Probabilistic modeling of wind energy potential for power grid expansion planning

Energy - Tập 230 - Trang 120831 - 2021
Gyeongmin Kim1, Jin Hur1
1Department of Climate and Energy Systems Engineering, Ewha Womans University, Republic of Korea

Tài liệu tham khảo

Albadi, 2010, Overview of wind power intermittency impacts on power systems, Elec Power Syst Res, 80, 627, 10.1016/j.epsr.2009.10.035 Panwar, 2011, Role of renewable energy sources in environmental protection: a review, Renew Sustain Energy Rev, 15, 1513, 10.1016/j.rser.2010.11.037 Bilgili, 2015, An overview of renewable electric power capacity and progress in new technologies in the world, Renew Sustain Energy Rev, 49, 323, 10.1016/j.rser.2015.04.148 Foley, 2020, A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation, Energy, 20, 117671 2019 2019 Wagner, 2011, Life cycle assessment of the offshore wind farm alpha ventus, Energy, 36, 2459, 10.1016/j.energy.2011.01.036 Iribarren, 2013, Environmental benchmarking of wind farms according to their operational performance, Energy, 61, 589, 10.1016/j.energy.2013.09.005 Lim, 2010, Wind energy estimation of the Wol-Ryong coastal region, Energy, 35, 4700, 10.1016/j.energy.2010.09.029 Dedecca, 2017, Transmission expansion simulation for the European Northern Seas offshore grid, Energy, 125, 805, 10.1016/j.energy.2017.02.111 Pei, 2015, Temporal-spatial analysis and improvement measures of Chinese power system for wind power curtailment problem, Renew Sustain Energy Rev, 49, 148, 10.1016/j.rser.2015.04.106 Pearre, 2018, Statistical approach for improved wind speed forecasting for wind power production, Sustainable Energy Technologies and Assessments, 27, 180, 10.1016/j.seta.2018.04.010 Deetjen, 2017, The impacts of wind and solar on grid flexibility requirements in the Electric Reliability Council of Texas, Energy, 123, 637, 10.1016/j.energy.2017.02.021 Diuana, 2019, An analysis of the impacts of wind power penetration in the power system of southern Brazil, Energy, 186, 115869, 10.1016/j.energy.2019.115869 Schaber, 2012, Parametric study of variable renewable energy integration in Europe: advantages and costs of transmission grid extensions, Energy Pol, 42, 498, 10.1016/j.enpol.2011.12.016 Topcu, 2014, Energy for the future: an integrated decision aid for the case of Turkey, Energy, 29, 137, 10.1016/S0360-5442(03)00160-9 Lyu, 2013, Evaluation of ramping capability for day-ahead unit commitment considering wind power variability, Trans Korean Inst Electr Eng, 62, 457, 10.5370/KIEE.2013.62.4.457 Cheng, 2011, Probabilistic wind power generation model: derivation and application, Int J Energy, 5, 17 Kim, 2018, Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method, Energy, 157, 211, 10.1016/j.energy.2018.05.157 Han, 2017, Non-parametric hybrid models for wind speed forecasting, Energy Convers Manag, 148, 554, 10.1016/j.enconman.2017.06.021 Dadkhah, 2018, Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed, Energy, 148, 775, 10.1016/j.energy.2018.01.163 Mabel, 2010, Adequacy evaluation of wind power generation systems, Energy, 35, 5217, 10.1016/j.energy.2010.07.044 Gutierrez-Martin, 2013, Effects of wind intermittency on reduction of CO2 emissions: the case of the Spanish power system, Energy, 61, 108, 10.1016/j.energy.2013.01.057 2017 Bertsch, 2016, Public acceptance and preferences related to renewable energy and grid expansion policy: empirical insights for Germany, Energy, 114, 465, 10.1016/j.energy.2016.08.022 Collins, 2017, Adding value to EU energy policy analysis using a multi-model approach with an EU-28 electricity dispatch model, Energy, 130, 433, 10.1016/j.energy.2017.05.010 Deane, 2012, Soft-linking of a power systems model to an energy systems model, Energy, 42, 303, 10.1016/j.energy.2012.03.052 Schaber, 2012, Transmission grid extensions for the integration of variable renewable energies in Europe: who benefits where?, Energy Pol, 43, 123, 10.1016/j.enpol.2011.12.040 Werapun, 2015, Comparative study of five methods to estimate Weibull parameters for wind speed on Phangan Island, Thailand, Energy Procedia, 79, 976, 10.1016/j.egypro.2015.11.596 Kaplan, 2017, Determination of Weibull parameters by different numerical methods and analysis of wind power density in Osmaniye, Turkey, Sci Iran, 24, 3204 Indhumathy, 2014, Estimation of Weibull parameters for wind speed calculation at Kanyakumari in India, Int J Innov Res Sci Eng Technol, 3, 8340 Khahro, 2014, Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan, Energy Convers Manag, 78, 956, 10.1016/j.enconman.2013.06.062 Agreira, 2006, Probabilistic steady-state security assessment of an electric power system using a Monte Carlo approach, Int Universities Power Engineering, 2, 408, 10.1109/UPEC.2006.367509 Jamil, 1995, Wind power statistics and an evaluation of wind energy density, Renew Energy, 6, 623, 10.1016/0960-1481(95)00041-H Wais, 2017, A review of Weibull functions in wind sector, Renew Sustain Energy Rev, 70, 1099, 10.1016/j.rser.2016.12.014 Ahmed, 2010, Wind energy as a potential generation source at Ras Benas, Egypt, Renew Sustain Energy Rev, 14, 2167, 10.1016/j.rser.2010.03.006 Heo, 2001, Estimation of confidence intervals of quantiles for the Weibull distribution, Stoch Environ Res Risk Assess, 15, 284, 10.1007/s004770100071 Dolas, 2014, Estimation the system reliability using Weibull distribution, Int Proc Econ Dev Res, 75, 144 Aryal, 2011, Transmuted Weibull distribution: a generalization of the Weibull probability distribution, Eur J Pure Appl Math, 4, 89 Park, 2018, Spatial prediction of renewable energy resources for reinforcing and expanding power grids, Energy, 164, 757, 10.1016/j.energy.2018.09.032 Brus, 2007, Optimization of sample patterns for universal kriging of environmental variables, Geoderma, 138, 86, 10.1016/j.geoderma.2006.10.016 Im, 2019, Comparison of quantitative precipitation estimate using geostatistical models, J Korean Data Inf Sci Soc, 30, 77 2018