Academic Journal of Energy, 2021, 2(1); doi: 10.38007/RE.2021.020105.
Luning Wang
College of Finance, Jilin University of Finance and Economics, Changchun 130117, Jilin, China
Energy development has a crucial impact on social progress. Renewable energy power generation has achieved good economic and environmental benefits, and will certainly play an important role in the future energy structure. The purpose of this paper is to study risk assessment methods for renewable energy systems and their applications in the context of a low-carbon economy. The grey relational clustering analysis method is used to attempt to regionally cluster the vulnerability of renewable energy in each province. By introducing risk preference, this paper discusses the impact of different decision makers' risk awareness on the assessment results. The classification results show that the renewable energy vulnerability indicators selected in this paper have good discrimination and representativeness, and can be used as the indicator system of the vulnerability comprehensive decision-making assessment model for decision-making analysis and regional vulnerability comparative analysis.
Low-carbon Economy, Renewable Energy, Risk Assessment, System Application
Luning Wang. Risk Assessment Method and Application of Renewable Energy System Based on the Background of Low-carbon Economy. Academic Journal of Energy (2021), Vol. 2, Issue 1: 34-42. https://doi.org/10.38007/RE.2021.020105.
[1] Sinha A , Shahbaz M . Estimation of Environmental Kuznets Curve for CO2 Emission: Role of Renewable Energy Generation in India. Renewable Energy, 2018, 119(APR.):703-711.
[2] Pina E A , Lozano M A , Serra L M . Allocation of economic costs in trigeneration systems at variable load conditions including renewable energy sources and thermal energy storage. Energy, 2018, 151(MAY15):633-646.
[3] Ali A , Tufa R A , Macedonio F , et al. Membrane technology in renewable-energy-driven desalination. Renewable and Sustainable Energy Reviews, 2018, 81(pt.1):1-21.
[4] Troster V , Shahbaz M , Uddin G S . Renewable Energy, Oil Prices, and Economic Activity: A Granger-causality in Quantiles Analysis. Energy Economics, 2018, 70(FEB.):440-452.
[5] Ahmad J , Imran M , Khalid A , et al. Techno economic analysis of a wind-photovoltaic-biomass hybrid renewable energy system for rural electrification: A case study of Kallar Kahar. Energy, 2018, 148(APR.1):208-234.
[6] Ricardo Saavedra M M , Fontes C H D O , Freires F G M . Sustainable and renewable energy supply chain: A system dynamics overview. Renewable and Sustainable Energy Reviews, 2018, 82(pt.1):247-259.
[7] Rezaei R , Ghofranfarid M . Rural households' renewable energy usage intention in Iran: Extending the unified theory of acceptance and use of technology. Renewable Energy, 2018, 122(JUL.):382-391.
[8] Appino R R , Ordiano J A G , Mikut R , et al. On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages. Applied Energy, 2018, 210(jan.15):1207-1218.
[9] Jenkins J D , Zhou Z , Ponciroli R , et al. The benefits of nuclear flexibility in power system operations with renewable energy. Applied Energy, 2018, 222(JUL.15):872-884.
[10] Sadiqa A , Gulagi A , Breyer C . Energy transition roadmap towards 100% renewable energy and role of storage technologies for Pakistan by 2050. Energy, 2018, 147(MAR.15):518-533.
[11] Poudineh R , Sen A , Fattouh B . Advancing renewable energy in resource-rich economies of the MENA. Renewable Energy, 2018, 123(AUG.):135-149.
[12] Sequeira T N , Santos M S . Renewable energy and politics: A systematic review and new evidence. Journal of Cleaner Production, 2018, 192(AUG.10):553-568.
[13] Setiadi H , Krismanto A U , Mithulananthan N , et al. Modal interaction of power systems with high penetration of renewable energy and BES systems. International Journal of Electrical Power & Energy Systems, 2018, 97(APR.):385-395.
[14] Da Silva P P , Cerqueira P A , Ogbe W . Determinants of Renewable Energy Growth in Sub-Saharan Africa: Evidence from Panel ARDL. Energy, 2018, 156(AUG.1):45-54.
[15] Shihavuddin A , Rifat M , Maruf M H , et al. Image based surface damage detection of renewable energy installations using a unified deep learning approach. Energy Reports, 2021, 7(November 2021):4566-4576.
[16] Cubizolles G , Mouginn J , Iorio S D , et al. Stack Optimization and Testing for its Integration in a rSOC-Based Renewable Energy Storage System. ECS Transactions, 2021, 103(1):351-361. https://doi.org/10.1149/10301.0351ecst
[17] Mouginn J , Cubizolles G , Hauch A , et al. Development of an Efficient rSOC Based Renewable Energy Storage System. ECS Transactions, 2021, 103(1):337-350. https://doi.org/10.1149/10301.0337ecst
[18] Opoku R , Obeng G Y , Adjei E A , et al. Integrated system efficiency in reducing redundancy and promoting residential renewable energy in countries without net-metering: A case study of a SHS in Ghana. Renewable Energy, 2020, 155(2020):65-78. https://doi.org/10.1016/j.renene.2020.03.099
[19] Faridpak B , Alahyari A , Farrokhifar M , et al. Toward Small Scale Renewable Energy Hub-based Hybrid Fuel Stations: Appraising Structure and Scheduling, IEEE Transactions on Transportation Electrification. IEEE Transactions on Transportation Electrification, 2020, 6(1):267-277. https://doi.org/10.1109/TTE.2020.2972382