IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v242y2019icp364-377.html
   My bibliography  Save this article

Modeling wind power investments, policies and social benefits for deregulated electricity market – A review

Author

Listed:
  • Chinmoy, Lakshmi
  • Iniyan, S.
  • Goic, Ranko

Abstract

This paper reviews the different aspects of modeling wind energy systems namely investment, policies, performance, and social benefits for integration in deregulated power market. The wind energy system models depend on wind resource, electrical response of wind turbine generator and the returns of economic market. The paper focuses on identifying the sub-problems and their modeling approaches. Variability of the local power system and the wind resource are chaotic and are usually difficult to model. Satisfactory and some successful algorithms are discussed in detail. Machine Learning models are presented to predict market return and described by market trend and resource forecast. This paper, proposes the representation of risk from large wind integration in unit commitment and presents regional aggregation. The review is followed by critical costs modeling for wind energy projects and market risk mitigation strategies. Finally, social impact and energy security compliance from large scale wind integration are reviewed.

Suggested Citation

  • Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:364-377
    DOI: 10.1016/j.apenergy.2019.03.088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919304970
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.03.088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
    2. Klessmann, Corinna & Nabe, Christian & Burges, Karsten, 2008. "Pros and cons of exposing renewables to electricity market risks--A comparison of the market integration approaches in Germany, Spain, and the UK," Energy Policy, Elsevier, vol. 36(10), pages 3646-3661, October.
    3. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    4. Gross, Robert & Heptonstall, Philip, 2008. "The costs and impacts of intermittency: An ongoing debate: "East is East, and West is West, and never the twain shall meet."," Energy Policy, Elsevier, vol. 36(10), pages 4005-4007, October.
    5. Hiroux, C. & Saguan, M., 2010. "Large-scale wind power in European electricity markets: Time for revisiting support schemes and market designs?," Energy Policy, Elsevier, vol. 38(7), pages 3135-3145, July.
    6. Thumthae, Chalothorn & Chitsomboon, Tawit, 2009. "Optimal angle of attack for untwisted blade wind turbine," Renewable Energy, Elsevier, vol. 34(5), pages 1279-1284.
    7. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    8. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    9. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
    10. Hvelplund, Frede & Østergaard, Poul Alberg & Meyer, Niels I., 2017. "Incentives and barriers for wind power expansion and system integration in Denmark," Energy Policy, Elsevier, vol. 107(C), pages 573-584.
    11. de Menezes, Lilian M. & Houllier, Melanie A. & Tamvakis, Michael, 2016. "Time-varying convergence in European electricity spot markets and their association with carbon and fuel prices," Energy Policy, Elsevier, vol. 88(C), pages 613-627.
    12. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2017. "Minimizing transportation and installation costs for turbines in offshore wind farms," Renewable Energy, Elsevier, vol. 101(C), pages 667-679.
    13. Ausubel, Lawrence M. & Cramton, Peter, 2010. "Virtual power plant auctions," Utilities Policy, Elsevier, vol. 18(4), pages 201-208, December.
    14. Steiner, Andrea & Köhler, Carmen & Metzinger, Isabel & Braun, Axel & Zirkelbach, Mathias & Ernst, Dominique & Tran, Peter & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part A: Cyclone detection for wind power," Renewable Energy, Elsevier, vol. 101(C), pages 41-50.
    15. Weekes, S.M. & Tomlin, A.S. & Vosper, S.B. & Skea, A.K. & Gallani, M.L. & Standen, J.J., 2015. "Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure–correlate–predict," Renewable Energy, Elsevier, vol. 81(C), pages 760-769.
    16. Karami, M. & Shayanfar, H.A. & Aghaei, J. & Ahmadi, A., 2013. "Scenario-based security-constrained hydrothermal coordination with volatile wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 726-737.
    17. Munns, Diane, 2008. "Modeling New Approaches for Electric Energy Efficiency," The Electricity Journal, Elsevier, vol. 21(2), pages 20-26, March.
    18. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
    19. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    20. Zhang, Zhao-Sui & Sun, Yuan-Zhang & Cheng, Lin, 2013. "Potential of trading wind power as regulation services in the California short-term electricity market," Energy Policy, Elsevier, vol. 59(C), pages 885-897.
    21. Croonenbroeck, Carsten & Stadtmann, Georg, 2015. "Minimizing asymmetric loss in medium-term wind power forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 197-208.
