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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 11295

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Special Issue Editors

Dr. Hyun-Goo Kim

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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

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Guest Editor

Institute for Energy Studies, Anna University, Chennai, India
Interests: solar resource assessment

Prof. Dr. Charlotte Bay Hasager

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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

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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.

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Keywords

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

Published Papers (4 papers)

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Research

26 pages, 10595 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/en15207698 - 18 Oct 2022

Cited by 5 | Viewed by 1355

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 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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/en15031130 - 03 Feb 2022

Cited by 7 | Viewed by 4113

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 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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/en14248463 - 15 Dec 2021

Cited by 7 | Viewed by 2758

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 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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/en14082216 - 15 Apr 2021

Cited by 10 | Viewed by 2022

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 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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