (<b>d</b>) DE 2040.</p>
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<p>Sensitivity analyses results in the 2030 policy scenario: additional transmission capacity needs in internal market sections [MW] if additional capacity external borders (Sensitivity 1) and additional storage capacity (Sensitivity 2) are reduced.</p>
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<p>Sensitivity in storage capacity: cases analyzed.</p>
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<p>Sensitivity in storage capacity: computed indicators.</p>
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23 pages, 1633 KiB  
Article
Energy Transformation Development Strategies: Evaluation of Asset Conversion in the Regions
by Mantas Svazas and Valentinas Navickas
Energies 2024, 17(7), 1612; https://doi.org/10.3390/en17071612 - 28 Mar 2024
Viewed by 718
Abstract
Manifestations of energy transformation are visible throughout the developed world. As the threat to the survival of humanity arises, the countries of the world are starting to take faster and more specific actions to transform the energy sector. One of the energy transformation [...] Read more.
Manifestations of energy transformation are visible throughout the developed world. As the threat to the survival of humanity arises, the countries of the world are starting to take faster and more specific actions to transform the energy sector. One of the energy transformation strategies is the decentralized development of the energy system in the regions. This concept is especially relevant at this time, when centralized sources of energy production and supply are becoming the target of physical and cyber attacks. The purpose of this article is to form theoretical assumptions for the smooth implementation of the decentralization of the energy system. This article aims to remove obstacles to short-term energy transformation. The novelty of this article is related to emphasizing the role of biomass cogeneration in achieving energy system efficiency and greening. Mathematical modeling based on RSM is used in the article. The established factors of the market structure revealed that the efficiency of energy production is based on the use of cogeneration and the markets for raw materials and energy can be attributed to different types. The results of this study showed that the optimal combination of biomass cogeneration can ensure competitive energy production. This article is relevant because it offers transitional solutions until adequate hydrogen utilization and energy storage solutions are developed. Full article
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<p>Typical roadmap of bioenergy production [<a href="#B33-energies-17-01612" class="html-bibr">33</a>].</p>
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<p>Decentralized regional biomass market structure.</p>
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<p>Combined effect of electricity (EP) and heat (TP) on electricity price (Y).</p>
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<p>Contour plot of the effect of electricity (EP) and heat (TP) on electricity price (Y).</p>
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<p>Optimal parameters of electricity (EP), heat (TP), and electricity price (Y).</p>
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35 pages, 2190 KiB  
Article
The European Market for Guarantees of Origin for Green Electricity: A Scenario-Based Evaluation of Trading under Uncertainty
by Alexander Wimmers and Reinhard Madlener
Energies 2024, 17(1), 104; https://doi.org/10.3390/en17010104 - 23 Dec 2023
Cited by 1 | Viewed by 2064
Abstract
Guarantees of Origin (GOs) were introduced in order to enhance transparency about the origin of green electricity produced in Europe, and to deliberately empower end-consumers to participate in the sustainable energy transition. The separation of electricity and the GO trade has resulted in [...] Read more.
