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Volumn 184, Issue , 2016, Pages 696-713

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

Author keywords

Grid flexibility; Operation timescales; Ramp capability; Storage; Wind power forecasting; Wind power integration

Indexed keywords

COMMERCE; DIGITAL STORAGE; ELECTRIC INDUSTRY; ELECTRIC UTILITIES; ENERGY STORAGE; FORECASTING; POWER MARKETS; RAMP GENERATORS; WEATHER FORECASTING; WIND POWER;

EID: 85000360987     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.11.016     Document Type: Article
Times cited : (59)

References (51)
  • 1
    • 79952428852 scopus 로고    scopus 로고
    • Calculating wind integration costs: separating wind energy value from integration cost Impacts
    • Technical report July NREL July Golden (CO) July
    • [1] Milligan, M., Kirby, B., Calculating wind integration costs: separating wind energy value from integration cost Impacts. Technical report, July 2009, NREL, Golden (CO).
    • (2009)
    • Milligan, M.1    Kirby, B.2
  • 2
    • 84939791476 scopus 로고    scopus 로고
    • Costs and benefits of renewables portfolio standards in the United States
    • [2] Barbose, G., et al. Costs and benefits of renewables portfolio standards in the United States. Renew Sustain Energy Rev 52 (2015), 523–533.
    • (2015) Renew Sustain Energy Rev , vol.52 , pp. 523-533
    • Barbose, G.1
  • 3
    • 84882619626 scopus 로고    scopus 로고
    • The past and future cost of wind energy
    • Technical report NREL Golden (CO)
    • [3] Lantz, E., Hand, M., Wiser, R., The past and future cost of wind energy. Technical report, 2012, NREL, Golden (CO) .
    • (2012)
    • Lantz, E.1    Hand, M.2    Wiser, R.3
  • 4
    • 80052785658 scopus 로고    scopus 로고
    • Cost analysis and pricing policy of wind power in China
    • [4] Wang, Q., Wen, F.S., Yang, A., Huang, J.S., Cost analysis and pricing policy of wind power in China. J Energy Eng 137:3 (2011), 138–150.
    • (2011) J Energy Eng , vol.137 , Issue.3 , pp. 138-150
    • Wang, Q.1    Wen, F.S.2    Yang, A.3    Huang, J.S.4
  • 6
    • 84966580386 scopus 로고    scopus 로고
    • The state of the art in short-term prediction of wind power—a literature overview
    • Technical report 2nd ed. ANEMOS.plus/SafeWind projects Paris (France)
    • [6] Giebel, G., The state of the art in short-term prediction of wind power—a literature overview. Technical report, 2nd ed., 2011, ANEMOS.plus/SafeWind projects, Paris (France).
    • (2011)
    • Giebel, G.1
  • 7
    • 84897656346 scopus 로고    scopus 로고
    • Wind power forecasts using Gaussian processes and numerical weather prediction
    • [7] Chen, N., Qian, Z., Nabney, I.T., Meng, X., Wind power forecasts using Gaussian processes and numerical weather prediction. IEEE Trans Power Syst 29:2 (2014), 656–665.
    • (2014) IEEE Trans Power Syst , vol.29 , Issue.2 , pp. 656-665
    • Chen, N.1    Qian, Z.2    Nabney, I.T.3    Meng, X.4
  • 8
    • 84929191655 scopus 로고    scopus 로고
    • Very-short-term probabilistic wind power forecasts by sparse vector autoregression
    • [8] Dowell, J., Pinson, P., Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans Smart Grid 7:2 (2016), 763–770.
    • (2016) IEEE Trans Smart Grid , vol.7 , Issue.2 , pp. 763-770
    • Dowell, J.1    Pinson, P.2
  • 9
    • 80052530027 scopus 로고    scopus 로고
    • Short-term wind power forecasting using ridgelet neural network
    • [9] Amjadya, N., Keyniaa, F., Zareipourb, H., Short-term wind power forecasting using ridgelet neural network. Electric Power Syst Res 81 (2011), 2099–2107.
    • (2011) Electric Power Syst Res , vol.81 , pp. 2099-2107
    • Amjadya, N.1    Keyniaa, F.2    Zareipourb, H.3
  • 10
    • 84891809865 scopus 로고    scopus 로고
    • Wind energy: forecasting challenges for its operational management
    • [10] Pinson, P., Wind energy: forecasting challenges for its operational management. Stat Sci 28:4 (2013), 564–585.
    • (2013) Stat Sci , vol.28 , Issue.4 , pp. 564-585
    • Pinson, P.1
  • 11
    • 84946594359 scopus 로고    scopus 로고
    • An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed
    • [11] Zhao, J., et al. An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl Energy 162 (2016), 808–826.
    • (2016) Appl Energy , vol.162 , pp. 808-826
    • Zhao, J.1
