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The Development of Energy Systems: Sustainability, Intelligence and Optimization

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (13 February 2024) | Viewed by 3732

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Special Issue Editors

Prof. Dr. Emmanuel Simeu

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Guest Editor

Institute of Engineering, Université Grenoble Alpes, CNRS, Grenoble INP, TIMA, 38000 Grenoble, France
Interests: management of hybrid energy systems; modelling; control and monitoring of heterogeneous systems

Prof. Dr. Mohamed Tabaa

E-Mail Website
Guest Editor

LPRI Laboratory, EMSI Casablanca, Casablanca 20330, Maroc
Interests: hybrid energy systems; embdedded systems; IoT; IA

Special Issue Information

Dear Colleagues,

Of the electrical energy consumed over the past ten years, the level of energy produced from renewable and carbon-free sources, such as solar and wind power, has been growing steadily. This long-lasting trend is now accelerated by the fight against global warming, which promotes the use of renewable solutions in the energy mix, with the long-term objective of producing as much carbon-free electricity as possible. The intermittent nature of renewable energies requires the ability to manage the optimization between production and demand in real time in order to control their integration into a centralized or localized and decentralized electricity network.

Electricity storage can help solve the problem of intermittency. Storage solutions whose intermittency is predictable are therefore used, such as batteries for solar power or hydraulic pumping with random intermittency when wind power generation is utilized.

However, beyond the technical issues, the need for energy storage requires the rethinking of electrical networks. Solutions will differ depending on whether it is possible to compensate for any form of intermittency by seeking electricity without any distance limit or whether a decentralized system is envisaged where the energy must be produced locally by maximizing the use of energies that do not emit greenhouse gases. Energy storage technologies provide a solution for the regional and carbon-free management of electricity. Compared to large electrical grids, small-scale decentralized installations with a diverse storage technologies contribute to the development of energy systems at lower costs and with a smaller impact on the environment.

This Special Issue focuses on the broad topic of energy system development. It will include articles that target sustainable solutions incorporating, in whole or in part, storage technologies or renewable sources for decarbonized energy production. The aim is to collect, in a recognized international journal, the latest reviews, ideas, theories, technologies, systems, models, tools, applications, works in progress, and experiences on all theoretical and practical concerns of scientists, engineers, and managers in academia and industry, as well as broader topics which address the various aspects relating to technologies, policies, and solutions for the production and intelligent and optimized management of clean energies.

You are invited to submit your contribution to the Special Issue of ‘The Development of Energy Systems: Sustainability, Intelligence and Optimization’. Areas of interest include (but are not limited to) the following topics:

Novel clean energy sources technologies;
Intelligent and optimal switching control of sources and loads;
Energy vector, hydrogen, and fuel cells;
Modeling and management of pumping energy transfer station storage solutions;
Novel clean energy storage technologies;
Intelligent control for building efficiency and energy sobriety;
Clean energy, robustness, reliability, life cycle, and cost assessment;
Assessment of social, economic, and environmental impacts of energy technologies;
Fault detection and diagnosis in energy systems.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Energies.

Prof. Dr. Emmanuel Simeu
Prof. Dr. Mohamed Tabaa
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. Sustainability 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 2400 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 systems
sustainability
intelligence and optimization
clean energy
energy storage
energy vector
energy efficiency
energy sobriety

Published Papers (4 papers)

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Research

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22 pages, 6653 KiB

Open AccessArticle

Optimal Location of Solar Photovoltaic Plants Using Geographic Information Systems and Multi-Criteria Analysis

by Julio Manuel de Luis-Ruiz, Benito Ramiro Salas-Menocal, Raúl Pereda-García, Rubén Pérez-Álvarez, Javier Sedano-Cibrián and Carolina Ruiz-Fernández

Sustainability 2024, 16(7), 2895; https://doi.org/10.3390/su16072895 - 30 Mar 2024

Viewed by 549

Abstract

Nowadays, solar energy is considered to be one of the most developed renewable energy sources, and its production capacity has increased in recent years. To optimize yields and production, the correct selection of the location of these plants is essential. This research develops a methodological proposal that allows for detecting and evaluating the most appropriate places to implement solar photovoltaic plants almost automatically through GIS tools. A multi-criteria analysis is proposed to analyze large extensions of land with ten duly weighted criteria that cover the energy and territorial requirements that any installation must meet. The method assigns each site a location coefficient that reflects the weighting of the chosen criteria so that the value ordered from highest to lowest reflects the best to the worst location. Unlike other research works that can be considered similar, the methodological proposal is much more consistent than traditional alternatives as it uses a multi-criteria analysis and a weighting mechanism that is also statistically consistent, objective, and based on logical criteria. This innovative methodology is applied to Cantabria (north of Spain), although it could be used for other contexts. Full article