Guarantees of Origin (GOs) were introduced in order to enhance transparency about the origin of green electricity produced in Europe, and to deliberately empower end-consumers to participate in the sustainable energy transition. The separation of electricity and the GO trade has resulted in a prosperous GO market that, however, has been characterized by non-transparency and opportunistic behavior. Historic price development has been opaque and can therefore not be used to forecast future GO prices. This paper, firstly, provides a thorough overview of the European GO market and an analysis of the historic price development; secondly, it proposes a model, the first of its kind, for determining future price developments of European GOs for different renewable energy technologies in different countries up to 2040. For household consumers, GO price determination is based on willingness-to-pay estimates from the literature, whereas for non-household consumers, the model introduces a novel approach to determine the willingness to pay for green electricity. Four different scenarios are considered (Status Quo, Sustainable Development, Full Harmonization, and Ideal Development) and annual GO data are used. The findings indicate that GO prices can be expected to increase on average in the next years, with prices ranging from 1.77 to 3.36 EUR/MWh in 2040. Sensitivity analysis shows that ‘WTP percentages’ have the highest influence on GO prices. It can be concluded that future GO prices will remain challenging to predict, even with the support of sophisticated models, due to the expected supply and demand-driven market growth affecting the market equilibrium prices for different GOs in different countries. Full article
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<p>Flowchart of the structure of the analysis, showing the analytical steps taken and corresponding findings. Abbreviations used: GO = guarantee of origin; ATP = ability to pay; WTP = willingness to pay; AIB = Association of Issuing Bodies.</p>
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<p>GO Prices for Nordic Hydro Futures in 2021 and 2022 (<b>left</b>), Aggregated Prices for Different Technologies (<b>right</b>). Source: Own illustration, based on Argus Media [<a href="#B21-energies-17-00104" class="html-bibr">21</a>] and Robert [<a href="#B20-energies-17-00104" class="html-bibr">20</a>].</p>
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<p>Estimation of relative maximum willingness to pay <span class="html-italic">WTP<sub>max</sub></span><sub>,<span class="html-italic">rel</span></sub> for exemplary non-household consumers A, B, and C. Abbreviations used: ATP = ability to pay, WTP = willingness to pay.</p>
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<p>Determination of the equilibrium price <span class="html-italic">P</span>*<span class="html-italic"><sub>o</sub></span><sub>,<span class="html-italic">t</span>,<span class="html-italic">p</span></sub>.</p>
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<p>Data and detailed modeling workflow diagram. Abbreviations used: EECS = European Energy Certificate System; GO = guarantee of origin; ATP = ability to pay; WTP = willingness to pay. Data sets are given according to their reference number [<a href="#B46-energies-17-00104" class="html-bibr">46</a>,<a href="#B63-energies-17-00104" class="html-bibr">63</a>,<a href="#B72-energies-17-00104" class="html-bibr">72</a>,<a href="#B73-energies-17-00104" class="html-bibr">73</a>,<a href="#B74-energies-17-00104" class="html-bibr">74</a>,<a href="#B75-energies-17-00104" class="html-bibr">75</a>,<a href="#B76-energies-17-00104" class="html-bibr">76</a>,<a href="#B77-energies-17-00104" class="html-bibr">77</a>,<a href="#B78-energies-17-00104" class="html-bibr">78</a>].</p>
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<p>Comparison of issued GO volumes, by scenario, 2020–2040. Abbreviations used: GO = guarantee of origin; p.a. = per year; RES = renewable energy sources.</p>
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<p>Development of average prices over all technologies and all countries for GOs, by scenario, 2020–2040. Abbreviations used: GO = guarantee of origin.</p>
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<p>Comparison of price developments per technology for all four scenarios. Abbreviations used: GO = guarantee of origin; RES = renewable energy sources.</p>
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<p>Results from the sensitivity analysis. Abbreviations used: RES = Renewable Energy Source; WTP = willingness to pay; LCOE = Levelized Cost of Electricity.</p>
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26 pages, 4836 KiB  
Article
AI-Based Computational Model in Sustainable Transformation of Energy Markets
by Izabela Rojek, Adam Mroziński, Piotr Kotlarz, Marek Macko and Dariusz Mikołajewski
Energies 2023, 16(24), 8059; https://doi.org/10.3390/en16248059 - 14 Dec 2023
Cited by 2 | Viewed by 1865
Abstract
The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to [...] Read more.
