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Simulation Modelling and Analysis of a Renewable Energy System
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Simulation Modelling and Analysis of a Renewable Energy System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A4: Bio-Energy".

Deadline for manuscript submissions: closed (25 June 2022) | Viewed by 17144

Special Issue Editors

Dr. Sumin Kim

E-Mail Website
Guest Editor
Department of Environmental Horticulture and Landscape Architecture, Environmental Horticulture, Dankook University, Cheonan 31116, Republic of Korea
Interests: renewable energy; energy crop; agrophotovoltaic system; simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Sojung Kim

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea
Interests: renewable energy; photovolatics; simulation; optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For decades, environmental pollution threatens the whole of civilization, and energy use generated by fossil fuel is one of major pollution sources. To mitigate the environmental pollution, identification of renewable energy has received nationwide attention. Currently, there are multiple renewable energy sources such as biofuel, solar and wind power, and geothermal energy. However, it is challenging to implement the renewable energy system in real-world due to its heavy implementation cost. Thus, it is crucial to utilize modelling techniques which enable to predict performance of a renewable energy system in terms of practicality, energy generation capacity, and monetary benefit. This special issue aims at identifying multiple techniques of simulation modelling and analysis for renewable energy management.

We are pleased to invite you to submit original research papers and critical review papers to a Special Issue of Energies on the topic of “Simulation Modelling and Analysis of a Renewable Energy System”. Any simulation modelling techniques (e.g., discrete-event simulation, system dynamic, agent-based simulation, artificial intelligence) for better renewable energy management will be considered in this special issue.

Dr. Sumin Kim
Dr. Sojung kim
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.

Related Special Issue

Published Papers (7 papers)