    22. Céline Jullien & Virginie Pignon & Stéphane Robin & Carine Staropoli, 2012. "Coordinating cross-border congestion management through auctions: An experimental approach to European solutions," Post-Print halshs-00617026, HAL.
    23. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    24. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    25. Denny, Eleanor & O'Mahoney, Amy & Lannoye, Eamonn, 2017. "Modelling the impact of wind generation on electricity market prices in Ireland: An econometric versus unit commitment approach," Renewable Energy, Elsevier, vol. 104(C), pages 109-119.
    26. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    27. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    28. Ghaffari, Reza & Venkatesh, Bala, 2015. "Network constrained model for options based reserve procurement by wind generators using binomial tree," Renewable Energy, Elsevier, vol. 80(C), pages 348-358.
    29. Cartea, Álvaro & González-Pedraz, Carlos, 2012. "How much should we pay for interconnecting electricity markets? A real options approach," Energy Economics, Elsevier, vol. 34(1), pages 14-30.
    30. Haney, Aoife Brophy & Pollitt, Michael G., 2009. "Efficiency analysis of energy networks: An international survey of regulators," Energy Policy, Elsevier, vol. 37(12), pages 5814-5830, December.
    31. Zhang, Ning & Hu, Zhaoguang & Shen, Bo & Dang, Shuping & Zhang, Jian & Zhou, Yuhui, 2016. "A source–grid–load coordinated power planning model considering the integration of wind power generation," Applied Energy, Elsevier, vol. 168(C), pages 13-24.
    32. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    33. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    34. Jullien, Céline & Pignon, Virginie & Robin, Stéphane & Staropoli, Carine, 2012. "Coordinating cross-border congestion management through auctions: An experimental approach to European solutions," Energy Economics, Elsevier, vol. 34(1), pages 1-13.
    35. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    36. Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
    37. Ueckerdt, Falko & Hirth, Lion & Luderer, Gunnar & Edenhofer, Ottmar, 2013. "System LCOE: What are the costs of variable renewables?," Energy, Elsevier, vol. 63(C), pages 61-75.
    38. Klein, Sharon J.W. & Whalley, Stephanie, 2015. "Comparing the sustainability of U.S. electricity options through multi-criteria decision analysis," Energy Policy, Elsevier, vol. 79(C), pages 127-149.
    39. Wang, Longyan & Tan, Andy C.C. & Cholette, Michael E. & Gu, Yuantong, 2017. "Optimization of wind farm layout with complex land divisions," Renewable Energy, Elsevier, vol. 105(C), pages 30-40.
    40. Neuhoff, Karsten & Barquin, Julian & Bialek, Janusz W. & Boyd, Rodney & Dent, Chris J. & Echavarren, Francisco & Grau, Thilo & von Hirschhausen, Christian & Hobbs, Benjamin F. & Kunz, Friedrich & Nabe, 2013. "Renewable electric energy integration: Quantifying the value of design of markets for international transmission capacity," Energy Economics, Elsevier, vol. 40(C), pages 760-772.
    41. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Fan, Xiaochao, 2016. "A new wind power prediction method based on chaotic theory and Bernstein Neural Network," Energy, Elsevier, vol. 117(P1), pages 259-271.
    42. Swider, Derk J. & Beurskens, Luuk & Davidson, Sarah & Twidell, John & Pyrko, Jurek & Prüggler, Wolfgang & Auer, Hans & Vertin, Katarina & Skema, Romualdas, 2008. "Conditions and costs for renewables electricity grid connection: Examples in Europe," Renewable Energy, Elsevier, vol. 33(8), pages 1832-1842.
    43. Pattanariyankool, Sompop & Lave, Lester B., 2010. "Optimizing transmission from distant wind farms," Energy Policy, Elsevier, vol. 38(6), pages 2806-2815, June.
    44. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    45. Neij, L, 1999. "Cost dynamics of wind power," Energy, Elsevier, vol. 24(5), pages 375-389.
    46. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    47. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    48. McInerney, Celine & Bunn, Derek, 2013. "Valuation anomalies for interconnector transmission rights," Energy Policy, Elsevier, vol. 55(C), pages 565-578.
    49. Georgilakis, Pavlos S., 2008. "Technical challenges associated with the integration of wind power into power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 852-863, April.