The ability of artificial intelligence (AI) to process large amounts of data, analyze complex patterns, and make predictions is driving innovation in the energy sector and transformation of energy markets. It helps optimize operations, improve efficiency, reduce costs, and accelerate the transition to cleaner and more sustainable energy sources. AI is playing an increasingly important role in transforming energy markets in various aspects of the industry in different ways, including smart grids and energy management, renewable energy integration, energy forecasting and trading, demand response and load management, energy efficiency and conservation, maintenance and asset management, energy storage optimization, carbon emission reduction, market analytics and risk management, exploration and production, regulatory compliance, and safety. The aim of this article is to discuss our own AI-based computational model in sustainable transformation of energy markets and to lay the foundations for further harmonious development based on a computational (AI/ML-based) models, with particular reference to current limitations and priority directions for further research. Such an approach may encourage new research for the practical application of AI algorithms in critical domains of the energy sector. Full article
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<p>Energy systems: (<b>a</b>) Centralized system—power generation concentrated in a small number of locations in the country: Extensive transmission and distribution network—large energy losses. Failure of source or grid affects a significant area of the network. Rising system maintenance costs = increasingly higher bills, (<b>b</b>) Distributed model—collection of microgrids based on energy cooperatives: Energy consumers can also be energy producers (prosumers). Energy generated locally is consumed locally. Failure of a single microgrid does not disrupt the system [<a href="#B26-energies-16-08059" class="html-bibr">26</a>].</p>
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<p>Example of simulation implementation in the calculator developed in the Renaldo project [<a href="#B28-energies-16-08059" class="html-bibr">28</a>,<a href="#B31-energies-16-08059" class="html-bibr">31</a>].</p>
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<p>Example hourly balance of an energy cooperative in one week in winter (<b>a</b>) and summer (<b>b</b>) [<a href="#B32-energies-16-08059" class="html-bibr">32</a>].</p>
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<p>Example hourly balance of an energy cooperative in one week in winter (<b>a</b>) and summer (<b>b</b>) [<a href="#B32-energies-16-08059" class="html-bibr">32</a>].</p>
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<p>Model of the energy cooperatives used in the article.</p>
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<p>Example parameter values (Prosumer 1, 30 min).</p>
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<p>Development of AI in computational modeling of sustainable transformation of energy markets [<a href="#B33-energies-16-08059" class="html-bibr">33</a>].</p>
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<p>The structure of the artificial neural network used in this study as multilayer perceptron (MLP) along with the input and output parameters.</p>
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<p>The structure of the artificial neural network used in this study as convolutional neural network (CNN) along with the input and output parameters.</p>
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<p>Intelligent energy management, ensuring optimum use of different sources of electricity.</p>
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<p>Outcomes of cross-validation and performance measures.</p>
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21 pages, 7852 KiB  
Article
The Energy Retrofit Impact in Public Buildings: A Numerical Cross-Check Supported by Real Consumption Data
by Vincenzo Ballerini, Bernadetta Lubowicka, Paolo Valdiserri, Dorota Anna Krawczyk, Beata Sadowska, Maciej Kłopotowski and Eugenia Rossi di Schio
Energies 2023, 16(23), 7748; https://doi.org/10.3390/en16237748 - 24 Nov 2023
Cited by 1 | Viewed by 782
Abstract
In the framework of reducing carbon dioxide emissions and energy consumption, the energy retrofit of existing buildings plays a significant role and is often supported by numerical analyses of the planned activities and expected results. This study analyses a public building (a kindergarten) [...] Read more.
In the framework of reducing carbon dioxide emissions and energy consumption, the energy retrofit of existing buildings plays a significant role and is often supported by numerical analyses of the planned activities and expected results. This study analyses a public building (a kindergarten) located in Bialystok (Poland) and aims to determine the building’s energy performance prior to and after thermal modernization. The building was examined by employing two different software packages, Audytor OZC 7.0 Pro and Trnsys 18. The thermal efficiency improvement applied to the renovated building in Bialystok was also analyzed by virtually locating the building in Bologna (Italy). Moreover, a comfort analysis focused on the classrooms of the kindergarten was carried out employing Trnsys. As a novelty, in the analysis, particular attention is paid to ventilation losses and to the influence of envelope elements properties on the building energy demand. The results arising from analyses were compared to real consumption data for the heating season. The results obtained from the two software programs display excellent agreement, and they also match the real consumption data if the heating demand is considered, while some differences arise when the cooling demand is considered. Full article
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<p>Photo of the building located in Bialystok (<b>a</b>) and location (red dot) in Bialystok municipality [<a href="#B28-energies-16-07748" class="html-bibr">28</a>] (<b>b</b>) (photo: D. Krawczyk).</p>
