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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 18938
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Dr. Adel Merabet

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Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada
Interests: renewable energy; smart microgrid; control; estimation; artificial intelligence; automation; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The guest editor is inviting submissions to a Special Issue of Energies on the subject area of “Advances in Modeling, Control, and Optimization of Renewable Energy Systems and Microgrids”. Renewable energy is becoming a key player in electric power generation. It includes different sources such as wind, solar, hydro, tidal/wave, geothermal, and biomass. As some of these sources are intermittent, the current power systems and microgrids include more than a single source with storage capabilities to enhance their efficiency and operation. Modeling, control, and optimization techniques are important for understanding and efficiently operating these complex energy systems. Innovative solutions and state-of-the-art studies for renewable energy integration and microgrids will be collected in this Special Issue. Topics of interest for publication include but are not limited to the following:

Renewable energy sources
Nano- and micro-grids
Energy storage (battery, fuel cell, etc.)
Hybrid power systems
Control, optimization, and energy management strategies for microgrids
Forecasting for renewable energy
Internet of Things (IoT) and artificial intelligence (AI) applications

Dr. Adel Merabet
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

Renewable energy sources
Nano- and micro-grids
Energy storage (battery, fuel cell, etc.)
Hybrid power systems
Control, optimization, and energy management strategies for microgrids
Forecasting for renewable energy
Internet of things (IoT) and artificial intelligence (AI) applications

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Published Papers (5 papers)

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Research

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23 pages, 8142 KiB

Open AccessArticle

A Fuzzy Logic-Based Emulated Inertia Control to a Supercapacitor System to Improve Inertia in a Low Inertia Grid with Renewables

by Ratnam Kamala Sarojini, Kaliannan Palanisamy and Enrico De Tuglie

Energies 2022, 15(4), 1333; https://doi.org/10.3390/en15041333 - 12 Feb 2022

Cited by 13 | Viewed by 2662

Abstract

The contribution of power generation from converter-dominated renewable energy sources (RES) has increased enormously. Consequently, the system inertia is decreasing, and it impacts the frequency of the system. With large-scale integration of power electronic inverter-based power generation from RES, inertia from energy storage devices would be unavoidable in future power grids. In this paper, the inertia emulator is formed with a supercapacitor (IE–SC) to improve inertia in a low inertia grid. To emulate the inertia in a low inertia grid, this paper proposes a fuzzy logic controller-based emulated inertia controller (FL-EIC) for an inverter attached to IE–SC. The proposed fuzzy logic controller estimates the inertial power required based on the frequency deviation and rate of change of frequency (ROCOF). The output of the fuzzy controller adds to the conventional emulated inertia control (EIC) technique to alter the load angle for the power electronic inverter of IE–SC. Specifically, the proposed FL-EIC achieves inertia emulation by proportionally linking the time derivative of the grid frequency and frequency deviation to active power references of IE–SC. A comparison of the conventional EIC and FL-EIC is carried out to prove the effectiveness of the proposed FL-EIC. Furthermore, real-time simulations with the help of the OPAL-RT real-time simulator (OP 5700) are presented to validate the advantage of the FL-EIC. Full article

(This article belongs to the Special Issue Advances in Modeling, Control and Optimization of Renewable Energy Systems and Microgrids)

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37 pages, 2577 KiB

Open AccessArticle

Spatial Model of Optimization Applied in the Distributed Generation Photovoltaic to Adjust Voltage Levels

by José A. G. Cararo, João Caetano Neto, Wagner A. Vilela Júnior, Márcio R. C. Reis, Gabriel A. Wainer, Paulo V. dos Santos and Wesley P. Calixto