    50. Anaya, Karim L. & Pollitt, Michael G., 2014. "Experience with smarter commercial arrangements for distributed wind generation," Energy Policy, Elsevier, vol. 71(C), pages 52-62.
    51. Céline Jullien & Virginie Pignon & Stéphane Robin & Carine Staropoli, 2012. "Coordinating cross-border congestion management through auctions: An experimental approach to European solutions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00617026, HAL.
    52. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    53. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions," Applied Energy, Elsevier, vol. 185(P1), pages 267-279.
    54. Taylor, James W. & Jeon, Jooyoung, 2015. "Forecasting wind power quantiles using conditional kernel estimation," Renewable Energy, Elsevier, vol. 80(C), pages 370-379.
    55. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    56. Agterbosch, Susanne & Glasbergen, Pieter & Vermeulen, Walter J.V., 2007. "Social barriers in wind power implementation in The Netherlands: Perceptions of wind power entrepreneurs and local civil servants of institutional and social conditions in realizing wind power project," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1025-1055, August.
    57. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    58. Köhler, Carmen & Steiner, Andrea & Saint-Drenan, Yves-Marie & Ernst, Dominique & Bergmann-Dick, Anja & Zirkelbach, Mathias & Ben Bouallègue, Zied & Metzinger, Isabel & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part B: Low stratus risk for solar power," Renewable Energy, Elsevier, vol. 101(C), pages 794-803.
    59. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    60. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    61. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    62. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
    63. Rubin, Ofir D. & Babcock, Bruce A., 2013. "The impact of expansion of wind power capacity and pricing methods on the efficiency of deregulated electricity markets," Energy, Elsevier, vol. 59(C), pages 676-688.
    64. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    65. Trivellato, F. & Raciti Castelli, M., 2015. "Appraisal of Strouhal number in wind turbine engineering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 795-804.
    66. Leuthold, Florian & Weigt, Hannes & von Hirschhausen, Christian, 2008. "Efficient pricing for European electricity networks - The theory of nodal pricing applied to feeding-in wind in Germany," Utilities Policy, Elsevier, vol. 16(4), pages 284-291, December.
    67. Bunn, Derek W. & Martoccia, Maria & Ochoa, Patricia & Kim, Haein & Ahn, Nam-Sung & Yoon, Yong-Beom, 2010. "Vertical integration and market power: A model-based analysis of restructuring in the Korean electricity market," Energy Policy, Elsevier, vol. 38(7), pages 3710-3716, July.
    68. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
    69. Colak, Ilhami & Fulli, Gianluca & Bayhan, Sertac & Chondrogiannis, Stamatios & Demirbas, Sevki, 2015. "Critical aspects of wind energy systems in smart grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 155-171.
    70. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.
    71. Edenhofer, Ottmar & Hirth, Lion & Knopf, Brigitte & Pahle, Michael & Schlömer, Steffen & Schmid, Eva & Ueckerdt, Falko, 2013. "On the economics of renewable energy sources," Energy Economics, Elsevier, vol. 40(S1), pages 12-23.
    72. Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
    73. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    74. Jolly, Suyash & Raven, R.P.J.M., 2015. "Collective institutional entrepreneurship and contestations in wind energy in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 999-1011.
    75. Phillips, Benjamin R. & Middleton, Richard S., 2012. "SimWIND: A geospatial infrastructure model for optimizing wind power generation and transmission," Energy Policy, Elsevier, vol. 43(C), pages 291-302.
    76. Holttinen, H., 2005. "Optimal electricity market for wind power," Energy Policy, Elsevier, vol. 33(16), pages 2052-2063, November.
    77. Lion Hirth, 2013. "The Market Value of Variable Renewables. The Effect of Solar and Wind Power Variability on their Relative Price," RSCAS Working Papers 2013/36, European University Institute.
    78. Blumsack, Seth & Lave, Lester B. & Ilic, Marija, 2008. "The Real Problem with Merchant Transmission," The Electricity Journal, Elsevier, vol. 21(2), pages 9-19, March.
    79. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    80. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    81. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    82. Gunturu, Udaya Bhaskar & Schlosser, C. Adam, 2015. "Behavior of the aggregate wind resource in the ISO regions in the United States," Applied Energy, Elsevier, vol. 144(C), pages 175-181.
    83. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    84. Agrawal, Alok & Sandhu, Kanwarjit Singh, 2016. "Most influential parametrical and data needs for realistic wind speed prediction," Renewable Energy, Elsevier, vol. 94(C), pages 452-465.