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<p>Photo of the building located in Bialystok (<b>a</b>) and location (red dot) in Bialystok municipality [<a href="#B28-energies-16-07748" class="html-bibr">28</a>] (<b>b</b>) (photo: D. Krawczyk).</p>
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<p>Layout of the building: ground floor (<b>a</b>) and first floor (<b>b</b>).</p>
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<p>3D view of the modeled building using Google SketchUp (v.15) software.</p>
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<p>Hourly (<b>a</b>) and monthly mean (<b>b</b>) dry bulb temperature and monthly irradiance on the horizontal (<b>c</b>) obtained from PVGIS database [<a href="#B31-energies-16-07748" class="html-bibr">31</a>].</p>
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<p>Average monthly dry bulb temperature.</p>
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<p>Layout of the heating system (<b>a</b>) and focus on the weather data processor (<b>b</b>) as built in Trnsys.</p>
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<p>Monthly thermal energy demand comparison, cases S1–S2 (<b>a</b>) and S3–S4 (<b>b</b>) employing Trnsys.</p>
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<p>Total design heat load, cases S1–S4.</p>
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<p>Monthly thermal energy demand comparison, cases S1–S2 (<b>a</b>) and S3–S4 (<b>b</b>) employing Audytor OZC 7.0 Pro.</p>
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<p>Total loss of thermal energy and ventilation for all analyzed cases (S1–S4) employing Audytor OZC 7.0 Pro.</p>
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<p><span class="html-italic">PMV</span> (in (<b>a</b>,<b>b</b>)), and <span class="html-italic">PPD</span> (in (<b>c</b>,<b>d</b>)) for cases S1 (<b>left</b>) and S2 (<b>right</b>) respectively, for five classrooms (CL 1–5).</p>
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<p>Monthly energy losses for ventilation and monthly mean temperature inside the classrooms (cases S1 and S2).</p>
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<p>Mean radiant temperature (<span class="html-italic">t<sub>mr</sub></span>) in classrooms during the coldest day of the year, in cases (<b>a</b>) S1 and (<b>b</b>) S2.</p>
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19 pages, 2310 KiB  
Article
A Machine Learning Approach for Investment Analysis in Renewable Energy Sources: A Case Study in Photovoltaic Farms
by Konstantinos Ioannou, Evangelia Karasmanaki, Despoina Sfiri, Spyridon Galatsidas and Georgios Tsantopoulos
Energies 2023, 16(23), 7735; https://doi.org/10.3390/en16237735 - 23 Nov 2023
Viewed by 1853
Abstract
Farmland offers excellent conditions for developing solar energy while farmers seem to appreciate its notable revenues. The increasing adoption of photovoltaics (PVs) on farmland raises various concerns with the most important being the loss of productive farmland and the increased farmland prices, which [...] Read more.
Farmland offers excellent conditions for developing solar energy while farmers seem to appreciate its notable revenues. The increasing adoption of photovoltaics (PVs) on farmland raises various concerns with the most important being the loss of productive farmland and the increased farmland prices, which may prevent young farmers from entering the farming occupation. The latter can threaten the future of agriculture in countries that are already facing the problem of rural population ageing. The aim of this paper is to examine the effect of crop type on farmers’ willingness to install photovoltaics on their farmland. To that end, this study applies four machine learning (ML) algorithms (categorical regression, decision trees and random forests, support vector machines) on a dataset obtained from a questionnaire survey on farmers in a Greek agricultural area. The results from the application of the algorithms allowed us to quantify and relate farmers’ willingness to invest in PVs with three major crop types (cotton, wheat, sunflower) which play a very important role in food security. Results also provide support for making policy interventions by defining the rate of productive farmland for photovoltaics and also for designing policies to support farmers to start and maintain farming operations. Full article
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<p>Municipal unit of Didymoteicho in light orange, Evros prefecture in red outline.</p>
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<p>Data preparation.</p>
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<p>Decision tree confusion matrix of the results.</p>
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<p>Decision tree feature importance.</p>
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<p>Random forest confusion matrix of the results.</p>
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<p>Random forest feature importance.</p>
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<p>Confusion matrix after the application of hyper optimization.</p>
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<p>New (hyper optimized feature importance).</p>
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<p>SVM confusion matrix of the results.</p>
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<p>Support vector machine feature importance.</p>
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32 pages, 982 KiB  
Article
Barriers to Renewable Energy Source (RES) Installations as Determinants of Energy Consumption in EU Countries
by Bożena Gajdzik, Radosław Wolniak, Rafał Nagaj, Wieslaw Wes Grebski and Taras Romanyshyn
Energies 2023, 16(21), 7364; https://doi.org/10.3390/en16217364 - 31 Oct 2023
Cited by 18 | Viewed by 2955
Abstract
The article presents an analysis of the statistical relationship between the determinants of and barriers to the development of renewable energy sources (RESs) in the macroeconomic system and the development of renewable energy source consumption in individual European Union countries. The article considers [...] Read more.