Energies 2021, 14(22), 7506; https://doi.org/10.3390/en14227506 - 10 Nov 2021

Cited by 1 | Viewed by 2135

Abstract

The main objective of this work is to develop a methodology for analyzing the quality of the voltage level in the distribution power grid to identify and reduce the violations of voltage limits through the proposition of optimal points for the allocation of photovoltaic distributed generation. The methodology uses the geographic location of the power grid and its consumers to perform the grouping and classification in spatial grids of 100 × 100 m using the average annual consumption profile. The generated profiles, including the grid information, are sent to the photovoltaic distributed generation allocation algorithm, which, using an optimization process, identifies the geographic location, the required installed capacity, and the minimum number of photovoltaic generation units that must be inserted to minimize the violations of voltage limits, respecting the necessary restrictions. The entire proposal is applied in a real feeder with thousands of bars, whose model is validated with measurements carried out in the field. Different violations of voltage limits scenarios are used to validate the methodology, obtaining grids with better voltage quality after the optimized allocation of photovoltaic distributed generation. The proposal presents itself as a new tool in the work of adapting the voltage of the distribution power grid using photovoltaic distributed generation. Full article

(This article belongs to the Special Issue Advances in Modeling, Control and Optimization of Renewable Energy Systems and Microgrids)

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16 pages, 23136 KiB

Open AccessArticle

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

by Meftah Elsaraiti and Adel Merabet

Energies 2021, 14(20), 6782; https://doi.org/10.3390/en14206782 - 18 Oct 2021

Cited by 78 | Viewed by 6460

Abstract

Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA. Full article

(This article belongs to the Special Issue Advances in Modeling, Control and Optimization of Renewable Energy Systems and Microgrids)

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20 pages, 4273 KiB

Open AccessArticle

Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration

by Mohammadali Kiehbadroudinezhad, Adel Merabet and Homa Hosseinzadeh-Bandbafha

Energies 2021, 14(13), 3777; https://doi.org/10.3390/en14133777 - 23 Jun 2021

Cited by 19 | Viewed by 2586

Abstract

This study investigates the use of division algorithms to optimize the size of a desalination system integrated with a microgrid based on a wind turbine plant and the battery storage to supply freshwater based on cost, reliability, and energy losses. Cumulative exergy demand is used to identify and minimize the energy losses in the optimized system. Division algorithms are used to overcome the drawback of low convergence speed encountered by the well-known method genetic algorithm. The findings indicated that there is a positive relationship between cost, cumulative exergy, and reliability. More specifically, when the loss of power supply probability is 10%, compared to when it is 0%, the total cumulative exergy demand and total life cycle cost are reduced by 34.76% when the battery is full and 45.44% when the battery is empty and there is a 44.43% decrease in total life cycle cost, respectively. However, the more reliable system, the less exergy is lost during the production of 1 m³ freshwater by desalination integrated into wind turbine plant. Full article

(This article belongs to the Special Issue Advances in Modeling, Control and Optimization of Renewable Energy Systems and Microgrids)

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Review

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21 pages, 3169 KiB

Open AccessReview

Intelligent and Optimized Microgrids for Future Supply Power from Renewable Energy Resources: A Review

by Mohammadali Kiehbadroudinezhad, Adel Merabet, Ahmed G. Abo-Khalil, Tareq Salameh and Chaouki Ghenai

Energies 2022, 15(9), 3359; https://doi.org/10.3390/en15093359 - 5 May 2022

Cited by 50 | Viewed by 3854

Abstract

Using renewable energy sources instead of fossil fuels is one of the best solutions to overcome greenhouse gas (GHG) emissions. However, in designing clean power generation microgrids, the economic aspects of using renewable energy technologies should be considered. Furthermore, due to the unpredictable nature of renewable energy sources, the reliability of renewable energy microgrids should also be evaluated. Optimized hybrid microgrids based on wind and solar energy can provide cost-effective power generation systems with high reliability. These microgrids can meet the power demands of the consuming units, especially in remote areas. Various techniques have been used to optimize the size of power generation systems based on renewable energy to improve efficiency, maintain reliability, improve the power grid’s resilience, and reduce system costs. Each of these techniques has shown its advantages and disadvantages in optimizing the size of hybrid renewable energy systems. To increase the share of renewable energies in electricity supply in the future and develop these new technologies further, this paper reviews the latest and most efficient techniques used to optimize green microgrids from an economical and reliable perspective to achieve a clean, economical, and highly reliable microgrid. Full article

(This article belongs to the Special Issue Advances in Modeling, Control and Optimization of Renewable Energy Systems and Microgrids)

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