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Renewable Energy Resource Assessment
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Renewable Energy Resource Assessment

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

Deadline for manuscript submissions: closed (29 August 2023) | Viewed by 14503

Special Issue Editors

Dr. Hyun-Goo Kim

E-Mail Website
Guest Editor
New and Renewable Energy Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea
Interests: renewable energy resource assessment; remote sensing; solar and wind forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Iniyan Selvarasan

E-Mail Website
Guest Editor
Institute for Energy Studies, Anna University, Chennai, India
Interests: solar resource assessment
Prof. Dr. Charlotte Bay Hasager

E-Mail Website
Guest Editor
Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
Interests: marine boundary-layer meteorology; satellite remote sensing; offshore wind energy; wind farm wakes; land- and sea-surface roughness; wind resources; ground-based remote sensing; offshore wind resource assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Resource assessment is essential for any energy plan within a policy or industry. As renewable energy has regional and temporal characteristics, resource assessment methods must be studied. Research on renewable energy resource assessment can increase the efficiency and values of renewable energy planning. This Special Issue is focused on novel approaches, meaningful insights, and new ways to solve existing problems for resource assessment. Renewable energy resource assessment has been conducted with various models and presented in various forms. Physical simulation and empirical regression have been studied for fundamental resource estimation. Geographic information systems or remote sensing techniques have been used for detailed assessment. Recently, machine learning has also has been studied for resource assessment. Estimated resources have been classified into various types of potential such as technical and economical depending on the purpose and data. The results have been produced in various forms such as the calculation model, two-dimensional map, and three-dimensional structure. This Special Issue will focus on all types of these studies but is not limited to them. Authors who are interested in resource assessment are invited to submit their manuscripts for publication in this Special Issue.

Dr. Hyun-Goo Kim
Prof. Dr. Iniyan Selvarasan
Prof. Dr. Charlotte Bay Hasager
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.

Keywords

  • Renewable energy 
  • Resource assessment 
  • Resource mapping 
  • Resource potential 
  • Geographic information system 
  • Remote sensing

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Published Papers (4 papers)

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Research

26 pages, 10595 KiB  
Article
Regionalization of Climate Change Simulations for the Assessment of Impacts on Precipitation, Flow Rate and Electricity Generation in the Xingu River Basin in the Brazilian Amazon
by Edmundo Wallace Monteiro Lucas, Fabrício Daniel dos Santos Silva, Francisco de Assis Salviano de Souza, David Duarte Cavalcante Pinto, Heliofábio Barros Gomes, Helber Barros Gomes, Mayara Christine Correia Lins and Dirceu Luís Herdies
Energies 2022, 15(20), 7698; https://doi.org/10.3390/en15207698 - 18 Oct 2022
Cited by 7 | Viewed by 1903
Abstract
This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte [...] Read more.
This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 2021–2080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (1981–2010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (2021–2050 and 2051–2180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment)
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31 pages, 7804 KiB  
Article
Incorporating Landscape Dynamics in Small-Scale Hydropower Site Location Using a GIS and Spatial Analysis Tool: The Case of Bohol, Central Philippines
by Imelida Torrefranca, Roland Emerito Otadoy and Alejandro Tongco
Energies 2022, 15(3), 1130; https://doi.org/10.3390/en15031130 - 3 Feb 2022
Cited by 8 | Viewed by 5374
Abstract
Hydropower depends on the elevation head and water flow of a river. However, other factors must be considered, such as the risk associated with surface processes and environmental factors. The study aims to analyze a landscape’s dynamics and locate potential sites for small-scale [...] Read more.
Hydropower depends on the elevation head and water flow of a river. However, other factors must be considered, such as the risk associated with surface processes and environmental factors. The study aims to analyze a landscape’s dynamics and locate potential sites for small-scale hydropower systems (<10 MW) using a geographic information system, the curve number method, and the TopoToolbox with a digital elevation model and available spatial datasets. Across Bohol Island in the central Philippines, the study found 94 potential sites with hydraulic heads ranging from 20–62.4 m, river discharges between 0.02 to 9.71 m3/s, and a total hydropower capacity of 13.595 MW. The river profile analysis classified the sites to five levels of risk to geo-hazards, with three-fourths of the sites being at ‘high’ to ‘very high’ risk levels while more than 50% of the total power can be generated in ‘low’ risk areas. Land-use and population constraints reduced the sites to 25 and the hydropower capacity by 60%. Although limited to the table assessment phase of hydropower development, the study showed the potential of small-scale hydropower systems in the study area, their spatial distribution, and the risk associated with each site. The study results provided data-limited resource managers’ and energy planners’ insights in targeting potential locations and minimizing field investigation costs and time. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment)
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16 pages, 4296 KiB  
Article
Simulation of Crop Yields Grown under Agro-Photovoltaic Panels: A Case Study in Chonnam Province, South Korea
by Jonghan Ko, Jaeil Cho, Jinsil Choi, Chang-Yong Yoon, Kyu-Nam An, Jong-Oh Ban and Dong-Kwan Kim
Energies 2021, 14(24), 8463; https://doi.org/10.3390/en14248463 - 15 Dec 2021
Cited by 8 | Viewed by 3534
Abstract
Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models [...] Read more.
Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models in the decision support system for agricultural technology (DSSAT) 4.6 package. We reproduced yield data of paddy rice, barley, and soybean grown in AVP experimental fields in Bosung and Naju, Chonnam Province, South Korea, using CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system, developed using the crop models, was then applied to simulate the regional variations in crop yield according to solar radiation reduction scenarios. Simulated crop yields agreed with the corresponding measured crop yields with root mean squared errors of 0.29-ton ha−1 for paddy rice, 0.46-ton ha−1 for barley, and 0.31-ton ha−1 for soybean, showing no significant differences according to paired sample t-tests. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in crop yields due to the solar radiation reduction regimes. An additional advancement in the GCSM design could help prepare a sustainable adaption strategy and understand future food supply insecurity. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment)
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18 pages, 7085 KiB  
Article
Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery
by Myeongchan Oh, Chang Ki Kim, Boyoung Kim, Changyeol Yun, Yong-Heack Kang and Hyun-Goo Kim
Energies 2021, 14(8), 2216; https://doi.org/10.3390/en14082216 - 15 Apr 2021
Cited by 13 | Viewed by 2506
Abstract
Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes [...] Read more.
Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants. Full article
(This article belongs to the Special Issue Renewable Energy Resource Assessment)
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