The article presents an analysis of the statistical relationship between the determinants of and barriers to the development of renewable energy sources (RESs) in the macroeconomic system and the development of renewable energy source consumption in individual European Union countries. The article considers four key categories of RES development barriers in the European Union: political, administrative, grid infrastructural, and socioeconomic. The work is based on publicly available historical data from European Union reports, Eurostat, and the Eclareon RES Policy Monitoring Database. The empirical analysis includes all 27 countries belonging to the European Union. The research aimed to determine the impact of all four types of factors, including socioeconomic, on the development of RESs in European Union countries. The analysis uncovered that describing the European Union as a consistent region regarding the speed of renewable energy advancement and the obstacles to such progress is not accurate. Notably, a significant link exists between a strong degree of societal development and the integration of renewable energy sources. In less prosperous EU nations, economic growth plays a pivotal role in renewable energy development. Barriers of an administrative nature exert a notable influence on renewable energy development, especially in less affluent EU countries, while grid-related obstacles are prevalent in Southern–Central Europe. In nations where the proportion of renewable energy sources in electricity consumption is substantial, an excess of capacity in the renewable energy market significantly affects its growth. Full article
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<p>Number of publications about RES barriers in the databases WoS and Scopus between 2004 and 2022 (“Barriers of renewables”).</p>
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<p>Stages of the research process.</p>
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<p>Model for analyzing the impact of factors of and barriers to RES development.</p>
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27 pages, 5271 KiB  
Article
Reviewing the Situation and Prospects for Developing Small Renewable Energy Systems in Poland
by Mirosława Witkowska-Dąbrowska, Natalia Świdyńska and Agnieszka Napiórkowska-Baryła
Energies 2023, 16(21), 7339; https://doi.org/10.3390/en16217339 - 30 Oct 2023
Cited by 2 | Viewed by 1271
Abstract
The objective of this study was to evaluate changes in the number of small renewable energy sources (RES) power plants and the volume of generated energy in the years 2016–2020, with an outlook to year 2025. The study covered the area of Poland, [...] Read more.
The objective of this study was to evaluate changes in the number of small renewable energy sources (RES) power plants and the volume of generated energy in the years 2016–2020, with an outlook to year 2025. The study covered the area of Poland, including the division into provinces and different sources of renewable energy. Absolute values of electric power production and sale were presented, in addition to calculated structure indices. Moreover, the number and structure of small power plants using different renewable energy sources was determined for every Polish province. A classification of the provinces was made, where four classes were distinguished depending on the number of RES plants operating in the provinces. The research results allowed us to diagnose the current situation and make a prognosis for the future, which may translate into support for the development of particular types of installations, depending on the natural and economic characteristics of each area. The added value of the study stems from the fact that previous reports focused mainly on micro or large power plants and the time span covered data before and during the pandemic. This made it possible to assess the impact of the pandemic on the development of small renewable energy sources. Full article
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<p>Production and sale of electricity generated in small RES power plants in 2016–2020. Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Production of energy in small RES installations in 2016–2020, including the division into types of sources (GWh), HP—hydropower, WE—wind energy, SR—solar radiation, BG—biogas (non-agricultural), BM—biomass (not visible on the graph). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Sale of energy in small RES installations in 2016–2020, including the division into types of sources (GWh), HP—hydropower, WE—wind energy, SR—solar radiation, BG—biogas (non-agricultural), BM—biomass. (Biomass, due to its share of approximately 0.002%, was not included.) Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Share of electricity production small RES power plants in 2016–2020, with the division into types of sources (in GWh). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Share of electricity sale from small RES power plants in 2016–2020, with the division into types of sources (in GWh). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Share of electricity production and sale from small RES power plants in 2021–2022, with the division into types of sources. Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Absolute growth (difference) in sold electricity generated in small RES power plants from 2016 to 2020 by source type, presented year-to-year (in GWh). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Number of small RES power plants in 2016–2020. Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Increase in the share of small RES power plants, with the division into types of sources (year 2016 as the starting point). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>The administrative division of Poland into provinces. Source: the authors.</p>
