A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model
Abstract
:1. Introduction
2. Materials and Methods
- Simulation results from ESMs were used.
- Analysis of future projections related to solar and wind energy. Previous articles employed representative concentration pathways (RCPs) for projection scenarios, while recent articles utilize shared socioeconomic pathways (SSPs). RCPs are based on varying levels of radiative forcing due to anthropogenic greenhouse gas and aerosol concentrations, categorized into four levels: RCP2.6, RCP4.5, RCP6.0, and RCP8.5, with RCP8.5 representing the highest emission scenario [34]. SSPs, on the other hand, are based on the current actual situation of countries and regions, as well as development plans to obtain specific socioeconomic development scenarios. SSP1 to SSP5 represent five representative scenarios, combined with RCPs to form the RCP-SSP framework, with SSP585 representing a highly carbon-emitting scenario [35].
- Publications between 2019 and 2023 were included. The start date of 2019 was chosen to focus on recent studies and understand the latest research trends and priorities.
3. The Main Process of Literature Analysis
- Bias correction of model results. Due to the limitations of model outputs, discrepancies between model results and actual observations may occur. Therefore, some studies perform bias correction on model results before making predictions to enhance forecast accuracy.
- Assessing the accuracy of model outputs or the corrected model results. Various assessment indicators such as deviation, root-mean-square error, and other metrics are employed using meteorological data from the model’s historical time series and actual observational data or reanalysis data (e.g., ERA5, etc.) in the study area. Analyses are conducted using various indicators to evaluate the quality of the model’s results and enhance the credibility of the prediction outcomes.
- Calculating energy potential indicators. For predicting solar energy potential, two main types are considered: photovoltaic (PV) potential and concentrating solar power (CSP) potential. Most of the literature utilizes the calculation method proposed by Crook et al., 2011 [135], where solar radiation reaching the surface, surface wind speed, and surface air temperature are considered for PV potential. For CSP potential, the primary consideration is the capacity of parabolic trough collector (PTC) technology for power generation, accounting for surface air temperature and solar radiation reaching the surface. For predicting wind energy potential, a wind power density of 100 m is typically used since the hub height of commonly used turbines is around 100 m from the surface, with adjustments made based on turbine specifications in the area.
- Estimating power generation. Power generation can be calculated using tools like the Global Solar Energy Estimator (GSEE) or the open-source PVLIB Python toolkit for modeling PV energy systems [39,44,52,64,67,74]. Based on the capacity of power generation units installed in the study area or commonly available installations on the market and combined with assumptions such as fixed-tilt or single-axis tracking and loss efficiency, the power generation output under corresponding meteorological factor data is calculated [125,131,133]. Some of the literature characterizes power generation using the capacity factor, reflecting the effective utilization of the installation [44,132,133].
4. Discussion
4.1. Forecasts of Solar and Wind Energy
4.1.1. Solar Energy
Future Changes in Solar Energy
Factors Influencing Solar Energy
4.1.2. Wind Energy
Future Changes in Wind Energy
The Variability of Wind Energy
Bias Correction in Wind Energy Forecasting
4.2. Reasons for Model Prediction Errors
4.3. Future Research Prospects
5. Conclusions
- In the future, an increase in PV power generation potential is anticipated in Europe, northern South America, and Central China, while a decrease is expected in North Africa, the Tibetan Plateau, South Asia, and northern North America. Globally, CSP potential is anticipated to decrease. The changes in solar resources are influenced by cloud cover, aerosols, temperature, and wind speed, with the impact varying by region.
- Wind resources are projected to increase on a global scale in the future. However, disparities in changes exist across various regions, with some studies yielding inconsistent results within the same region. Uncertainty in wind resource forecasts necessitates many studies to conduct bias correction on wind speeds before forecasting wind resources.