    85. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    86. Vandezande, Leen & Meeus, Leonardo & Belmans, Ronnie & Saguan, Marcelo & Glachant, Jean-Michel, 2010. "Well-functioning balancing markets: A prerequisite for wind power integration," Energy Policy, Elsevier, vol. 38(7), pages 3146-3154, July.
    87. Dale, Lewis & Milborrow, David & Slark, Richard & Strbac, Goran, 2004. "Total cost estimates for large-scale wind scenarios in UK," Energy Policy, Elsevier, vol. 32(17), pages 1949-1956, November.
    88. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    89. Fleten, Stein-Erik & Kristoffersen, Trine Krogh, 2007. "Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer," European Journal of Operational Research, Elsevier, vol. 181(2), pages 916-928, September.
    90. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    91. Mackay, R.M & Probert, S.D, 1998. "Likely market-penetrations of renewable-energy technologies," Applied Energy, Elsevier, vol. 59(1), pages 1-38, January.
    92. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    93. Wei, Max & Smith, Sarah Josephine & Sohn, Michael D., 2017. "Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs," Energy Policy, Elsevier, vol. 107(C), pages 356-369.
    94. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    95. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    96. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    97. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    98. Moghaddam, Iman Gerami & Nick, Mostafa & Fallahi, Farhad & Sanei, Mohsen & Mortazavi, Saeid, 2013. "Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market," Renewable Energy, Elsevier, vol. 55(C), pages 252-259.
    99. Verhees, Bram & Raven, Rob & Kern, Florian & Smith, Adrian, 2015. "The role of policy in shielding, nurturing and enabling offshore wind in The Netherlands (1973–2013)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 816-829.
    100. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    101. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    102. Xu, M. & Zhuan, X., 2013. "Optimal planning for wind power capacity in an electric power system," Renewable Energy, Elsevier, vol. 53(C), pages 280-286.
    103. Rueger, Jane & Attanasio, Donna, 2009. "The Winds of Change: Commitment Secures Transmission Rights," The Electricity Journal, Elsevier, vol. 22(6), pages 29-36, July.
    104. Bouzgou, Hassen & Benoudjit, Nabil, 2011. "Multiple architecture system for wind speed prediction," Applied Energy, Elsevier, vol. 88(7), pages 2463-2471, July.
    105. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
    106. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
    107. Vasel-Be-Hagh, Ahmadreza & Archer, Cristina L., 2017. "Wind farm hub height optimization," Applied Energy, Elsevier, vol. 195(C), pages 905-921.
    108. Mentis, Dimitrios & Siyal, Shahid Hussain & Korkovelos, Alexandros & Howells, Mark, 2016. "A geospatial assessment of the techno-economic wind power potential in India using geographical restrictions," Renewable Energy, Elsevier, vol. 97(C), pages 77-88.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Menghan & Yang, Zhifang & Lin, Wei & Yu, Juan & Dai, Wei & Du, Ershun, 2021. "Enhancing economics of power systems through fast unit commitment with high time resolution," Applied Energy, Elsevier, vol. 281(C).
    2. Carlos Pereyra-Mariñez & José Andrickson-Mora & Victor Samuel Ocaña-Guevera & Félix Santos García & Alexander Vallejo Diaz, 2023. "Energy Supply Systems Predicting Model for the Integration of Long-Term Energy Planning Variables with Sustainable Livelihoods Approach in Remote Communities," Energies, MDPI, vol. 16(7), pages 1-17, March.
    3. Changzheng Gao & Xiuna Wang & Dongwei Li & Chao Han & Weiyang You & Yihang Zhao, 2023. "A Novel Hybrid Power-Grid Investment Optimization Model with Collaborative Consideration of Risk and Benefit," Energies, MDPI, vol. 16(20), pages 1-23, October.
    4. Russell McKenna & Stefan Pfenninger & Heidi Heinrichs & Johannes Schmidt & Iain Staffell & Katharina Gruber & Andrea N. Hahmann & Malte Jansen & Michael Klingler & Natascha Landwehr & Xiaoli Guo Lars', 2021. "Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments," Papers 2103.09781, arXiv.org.