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<p>Dynamics of change number of small RES power plants in 2016–2020 in the Polish provinces, Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>Increase in the share of small RES power plants, with the division into types of sources in the Polish provinces in years 2016–2020 (year 2016 as the starting point). Source: the authors, based on [<a href="#B109-energies-16-07339" class="html-bibr">109</a>].</p>
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<p>The Polish provinces divided into classes in 2016–2020: 1, provinces with a high number of small RES power plants: <span class="html-italic">d</span><span class="html-italic">i</span> ≥ (<span class="html-italic">đ</span><span class="html-italic">i</span>) + <span class="html-italic">S</span><span class="html-italic">d</span><span class="html-italic">i</span>; 2, provinces with a moderate number: (<span class="html-italic">đ</span><span class="html-italic">i</span>) ≤ <span class="html-italic">d</span><span class="html-italic">i</span> &lt; (<span class="html-italic">đ</span><span class="html-italic">i</span>) + <span class="html-italic">Sdi</span>; 3, provinces with an average number: (<span class="html-italic">đ</span><span class="html-italic">i</span>) − <span class="html-italic">Sdi</span> ≤ <span class="html-italic">di</span> &lt; (<span class="html-italic">đ</span><span class="html-italic">i</span>); 4, provinces with a low number: <span class="html-italic">di</span> &lt; (<span class="html-italic">đ</span><span class="html-italic">i</span>) − <span class="html-italic">Sdi</span>, where <span class="html-italic">di</span>—number of installations in a province, (<span class="html-italic">đ</span><span class="html-italic">i</span>)—arithmetic mean of the number of installations in a province, <span class="html-italic">Sdi</span>—standard deviation of the number of installations in a province. Source: the authors.</p>
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<p>Current status and prospects for the growth of small RES power plants in Poland. Source: the authors.</p>
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<p>Structure of electric power generation (%). Source: the authors, based on [<a href="#B12-energies-16-07339" class="html-bibr">12</a>].</p>
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Review

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31 pages, 720 KiB  
Review
Agricultural Wastes and Their By-Products for the Energy Market
by Magdalena Zielińska and Katarzyna Bułkowska
Energies 2024, 17(9), 2099; https://doi.org/10.3390/en17092099 - 27 Apr 2024
Cited by 1 | Viewed by 1228
Abstract
The conversion of lignocellulosic agricultural waste into biofuels and other economically valuable compounds can reduce dependence on fossil fuels, reduce harmful gas emissions, support the sustainability of natural resources, including water, and minimize the amount of waste in landfills, thus reducing environmental degradation. [...] Read more.
The conversion of lignocellulosic agricultural waste into biofuels and other economically valuable compounds can reduce dependence on fossil fuels, reduce harmful gas emissions, support the sustainability of natural resources, including water, and minimize the amount of waste in landfills, thus reducing environmental degradation. In this paper, the conversion of agricultural wastes into biomethane, biohydrogen, biodiesel, bioethanol, biobutanol, and bio-oil is reviewed, with special emphasis on primary and secondary agricultural residues as substrates. Some novel approaches are mentioned that offer opportunities to increase the efficiency of waste valorization, e.g., hybrid systems. In addition to physical, chemical, and biological pretreatment of waste, some combined methods to mitigate the negative effects of various recalcitrant compounds on waste processing (alkali-assisted thermal pretreatment, thermal hydrolysis pretreatment, and alkali pretreatment combined with bioaugmentation) are evaluated. In addition, the production of volatile fatty acids, polyhydroxyalkanoates, biochar, hydrochar, cellulosic nanomaterials, and selected platform chemicals from lignocellulosic waste is described. Finally, the potential uses of biofuels and other recovered products are discussed. Full article
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<p>Biomass conversion to energy.</p>
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23 pages, 5615 KiB  
Review
Bibliometric Analysis of Renewable Energy Research on the Example of the Two European Countries: Insights, Challenges, and Future Prospects
by Paweł Kut and Katarzyna Pietrucha-Urbanik
Energies 2024, 17(1), 176; https://doi.org/10.3390/en17010176 - 28 Dec 2023
Cited by 9 | Viewed by 1637
Abstract
Renewable energy sources, encompassing wind, solar, hydro, and geothermal options, are assuming an increasingly crucial role in the global energy landscape. They present a sustainable substitute for fossil fuels, effectively reducing greenhouse gas emissions and significantly contributing to the ongoing efforts against climate [...] Read more.
