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

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 6321

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Special Issue Editors

Prof. Dr. Erol Kurt

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Guest Editor

Department of Electrical and Electronic Engineering, Gazi University, Ankara, Turkey
Interests: theoretical and experimental cutting-edge studies; energy systems including wind, solar, geothermal, and hydrogen
Special Issues, Collections and Topics in MDPI journals

Dr. Jose Manuel Lopez-Guede

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Guest Editor

Department of Automation Control, University of the Basque Country, Biscay, Spain
Interests: LiDAR data processing; image processing; control for autonomous vehicles; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This issue is devoted to the highly qualified selected papers of the 11th European Conference on Renewable Energy Systems (ECRES2023, ecres.net), which will take place in Riga, Latvia, in a hybrid format. This international event is expected to involve participants from more than 60 countries.

ECRES aims to bring together researchers, engineers and natural scientists from all over the world who are interested in the advancement of all branches of renewable energy systems. Wind, solar, hydrogen, hydro-, geothermal, solar-concentrating, fuel cell, energy harvesting and other energy-related topics are welcome.

We are open to the inclusion of the following related topics:

Hydrogen energy system design and implementation;
Policies on renewable energy;
Energy system optimization;
Smart systems;
Energy statistics and efficiency;
Electric networks;
Thermodynamical issues on energy;
Wind energy systems;
Turbine designs and implementation;
Photovoltaic material characterization;
Electrical machine design and implementation;
Plasma systems for nuclear fusion.

The papers for this Special Issue have already been submitted to the ECRES 2023 conference via the link https://cmt3.research.microsoft.com/ECRES2023 and will be presented in the conference. After approval, successful papers will be directed to the journal.

Prof. Dr. Erol Kurt
Dr. Jose Manuel Lopez-Guede
Guest Editors

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

hydrogen
optimization
thermodynamics
wind
photovoltaics
electrical machine

Published Papers (5 papers)

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Research

26 pages, 6560 KiB

Open AccessArticle

PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm

by Rafa Elshara, Aybaba Hançerlioğullari, Javad Rahebi and Jose Manuel Lopez-Guede

Energies 2024, 17(7), 1716; https://doi.org/10.3390/en17071716 - 03 Apr 2024

Viewed by 515

Abstract

In recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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17 pages, 5216 KiB

Open AccessArticle

Integrating Fiber Sensing for Spatially Resolved Temperature Measurement in Fuel Cells

by Nicolas Muck and Christoph David

Energies 2024, 17(1), 16; https://doi.org/10.3390/en17010016 - 19 Dec 2023

Viewed by 643

Abstract

Fiber optic sensors integrated into fuel cell stacks have the potential to significantly enhance the temperature control and health monitoring of fuel cells. Inhomogeneous loading, both within individual cells and across different cells in a stack, leads to the formation of local hotspots that accelerate aging and degrade performance. This study investigates the behavior and feasibility of incorporating polyimide-coated optical fiber sensors into bipolar plates for precise and spatially resolved temperature monitoring. The sensor is successfully integrated into a single cell of a fuel cell stack, positioned on the bipolar plate in direct contact with the membrane. Pre-tests are conducted to thoroughly evaluate the technical properties of the fiber in relation to specific cell requirements. Additionally, a physical prototype featuring the sensor is developed and employed to validate its effectiveness under realistic operating conditions. The temperature measurement obtained via the fiber exhibits a continuous profile throughout the entire length, covering both the active area and distributor region of the cell. Throughout the entire 60 min test period, the measuring system provided continuous and uninterrupted temperature measurements, encompassing the start of the stack, the heating phase, the subsequent stable operating point, and the cooling phase. However, some technical challenges are identified, as mechanical pressure exerted on the fiber influences the measured temperature. While this work demonstrates promising results, further advancements are necessary to address inhomogeneous loading within fuel cells and hotspot mitigation. The precise monitoring of temperature distribution enables early detection of potential damage, facilitating timely interventions to improve the service life and overall performance of fuel cells. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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27 pages, 1443 KiB

Open AccessFeature PaperArticle

Consumption–Production Profile Categorization in Energy Communities

by Wolfram Rozas, Rafael Pastor-Vargas, Angel Miguel García-Vico and José Carpio

Energies 2023, 16(19), 6996; https://doi.org/10.3390/en16196996 - 08 Oct 2023

Cited by 1 | Viewed by 906

Abstract

Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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16 pages, 2684 KiB

Open AccessArticle

Potential Business Models of Carbon Capture and Storage (CCS) for the Oil Refining Industry in Thailand

by Waranya Thepsaskul, Wongkot Wongsapai, Jirakom Sirisrisakulchai, Tassawan Jaitiang, Sopit Daroon, Varoon Raksakulkan, Phitsinee Muangjai, Chaichan Ritkrerkkrai, Pana Suttakul and Gengwit Wattakawigran

Energies 2023, 16(19), 6955; https://doi.org/10.3390/en16196955 - 05 Oct 2023

Cited by 1 | Viewed by 1282

Abstract

The escalating concerns over climate change have propelled industries worldwide to seek innovative strategies for mitigating greenhouse gas emissions. Within the energy sector, Carbon Capture and Storage (CCS) technology emerges as a promising solution to curtail emissions and foster sustainable development aims for the net zero approach. This research delves into the role of government support in expediting CCS adoption for the maximum potential of 9.79 MtCO₂ storage from six major refinery plants. The refineries mentioned above are anticipated to necessitate an initial capital investment of approximately 18,307 million THB. This research focuses on potential business model proposals appropriate for a country’s context, specifically, applying CCS technology to the Thai oil refining sector. To achieve the realization of CCS within the context of this study, a combination of three essential measures will be required: tax incentives, carbon credits, and grants. This process will commence with the implementation of tax incentives, followed by an increase in the carbon price within the country. Finally, the establishment of a dedicated fund, funded through deductions from oil excise tax revenue, will play a pivotal role in facilitating the necessary financial support for the emergence of CCS. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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14 pages, 3816 KiB

Open AccessArticle

Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

by Witsarut Achariyaviriya, Wongkot Wongsapai, Kittitat Janpoom, Tossapon Katongtung, Yuttana Mona, Nakorn Tippayawong and Pana Suttakul

Energies 2023, 16(17), 6351; https://doi.org/10.3390/en16176351 - 01 Sep 2023

Cited by 3 | Viewed by 2059

Abstract

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification. Full article

(This article belongs to the Special Issue Design and Implementation of Renewable Energy Systems)

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