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Research

13 pages, 1293 KiB  
Article
Hybrid Performance Modeling of an Agrophotovoltaic System in South Korea
by Sojung Kim, Youngjin Kim, Youngjae On, Junyong So, Chang-Yong Yoon and Sumin Kim
Energies 2022, 15(18), 6512; https://doi.org/10.3390/en15186512 - 06 Sep 2022
Cited by 7 | Viewed by 1581
Abstract
APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that [...] Read more.
APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R2 of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers’ income as well as the sustainability of our society. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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27 pages, 7308 KiB  
Article
Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation
by Siripha Junlakarn, Radhanon Diewvilai and Kulyos Audomvongseree
Energies 2022, 15(14), 5103; https://doi.org/10.3390/en15145103 - 13 Jul 2022
Cited by 5 | Viewed by 1995
Abstract
In power system planning, the growth of renewable energy generation leads to several challenges including system reliability due to its intermittency and uncertainty. To quantify the relatively reliable capacity of this generation, capacity credit is usually adopted for long-term power system planning. This [...] Read more.
In power system planning, the growth of renewable energy generation leads to several challenges including system reliability due to its intermittency and uncertainty. To quantify the relatively reliable capacity of this generation, capacity credit is usually adopted for long-term power system planning. This paper proposes an evaluation of the capacity credit of renewable energy generation using stochastic models for resource availability. Six renewable energy generation types including wind, solar PV, small hydro, biomass, biogas, and waste were considered. The proposed models are based on the stochastic process using the Wiener process and other probability distribution functions to explain the randomness of the intermittency. Moreover, for solar PV—the generation of which depends on two key random variables, namely irradiance and temperature—a copula function is used to model their joint probabilistic behavior. These proposed models are used to simulate power outputs of renewable energy generations and then determine the capacity credit which is defined as the capacity of conventional generation that can maintain a similar level of system reliability. The proposed method is tested with Thailand’s power system and the results show that the capacity credit depends on the time of day and the size of installed capacity of the considered renewable energy generation. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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23 pages, 13841 KiB  
Article
An Energy Cost Assessment of Future Energy Scenarios: A Case Study on San Pietro Island
by Alberto Vargiu, Riccardo Novo, Claudio Moscoloni, Enrico Giglio, Giuseppe Giorgi and Giuliana Mattiazzo
Energies 2022, 15(13), 4535; https://doi.org/10.3390/en15134535 - 21 Jun 2022
Cited by 9 | Viewed by 1673
Abstract
The need for a clean and affordable energy supply is a major challenge of the current century. The tough shift toward a sustainable energy mix becomes even more problematic when facing realities that lack infrastructures and financing, such as small islands. Energy modeling [...] Read more.
The need for a clean and affordable energy supply is a major challenge of the current century. The tough shift toward a sustainable energy mix becomes even more problematic when facing realities that lack infrastructures and financing, such as small islands. Energy modeling and planning is crucial at this early stage of the ecological transition. For this reason, this article aims to improve an established long-run energy model framework, known as “OSeMOSYS,” with an add-on tool able to estimate different types of Levelized Cost Of Electricity (LCOE): a real and theoretical LCOE of each technology and a real and theoretical system LCOE. This tool fills a gap in most modeling frameworks characterized by a lack of information when evaluating energy costs and aims at guiding policymakers to the most appropriate solution. The model is then used to predict future energy scenarios for the island of San Pietro, in Sardinia, which was chosen as a case study. Four energy scenarios with a time horizon from 2020 to 2050—the Business-As-Usual (BAU) scenario, the Current Policy Projection (CPP) scenario, the Sustainable Growth (SG) scenario, and the Self-Sufficient-Renewable (SSR) scenario—are explored and ranked according to the efforts made in them to achieve an energy transition. Results demonstrates the validity of the tool, showing that, in the long run, the average LCOE of the system benefits from the installation of RES plants, passing from 49.1 €/MWh in 2050 in the BAU scenario to 48.8 €/MWh in the ambitious SG scenario. On the other hand, achieving carbon neutrality and the island’s energy independence brings the LCOE to 531.5 €/MWh, questioning the convenience of large storage infrastructures in San Pietro and opening up future work on the exploration of different storage systems. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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20 pages, 5938 KiB  
Article
Performance Assessment of Global Horizontal Irradiance Models in All-Sky Conditions
by Raihan Kamil, Pranda M. P. Garniwa and Hyunjin Lee
Energies 2021, 14(23), 7939; https://doi.org/10.3390/en14237939 - 26 Nov 2021
Cited by 5 | Viewed by 1540
Abstract
Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While [...] Read more.
Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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19 pages, 6977 KiB  
Article
Performance Evaluation of Grid-Connected Wind Turbine Generators
by Henok Ayele Behabtu, Thierry Coosemans, Maitane Berecibar, Kinde Anlay Fante, Abraham Alem Kebede, Joeri Van Mierlo and Maarten Messagie
Energies 2021, 14(20), 6807; https://doi.org/10.3390/en14206807 - 18 Oct 2021
Cited by 14 | Viewed by 3277
Abstract
The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have [...] Read more.
The risk of oscillation of grid-connected wind turbine generators (WTGs) is well known, making it all the more important to understand the characteristics of different WTGs and analyze their performance so that the problems’ causes are identified and resolved. While many studies have evaluated the performance of grid-connected WTGs, most lack clarity and precision in the modeling and simulation techniques used. Moreover, most of the literature focuses on a single mode of operation of WTGs to analyze their performances. Therefore, this paper updates the literature by considering the different operating conditions for WTGs. Using MATLAB/SIMULINK it expands the evaluation to the full range of vulnerabilities of WTGs: from the wind turbine to grid connection. A network representing grid-connected squirrel-cage induction generator (SCIG) and doubly-fed induction generator (DFIG) wind turbines are selected for simulation. The performances of SCIG and DFIG wind turbines are evaluated in terms of their energy generation capacity during constant rated wind speed, variable wind speed, and ability of fault-ride through during dynamic system transient operating conditions. The simulation results show the performance of DFIG is better than SCIG in terms of its energy generation capacity during variable wind speed conditions and active and reactive power control capability during steady-state and transient operating conditions. As a result, DFIG wind turbine is more suitable for large-scale wind power plants connected to weak utility grid applications than SCIG. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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13 pages, 2630 KiB  
Article
Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea
by Sojung Kim and Sumin Kim
Energies 2021, 14(20), 6724; https://doi.org/10.3390/en14206724 - 15 Oct 2021
Cited by 16 | Viewed by 2118
Abstract
The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance [...] Read more.
The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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13 pages, 2882 KiB  
Article
Simulation Modeling of a Photovoltaic-Green Roof System for Energy Cost Reduction of a Building: Texas Case Study
by Sojung Kim, Burchan Aydin and Sumin Kim
Energies 2021, 14(17), 5443; https://doi.org/10.3390/en14175443 - 01 Sep 2021
Cited by 8 | Viewed by 2673
Abstract
This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a [...] Read more.
This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a building surface, a field-based study has been conducted in Commerce, Texas, U.S., where the average maximum temperature in summer is 104 °F (40 °C). Two types of analyses were conducted: (1) comparison of temperature between different plant type via Analysis of variance (ANOVA) and (2) polynomial regression analysis to develop thermal impact estimation model based on air temperature and presence of a green roof. In addition, an agent-based simulation (ABS) model was developed via AnyLogic® University 8.6.0 simulation software, Chicago, IL, U.S., in order to accurately estimate energy cost and benefits of a building with a photovoltaic-green roof system. The proposed approach was applied to estimate energy reduction cost of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas (33.2410° N, 95.9104° W). As a result, the proposed approach was able to save $740,325.44 in energy cost of a heating, ventilation, and air conditioning (HAVC) system in the subject building. The proposed approach will contribute to the implementation of a sustainable building and urban agriculture. Full article
(This article belongs to the Special Issue Simulation Modelling and Analysis of a Renewable Energy System)
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