Renewable energy sources, encompassing wind, solar, hydro, and geothermal options, are assuming an increasingly crucial role in the global energy landscape. They present a sustainable substitute for fossil fuels, effectively reducing greenhouse gas emissions and significantly contributing to the ongoing efforts against climate change. The widespread adoption of renewable energy technologies has undergone rapid expansion on a global scale, propelled by governmental policies, technological advancements, and decreasing costs. Despite their numerous advantages, renewable energy sources encounter challenges such as intermittent energy supply, storage solutions, and integration into existing power grids. Nevertheless, with sustained investment and innovation, renewable energy sources have the potential to become the predominant energy source of the future. This article conducts a bibliometric analysis of research on renewable energy sources in Poland and Germany. The analysis is grounded in publications catalogued in the Web of Science database, spanning the years from 1990 to 2023. The investigation delves into research topics related to renewable energy sources and scrutinizes the most frequently cited publications authored by individuals from these two countries. This bibliometric analysis stands out through its unique value proposition compared to other similar studies by placing a distinctive emphasis on critical research gaps, such as energy storage, smart grid technologies, and renewable energy in transportation. Additionally, the study’s focus on the specific trajectories of Poland and Germany in renewable energy adoption, coupled with the identification of key institutions with the highest centrality index, provides unparalleled insights into the evolving landscape of sustainable energy research. The findings from this study can serve as a valuable source of information for policymakers, researchers, and other stakeholders interested in promoting the advancement and utilization of renewable energy sources. Full article
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<p>Renewable energy share in Poland and Germany based on Eurostat data [<a href="#B82-energies-17-00176" class="html-bibr">82</a>].</p>
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<p>Renewable energy structure in (<b>a</b>) Poland, (<b>b</b>) Germany, based on data sourced from Eurostat [<a href="#B82-energies-17-00176" class="html-bibr">82</a>].</p>
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<p>Renewable energy structure in (<b>a</b>) Poland, (<b>b</b>) Germany, based on data sourced from Eurostat [<a href="#B82-energies-17-00176" class="html-bibr">82</a>].</p>
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<p>Structure of total energy supply by source in (<b>a</b>) Poland, (<b>b</b>) Germany, based on data sourced from the International Energy Agency [<a href="#B87-energies-17-00176" class="html-bibr">87</a>].</p>
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<p>Number of publications with Polish affiliation.</p>
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<p>Cooperation between institutes in Poland.</p>
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<p>Citation analysis and major area of interest in Poland.</p>
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<p>The five references with the most notable citation burst [<a href="#B71-energies-17-00176" class="html-bibr">71</a>,<a href="#B95-energies-17-00176" class="html-bibr">95</a>,<a href="#B96-energies-17-00176" class="html-bibr">96</a>,<a href="#B97-energies-17-00176" class="html-bibr">97</a>,<a href="#B98-energies-17-00176" class="html-bibr">98</a>].</p>
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<p>The five references with the most notable citation burst [<a href="#B71-energies-17-00176" class="html-bibr">71</a>,<a href="#B95-energies-17-00176" class="html-bibr">95</a>,<a href="#B96-energies-17-00176" class="html-bibr">96</a>,<a href="#B97-energies-17-00176" class="html-bibr">97</a>,<a href="#B98-energies-17-00176" class="html-bibr">98</a>].</p>
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<p>Number of publications with Germany affiliation.</p>
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<p>Cooperation between institutes in Germany.</p>
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<p>Citation anylsis and major area of interest in Germany.</p>
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<p>Top 5 references with the strongest citation burst [<a href="#B99-energies-17-00176" class="html-bibr">99</a>,<a href="#B100-energies-17-00176" class="html-bibr">100</a>,<a href="#B101-energies-17-00176" class="html-bibr">101</a>,<a href="#B102-energies-17-00176" class="html-bibr">102</a>,<a href="#B103-energies-17-00176" class="html-bibr">103</a>].</p>
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