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Energies
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Management and Optimization for Renewable Energy
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Management and Optimization for Renewable Energy

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

Deadline for manuscript submissions: closed (27 February 2024) | Viewed by 1946

Special Issue Editor

Dr. Meysam Khojasteh

E-Mail Website
Guest Editor
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: electric vehicle; smart grid; energy community; vehicle-to-grid; demand response; demand side management; renewable energy; electricity storage; arbitrage

Special Issue Information

Dear Colleagues,

Currently, emerging smart grids and the increasing penetration level of renewable resources in power systems is leading to tremendous changes in the operation and planning of networks. Accordingly, new optimization methods are required for the management and impact assessments of these resources. This Special Issue encompasses novel optimization methods for the management of renewable energy resources. The focus will include new methods and techniques for the optimal modelling of resources and markets, modelling of energy communities, optimization methods to improve the performance of local electricity markets, models of uncertain parameters and risk management approaches, assessing the impacts of uncertain resources on energy and ancillary service markets, optimization methods for trading imbalance, demand side management, economic aspects of climate change, etc.

Dr. Meysam Khojasteh
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

  • demand-side resources
  • energy community
  • energy storage
  • local electricity market
  • optimal operation
  • power system optimization
  • renewable energy resources
  • resource management
  • risk management
  • smart grid
  • uncertainty modeling

Published Papers (2 papers)

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Research

16 pages, 2724 KiB  
Article
Electricity Cost Savings in Energy-Intensive Companies: Optimization Framework and Case Study
by Pablo Benalcazar, Marcin Malec, Przemysław Kaszyński, Jacek Kamiński and Piotr W. Saługa
Energies 2024, 17(6), 1307; https://doi.org/10.3390/en17061307 - 8 Mar 2024
Viewed by 808
Abstract
In recent years, there has been an increasing urgency among energy-intensive companies to find innovative ways of mitigating the negative financial impacts of rising fuel and electricity prices. Consequently, companies are exploring new technological solutions to lower electricity costs, such as investing in [...] Read more.
In recent years, there has been an increasing urgency among energy-intensive companies to find innovative ways of mitigating the negative financial impacts of rising fuel and electricity prices. Consequently, companies are exploring new technological solutions to lower electricity costs, such as investing in their own power generation sources or storage systems. In this context, this article presents a data-driven optimization-based framework to manage and optimize the operation of a hybrid energy system within industries characterized by substantial power requirements. The framework encompasses several key aspects: electricity generation, self-consumption, storage, and electric grid interaction. The case of an energy-intensive company specializing in wood processing and office furniture production is evaluated. This study explored two system configurations of hybrid energy systems within an energy-intensive company. The result of the analyzed case shows that the system’s flexibility is enhanced by its ability to store energy, resulting in electricity cost savings of nearly 72% and total operating cost savings of 20%. Full article
(This article belongs to the Special Issue Management and Optimization for Renewable Energy)
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26 pages, 14427 KiB  
Article
Bayesian Entropy Methodology: A Novel Approach to Setting Anti-Islanding Protections with Enhanced Stability and Sensibility
by Eduardo Marcelo Seguin Batadi, Maximiliano Martínez and Marcelo Gustavo Molina
Energies 2024, 17(3), 693; https://doi.org/10.3390/en17030693 - 31 Jan 2024
Viewed by 811
Abstract
The risk of unintentional islanding creation in distributed energy systems poses a significant security concern since unintentional islanding formation could lead to a supply of energy outside of the optimal quality limits. This constitutes a risk for users, maintenance personnel, infrastructure, and devices. [...] Read more.
The risk of unintentional islanding creation in distributed energy systems poses a significant security concern since unintentional islanding formation could lead to a supply of energy outside of the optimal quality limits. This constitutes a risk for users, maintenance personnel, infrastructure, and devices. To mitigate this problem, anti-islanding protections are widely used to prevent the distributed generator from feeding a portion of the radial distribution grid when a protection device trips upstream. However, the effectiveness of these protections heavily relies on properly tuning protection setting thresholds (such as time delay and pickup). This work proposes a novel approach that utilizes entropy as a model and metric of the uncertainty associated with a particular protection setting. By minimizing entropy, the proposed method aims to improve stability and sensitivity, consequently improving the overall performance of anti-islanding protection. Simulation results demonstrate that the Bayesian entropy methodology (BEM) approach achieves enhanced stability in various scenarios, including frequency transients, and demonstrates a notable reduction in the size of the dataset and computational burden, ranging between 91% and 98%, when compared to related works, with an improvement of the uncertainty achieved. The findings of this study contribute to the development of more robust and reliable anti-islanding protections. Full article
(This article belongs to the Special Issue Management and Optimization for Renewable Energy)
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