    5. Jayanta Bhusan Basu & Subhojit Dawn & Pradip Kumar Saha & Mitul Ranjan Chakraborty & Taha Selim Ustun, 2022. "Economic Enhancement of Wind–Thermal–Hydro System Considering Imbalance Cost in Deregulated Power Market," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    6. Carlos Pereyra-Mariñez & Félix Santos-García & Víctor S. Ocaña-Guevara & Alexander Vallejo-Díaz, 2022. "Energy Supply System Modeling Tools Integrating Sustainable Livelihoods Approach—Contribution to Sustainable Development in Remote Communities: A Review," Energies, MDPI, vol. 15(7), pages 1-17, April.
    7. Radpour, S. & Gemechu, E. & Ahiduzzaman, Md & Kumar, A., 2021. "Developing a framework to assess the long-term adoption of renewable energy technologies in the electric power sector: The effects of carbon price and economic incentives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    8. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    9. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    10. Lak, Omidreza & Rastegar, Mohammad & Mohammadi, Mohammad & Shafiee, Soroush & Zareipour, Hamidreza, 2021. "Risk-constrained stochastic market operation strategies for wind power producers and energy storage systems," Energy, Elsevier, vol. 215(PB).
    11. Wang, Yongli & Yang, Jiale & Zhou, Minhan & Zhang, Danyang & Song, Fuhao & Dong, Fugui & Zhu, Jinrong & Liu, Lin, 2021. "Evaluating the sustainability of China's power generation industry based on a matter-element extension model," Utilities Policy, Elsevier, vol. 69(C).
    12. Sung-Hyun Hwang & Mun-Kyeom Kim & Ho-Sung Ryu, 2019. "Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market," Energies, MDPI, vol. 12(13), pages 1-18, June.
    13. Li, Xingping & Li, Ji & Zhou, Guohui & Lv, Lucang, 2020. "Quantitative analysis of passive seasonal cold storage with a two-phase closed thermosyphon," Applied Energy, Elsevier, vol. 260(C).
    14. Pablo David Necoechea-Porras & Asunción López & Juan Carlos Salazar-Elena, 2021. "Deregulation in the Energy Sector and Its Economic Effects on the Power Sector: A Literature Review," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    15. Ajay Kumar & Deepak Kumar Gupta & Sriparna Roy Ghatak & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong, 2022. "A Novel Improved GSA-BPSO Driven PID Controller for Load Frequency Control of Multi-Source Deregulated Power System," Mathematics, MDPI, vol. 10(18), pages 1-41, September.
    16. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    17. Song, Ziyou & Feng, Shuo & Zhang, Lei & Hu, Zunyan & Hu, Xiaosong & Yao, Rui, 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
    19. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    20. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    21. Zeinalnezhad, Masoomeh & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Hashemi, Leila Sadat & Klemeš, Jiří Jaromír, 2021. "A hybrid risk analysis model for wind farms using Coloured Petri Nets and interpretive structural modelling," Energy, Elsevier, vol. 229(C).
    22. Dorotić, Hrvoje & Ban, Marko & Pukšec, Tomislav & Duić, Neven, 2020. "Impact of wind penetration in electricity markets on optimal power-to-heat capacities in a local district heating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    23. Arup Das & Subhojit Dawn & Sadhan Gope & Taha Selim Ustun, 2022. "A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System," Sustainability, MDPI, vol. 14(13), pages 1-21, July.
    24. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    2. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    3. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    4. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    5. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    6. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    7. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    8. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    9. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    10. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    11. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    12. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    13. Waite, Michael & Modi, Vijay, 2016. "Modeling wind power curtailment with increased capacity in a regional electricity grid supplying a dense urban demand," Applied Energy, Elsevier, vol. 183(C), pages 299-317.
    14. Lin, Boqiang & Zhang, Chongchong, 2021. "A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China," Renewable Energy, Elsevier, vol. 179(C), pages 1565-1577.
    15. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    16. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    17. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    18. Paul Simshauser & Farhad Billimoria & Craig Rogers, 2021. "Optimising VRE plant capacity in Renewable Energy Zones," Working Papers EPRG2121, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    19. Höltinger, Stefan & Salak, Boris & Schauppenlehner, Thomas & Scherhaufer, Patrick & Schmidt, Johannes, 2016. "Austria's wind energy potential – A participatory modeling approach to assess socio-political and market acceptance," Energy Policy, Elsevier, vol. 98(C), pages 49-61.
    20. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:242:y:2019:i:c:p:364-377. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.