(This article belongs to the Special Issue The Development of Energy Systems: Sustainability, Intelligence and Optimization)

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24 pages, 6241 KiB

Open AccessArticle

Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings

by Asmae Chakir and Mohamed Tabaa

Sustainability 2024, 16(5), 2218; https://doi.org/10.3390/su16052218 - 06 Mar 2024

Cited by 1 | Viewed by 685

Abstract

Electricity demand in residential areas is generally met by the local low-voltage grid or, alternatively, the national grid, which produces electricity using thermal power stations based on conventional sources. These generators are holding back the revolution and the transition to a green planet, being unable to cope with climatic constraints. In the residential context, to ensure a smooth transition to an ecological green city, the idea of using alternative sources will offer the solution. These alternatives must be renewable and naturally available on the planet. This requires a generation that is very responsive to the constraints of the 21st century. However, these sources are intermittent and require a hybrid solution known as Hybrid Renewable Energy Systems (HRESs). To this end, we have designed a hybrid system based on PV-, wind-turbine- and grid-supported battery storage and an electric vehicle connected to a residential building. We proposed an energy management system based on nonlinear programming. This optimization was solved using sequential quadrature programming. The data were then processed using a long short-term memory (LSTM) model to predict, with the contribution and cooperation of each source, how to meet the energy needs of each home. The prediction was ensured with an accuracy of around 95%. These prediction results have been injected into K-nearest neighbors (KNN), random forest (RF) and gradient boost (GRU) repressors to predict the storage collaboration rates handled by the local battery and the electric vehicle. Results have shown an R2_score of 0.6953, 0.8381, and 0.739, respectively. This combination permitted an efficient prediction of the potential consumption from the grid with a value of an R²-score of around 0.9834 using LSTM. This methodology is effective in allowing us to know in advance the amount of energy of each source, storage, and excess grid injection and to propose the switching control of the hybrid architecture. Full article

(This article belongs to the Special Issue The Development of Energy Systems: Sustainability, Intelligence and Optimization)

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28 pages, 3369 KiB

Open AccessArticle

An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

by Shufu Yuan, Yuzhang Ji, Yongxu Chen, Xin Liu and Weijun Zhang

Sustainability 2023, 15(18), 13916; https://doi.org/10.3390/su151813916 - 19 Sep 2023

Cited by 4 | Viewed by 946

Abstract

Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10⁻⁴, 9.82484851784979 × 10⁻⁴, 9.82484851784993 × 10⁻⁴, 2.42507486809489 × 10⁻³, 1.72981370994064 × 10⁻³, and 1.66006031250846 × 10⁻². Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification. Full article

(This article belongs to the Special Issue The Development of Energy Systems: Sustainability, Intelligence and Optimization)

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Review

Jump to: Research

36 pages, 5219 KiB

Open AccessReview

Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort

by Amal Azzi, Mohamed Tabaa, Badr Chegari and Hanaa Hachimi

Sustainability 2024, 16(5), 2154; https://doi.org/10.3390/su16052154 - 05 Mar 2024

Viewed by 857

Abstract

The objective of energy transition is to convert the worldwide energy sector from using fossil fuels to using sources that do not emit carbon by the end of the current century. In order to achieve sustainability in the construction of energy-positive buildings, it is crucial to employ novel approaches to reduce reliance on fossil fuels. Hence, it is essential to develop buildings with very efficient structures to promote sustainable energy practices and minimize the environmental impact. Our aims were to shed some light on the standards, building modeling strategies, and recent advances regarding the methods of control utilized in the building sector and to pinpoint the areas for improvement in the methods of control in buildings in hopes of giving future scholars a clearer understanding of the issues that need to be addressed. Accordingly, we focused on recent works that handle methods of control in buildings, which we filtered based on their approaches and relevance to the subject at hand. Furthermore, we ran a critical analysis of the reviewed works. Our work proves that model predictive control (MPC) is the most commonly used among other methods in combination with AI. However, it still faces some challenges, especially regarding its complexity. Full article

(This article belongs to the Special Issue The Development of Energy Systems: Sustainability, Intelligence and Optimization)

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