- Based on the limitations of the current analysis, future research in this field should explore various aspects to enhance the accuracy and reliability of predictions. Firstly, ESMs should be optimized, which requires further improvement of the physical and chemical processes within the models, coupled with increasing resolution. Secondly, considering the ongoing advancements in wind and solar power generation technologies, future studies should place greater emphasis on the impact of these technological innovations on the distribution of future energy resources. Additionally, since extreme weather events significantly affect wind turbines and solar panels, it is imperative for future research to enhance the prediction and analysis of such events. By conducting comprehensive research in these areas, we can improve our understanding and prediction of the distribution and trends of future wind and solar energy resources, thereby providing stronger support for the SD of renewable energy.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | Geographic Area | Model Data Source |
---|---|---|
Solar resource | ||
World | ||
[36] | World | CMIP6 |
[37] | World | CMIP5 |
[38] | World | CMIP5 And CMIP6 |
[39] | World | CMIP6 |
[40] | World | CMIP5 |
[41] | World | CMIP5 |
Asia | ||
[42] | China | CMIP6 |
[43] | China | CORDEX |
[44] | China | CMIP6 |
[45] | China | CMIP6 |
[46] | China | CMIP6 |
[47] | China | CMIP5 |
[48] | China | CMIP6 |
[49] | Fukushima, Japan | Model coupled crop–meteorological database Ver.2 |
[50] | Iraq | Community Climate System Model (CCSM) |
Europe | ||
[51] | Europe | CORDEX |
[52] | Europe | CMIP5 |
[53] | Europe | CMIP6 |
[54] | Greece | Weather Research and Forecasting Model (WRF) |
[55] | Italy | CORDEX |
[56] | French | CORDEX |
[57] | the Canary Islands, Spain | CMIP5 |
Africa | ||
[58] | Africa | CORDEX |
[59] | Africa | CORDEX |
[60] | West Africa | CMIP6 |
[61] | West Africa | CORDEX |
[62] | Zambia | CORDEX |
South America | ||
[63] | South America | CORDEX |
[64] | Southwestern Colombia | CORDEX |
[65] | Brazil | CMIP6 |
[66] | the Atacama Desert, Chile and Peru | CORDEX |
North America | ||
[67] | North America | CMIP6 |
Oceania | ||
[68] | Australia | New South Wales/Australian Capital Territory Regional Climate Modelling regional projections (NARCliM) |
[69] | Australia | CORDEX |
Wind resource | ||
World | ||
[70] | world | Community Earth System Model (CESM) |
[71] | Northern Hemisphere | CMIP5 and CMIP6 |
Asia | ||
[72] | East Asia | CORDEX |
[73] | South Asia | CORDEX |
[74] | India | CORDEX |
[75] | China | CORDEX |
[76] | China | CORDEX |
[77] | China | MPI-ESM-LR, CNRM-CM5, CSIRO-Mk-3.6.0 |
[78] | China | CORDEX, Providing Regional Climates for Impacts Studies (PRECIS) |
[79] | South China Sea | regional climate model, Version 4.7 (RegCM4.7) |
[80] | Hong Kong, China | CMIP6 |
[81] | Vietnam’s tropical area | RegCM4 |
Europe | ||
[82] | Europe | CMIP6 |
[83] | Europe | CORDEX |
[84] | Northern Europe | CMIP6 |
[85] | Ireland | CMIP6 |
[86] | Italy | CORDEX |
[87] | Ireland | CORDEX |
[88] | Germany | CORDEX |
[89] | Greece | WRF |
[90] | Greece | Rossby Centre Regional Atmospheric Model, Version 4 (RCA4) |
[91] | Spain | CORDEX |
[92] | Portugal | CORDEX |
[93] | Iberian Peninsula, Spain and Portugal | CORDEX |
[94] | Republic of Serbia | Erdemli-Basin-Uni Oslo–Physical Oceanography Model (EBU-POM), Nonhydrostatic Multiscale Model on B-grid (NMMB) |
[95] | Lithuania | MPI-ESM-LR, IPSL-CM5A-M |
Africa | ||
[96] | Northwestern Africa | CORDEX |
[97] | West Africa | CMIP6 |
[98] | West Africa | CORDEX |
[99] | West Africa | CORDEX |
[100] | Southwestern Africa | CORDEX |
[101] | Zambia | CORDEX |
[102] | Morocco | CORDEX |
[103] | Egypt | CMIP6 |
South America | ||
[104] | South America | CMIP6 |
[105] | Suriname | CMIP5 |
North America | ||
[106] | North America | CORDEX |
[107] | North America | CMIP6 |
[108] | North America | Canadian Centre for Climate Modelling and Analysis (CCCma) |
[109] | Canada | WRF |
[110] | United States | CORDEX |
[111] | Alaska’s Offshore Regions | WRF |
Others and seas | ||
[112] | Australasia and Southeast Asia | CMIP6 |
[113] | Arctic and Subarctic | RCA4 |
[114] | Caribbean | CORDEX |
[115] | Black Sea | RCA4 |
[116] | Northwest Passage | CMIP6 |
[117] | Black Sea | RCA4 |
[118] | Baltic Sea | RCA4 |
[119] | North Sea | RCA5 |
[120] | Persian Gulf | CORDEX |
[121] | North Sea and Irish Sea | CORDEX |
Solar and wind resource | ||
[122] | China | WRF, RegCM4 |
[123] | Europe | CMIP5 |
[124] | Europe | CORDEX |
[125] | Portugal | WRF |
[126] | Portugal | CORDEX |
[127] | Iberian Peninsula, Spain and Portugal | CORDEX |
[128] | Africa | RegCM4 |
[129] | West Africa | CORDEX |
[130] | Brazil | CMIP5 |
[131] | Texas, United States | WRF, RegCM4 |
[132] | Texas, United States | WRF, RegCM4 |
[133] | Latin America | ISIMIP2 |
[134] | Arab countries | RegCM, ECHAM5-MPIQM |
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Chen, G.; Ji, Z. A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model. Sustainability 2024, 16, 3339. https://doi.org/10.3390/su16083339
Chen G, Ji Z. A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model. Sustainability. 2024; 16(8):3339. https://doi.org/10.3390/su16083339
Chicago/Turabian StyleChen, Guanying, and Zhenming Ji. 2024. "A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model" Sustainability 16, no. 8: 3339. https://doi.org/10.3390/su16083339