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Energies
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Techno-Economic and Environmental Analysis of Hybrid Renewable Energy Systems
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Techno-Economic and Environmental Analysis of Hybrid Renewable Energy Systems

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2391

Special Issue Editors

Dr. Dilip Khatiwada

E-Mail Website
Guest Editor
Division of Energy Systems, Department of Energy Technology, KTH-Royal Institute of Technology, 100 44 Stockholm, Sweden
Interests: energy systems analysis; nexus and sustainability assessment; techno-economic optimation of energy systems; decarbonization strategies; circular economy and bio-based economy; decision support systems and life cycle assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Farzin Golzar

E-Mail Website
Guest Editor
Division of Heat and Power Technology, Department of Energy Technology, KTH-Royal Institute of Technology, 100 44 Stockholm, Sweden
Interests: Artificial Intelligence (AI) in modeling and optimizing renewable energy resources; developing hybrid data-driven statistical models and process driven physical models; comparative Life Cycle Impact Assessment (LCIA); energy conversion and storage technologies

Special Issue Information

Dear Colleagues,

Renewable energy is emerging as a reliable alternative to fossil fuels. It is much safer and cleaner than conventional sources. With the advancements in technology, the renewable energy sector has made significant progress in the last decade. However, there are still a wide variety of challenges in this sector that can be addressed with the help of emerging techniques, such as Artificial Intelligence (AI) and Internet of Things (IoT), to deal with the uncertainties associated with renewable energy resources. Furthermore, evaluating renewable energy resources in an integrated approach could lead to addressing the challenges of the water–energy–food nexus. This Special Issue will deal with innovative techniques and concepts for integrating renewable energy resources with energy systems. Topics of interest for publication include, but are not limited to, the following:

  • Optimum design and operation of hybrid renewable energy systems;
  • Innovative techniques for analysing renewable energy resources;
  • Application of AI and IoT for sustainable energy systems;
  • Renewable energy strategies for sustainable development goals (SDG);
  • Security aspects of future renewable energy systems;
  • Balancing supply and demand conditionEnergy storage system;
  • Demand side management;
  • Optimal allocation/dispatch of neweables and renewable electricity.

Dr. Dilip Khatiwada
Dr. Farzin Golzar
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.

Published Papers (2 papers)

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Research

18 pages, 733 KiB  
Article
Techno-Economic Assessment of a Hybrid Renewable Energy System for a County in the State of Bahia
by Ana Tereza Andrade Borba, Leonardo Jaime Machado Simões, Thamiles Rodrigues de Melo and Alex Álisson Bandeira Santos
Energies 2024, 17(3), 572; https://doi.org/10.3390/en17030572 - 24 Jan 2024
Viewed by 646
Abstract
Installation of hybrid systems with storage is a way to maximize the amount of energy generated through exploring the complementarity of different sources. Understanding hybrid power plant (HPP) operation is crucial for optimizing new systems and reconfiguring existing plants, to their enhance efficiency. [...] Read more.
Installation of hybrid systems with storage is a way to maximize the amount of energy generated through exploring the complementarity of different sources. Understanding hybrid power plant (HPP) operation is crucial for optimizing new systems and reconfiguring existing plants, to their enhance efficiency. Alongside technical aspects, economic feasibility is also a fundamental feature. This study simulated an off-grid HPP to consider the energy consumption of Casa Nova in Bahia, Brazil. The methodology consisted of the selection of energy sources, choosing a reference location, acquisition of generation and operational data, modeling and simulating the system in different scenarios, and a financial analysis. HOMER Pro software Version 3.16.2 was used to optimize the plant configuration, and the outputs were evaluated using the perspectives of levelized cost of energy (LCOE), simple payback, and power load fulfillment. As a result, scenario 3 was the most competitive, emphasizing that the use of different energy sources increased the system generation capacity. However, the addition of battery energy system storage (BESS) resulted in a high LCOE when compared to individual sources, which demonstrated that the cost of battery integration is not yet nationally competitive. Moreover, the results highlighted the importance of research investments, energy governance, and regulation in promoting hybrid system adoption. Full article
(This article belongs to the Special Issue Techno-Economic and Environmental Analysis of Hybrid Renewable Energy Systems)
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18 pages, 3398 KiB  
Article
Application of Artificial Intelligence for Predicting CO2 Emission Using Weighted Multi-Task Learning
by Mohammad Talaei, Majid Astaneh, Elmira Ghiasabadi Farahani and Farzin Golzar
Energies 2023, 16(16), 5956; https://doi.org/10.3390/en16165956 - 12 Aug 2023
Viewed by 1292
Abstract
Carbon emissions significantly contribute to global warming, amplifying the occurrence of extreme weather events and negatively impacting the overall environmental transformation. In line with the global commitment to combat climate change through the Paris Agreement (COP21), the European Union (EU) has formulated strategies [...] Read more.
Carbon emissions significantly contribute to global warming, amplifying the occurrence of extreme weather events and negatively impacting the overall environmental transformation. In line with the global commitment to combat climate change through the Paris Agreement (COP21), the European Union (EU) has formulated strategies aimed at achieving climate neutrality by 2050. To achieve this goal, EU member states focus on developing long-term national strategies (NLTSs) and implementing local plans to reduce greenhouse gas (GHG) emissions in alignment with EU objectives. This study focuses on the case of Sweden and aims to introduce a comprehensive data-driven framework that predicts CO2 emissions by using a diverse range of input features. Considering the scarcity of data points, we present a refined variation of multi-task learning (MTL) called weighted multi-task learning (WMTL). The findings demonstrate the superior performance of the WMTL model in terms of accuracy, robustness, and computation cost of training compared to both the basic model and MTL model. The WMTL model achieved an average mean squared error (MSE) of 0.12 across folds, thus outperforming the MTL model’s 0.15 MSE and the basic model’s 0.21 MSE. Furthermore, the computational cost of training the new model is only 20% of the cost required by the other two models. The findings from the interpretation of the WMTL model indicate that it is a promising tool for developing data-driven decision-support tools to identify strategic actions with substantial impacts on the mitigation of CO2 emissions. Full article
(This article belongs to the Special Issue Techno-Economic and Environmental Analysis of Hybrid Renewable Energy Systems)
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