  • 12
    • 84953432366 scopus 로고    scopus 로고
    • Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform
    • [12] Tascikaraoglu, A., Sanandaji, B.M., Poolla, K., Varaiya, P., Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform. Appl Energy 165 (2016), 735–747.
    • (2016) Appl Energy , vol.165 , pp. 735-747
    • Tascikaraoglu, A.1    Sanandaji, B.M.2    Poolla, K.3    Varaiya, P.4
  • 13
    • 84864797603 scopus 로고    scopus 로고
    • Performance analysis of four modified approaches for wind speed forecasting
    • [13] Zhang, W., Wu, J., Wang, J.Z., Zhao, G.W., Shen, L., Performance analysis of four modified approaches for wind speed forecasting. Appl Energy 99 (2012), 324–333.
    • (2012) Appl Energy , vol.99 , pp. 324-333
    • Zhang, W.1    Wu, J.2    Wang, J.Z.3    Zhao, G.W.4    Shen, L.5
  • 14
    • 84901926562 scopus 로고    scopus 로고
    • Short-term wind speed forecasting with Markov-switching model
    • [14] Song, Z., Jiang, Y., Zhang, Z.J., Short-term wind speed forecasting with Markov-switching model. Appl Energy 130 (2014), 103–112.
    • (2014) Appl Energy , vol.130 , pp. 103-112
    • Song, Z.1    Jiang, Y.2    Zhang, Z.J.3
  • 15
    • 84876303697 scopus 로고    scopus 로고
    • Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting
    • [15] Poncela, M., Poncela, P., Peran, J.R., Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting. Appl Energy 108 (2013), 349–362.
    • (2013) Appl Energy , vol.108 , pp. 349-362
    • Poncela, M.1    Poncela, P.2    Peran, J.R.3
  • 16
    • 84927737833 scopus 로고    scopus 로고
    • Wind power forecasting based on principle component phase space reconstruction
    • [16] Han, L., Romero, C.F., Yao, Z., Wind power forecasting based on principle component phase space reconstruction. Renew Energy 81 (2015), 737–744.
    • (2015) Renew Energy , vol.81 , pp. 737-744
    • Han, L.1    Romero, C.F.2    Yao, Z.3
  • 17
    • 84960799300 scopus 로고    scopus 로고
    • A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining
    • [17] Xu, Q., et al. A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Trans Sustain Energy 6:4 (2015), 1283–1291.
    • (2015) IEEE Trans Sustain Energy , vol.6 , Issue.4 , pp. 1283-1291
    • Xu, Q.1
  • 18
    • 84924977785 scopus 로고    scopus 로고
    • Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel
    • [18] Yao, Z., Wang, J., Luo, X., Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel. Energy Convers Manage 96 (2015), 440–451.
    • (2015) Energy Convers Manage , vol.96 , pp. 440-451
    • Yao, Z.1    Wang, J.2    Luo, X.3
  • 19
    • 84899566603 scopus 로고    scopus 로고
    • Probabilistic forecasting of wind power generation using extreme learning machine
    • [19] Wan, C., Xu, Z., Pinson, P., Dong, Z.Y., Wong, K.P., Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29:3 (2014), 1033–1044.
    • (2014) IEEE Trans Power Syst , vol.29 , Issue.3 , pp. 1033-1044
    • Wan, C.1    Xu, Z.2    Pinson, P.3    Dong, Z.Y.4    Wong, K.P.5
  • 20
    • 84871851152 scopus 로고    scopus 로고
    • Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois
    • [20] Botterud, A., Zhou, Z., Wang, J., et al. Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois. IEEE Trans Sustain Energy 4:1 (2013), 250–261.
    • (2013) IEEE Trans Sustain Energy , vol.4 , Issue.1 , pp. 250-261
    • Botterud, A.1    Zhou, Z.2    Wang, J.3
  • 22
    • 84963860883 scopus 로고    scopus 로고
    • Machine learning based multi-physical-model blending for enhancing renewable energy forecast—improvement via situation dependent error correction
    • [22] Lu, S., et al. Machine learning based multi-physical-model blending for enhancing renewable energy forecast—improvement via situation dependent error correction. 2015 European Control Conference (ECC), 2015.
    • (2015) 2015 European Control Conference (ECC)
    • Lu, S.1
  • 24
    • 79959828041 scopus 로고    scopus 로고
    • Wind power forecasting uncertainty and unit commitment
    • [24] Wang, J., et al. Wind power forecasting uncertainty and unit commitment. Appl Energy 88 (2011), 4014–4023.
    • (2011) Appl Energy , vol.88 , pp. 4014-4023
    • Wang, J.1
  • 25
    • 84942026696 scopus 로고    scopus 로고
    • The value of improved short-term wind power forecasting
    • Technical report NREL Golden (CO) Available at:
    • [25] Hodge, B.M., Florita, A., Sharp, J., Margulis, M., Mcreavy, D., The value of improved short-term wind power forecasting. Technical report, 2015, NREL, Golden (CO) Available at: .
    • (2015)
    • Hodge, B.M.1    Florita, A.2    Sharp, J.3    Margulis, M.4    Mcreavy, D.5
  • 27
    • 84924303286 scopus 로고    scopus 로고
    • Quantifying the value of improved wind energy forecasts in a pool-based electricity market
    • [27] McGarrigle, E.V., Leahy, P.G., Quantifying the value of improved wind energy forecasts in a pool-based electricity market. Renew Energy 80 (2015), 517–524.
    • (2015) Renew Energy , vol.80 , pp. 517-524
    • McGarrigle, E.V.1    Leahy, P.G.2
  • 29
    • 85010046753 scopus 로고    scopus 로고
    • What the duck curve tells us about managing a green grid
    • [acccessed 02.11.16]
    • [29] CAISO, What the duck curve tells us about managing a green grid. , 2016 [acccessed 02.11.16].
    • (2016)
    • CAISO1
  • 30
    • 85000806950 scopus 로고    scopus 로고
    • MISO. MISO 2014-2015 winter assessment report. Carmel, IN; May 2015.
    • [30] MISO. MISO 2014-2015 winter assessment report. Carmel, IN; May 2015. .
  • 31
    • 85000758782 scopus 로고    scopus 로고
    • IIT. The IEEE 118-bus 54-unit 24-hour system. <>.
    • [31] IIT. The IEEE 118-bus 54-unit 24-hour system. < motor.ece.iit.edu/data/JEAS_IEEE118.doc>.
  • 32
    • 84929323312 scopus 로고    scopus 로고
    • The Wind Integration National Dataset (WIND) toolkit
    • [32] Draxl, C., Clifton, A., Hodge, B.M., McCaa, J., The Wind Integration National Dataset (WIND) toolkit. Appl Energy 151 (2015), 355–366.
    • (2015) Appl Energy , vol.151 , pp. 355-366
    • Draxl, C.1    Clifton, A.2    Hodge, B.M.3    McCaa, J.4
  • 33
    • 84910629924 scopus 로고    scopus 로고
    • A suite of metrics for assessing the performance of solar power forecasting
    • [33] Zhang, J., et al. A suite of metrics for assessing the performance of solar power forecasting. Sol Energy 111 (2015), 157–175.
    • (2015) Sol Energy , vol.111 , pp. 157-175
    • Zhang, J.1
  • 34
    • 85054038790 scopus 로고    scopus 로고
    • Final report on the creation of the Wind Integration National Dataset (WIND) Toolkit and API
    • Technical report April NREL April Golden (CO) April
    • [34] Vaisala Inc., Final report on the creation of the Wind Integration National Dataset (WIND) Toolkit and API. Technical report, April 2016, NREL, Golden (CO).
    • (2016)
    • Vaisala Inc.1
  • 35
    • 85000414652 scopus 로고    scopus 로고
    • The impact of distributed wind on bulk power system operations in ISO-NE
    • Technical report NREL Golden (CO)
    • [35] Martínez-Anido, C.B., Hodge, B.M., Palchak, D., The impact of distributed wind on bulk power system operations in ISO-NE. Technical report, 2014, NREL, Golden (CO) .
    • (2014)
    • Martínez-Anido, C.B.1    Hodge, B.M.2    Palchak, D.3
  • 36
    • 84886708405 scopus 로고    scopus 로고
    • The western wind and solar integration study phase 2
    • Technical report NREL Golden (CO)
    • [36] Lew, D., et al. The western wind and solar integration study phase 2. Technical report, 2013, NREL, Golden (CO) .
    • (2013)
    • Lew, D.1
  • 37
    • 85000479825 scopus 로고    scopus 로고
    • CAISO. 2014 annual report on market issues & performance. Folsom, CA; 2014. <>.
    • [37] CAISO. 2014 annual report on market issues & performance. Folsom, CA; 2014. < http://www.caiso.com/Documents/2014AnnualReport_MarketIssues_Performance.pdf>.
  • 38
    • 85000594369 scopus 로고    scopus 로고
    • ISO-NE. Sources of electricity used in 2014. Holyoke, MA; 2014. <>.
    • [38] ISO-NE. Sources of electricity used in 2014. Holyoke, MA; 2014. < http://www.iso-ne.com/about/what-we-do/key-stats/resource-mix>.
  • 39
    • 85000624619 scopus 로고    scopus 로고
    • [39] CAISO. OASIS, < http://oasis.caiso.com/mrioasis/logon.do>; 2016 [acccessed 02.11.16].
    • OASIS1
  • 40
    • 85000594376 scopus 로고    scopus 로고
    • WECC. 2024 common case, <>; 2016 [acccessed 02.11.16].
    • [40] WECC. 2024 common case, < www.wecc.biz/TransmissionExpansionPlanning/Pages/Datasets.aspx>; 2016 [acccessed 02.11.16].
  • 41
    • 84867924308 scopus 로고    scopus 로고
    • Operating reserves and variable generation
    • Technical report NREL Golden (CO)
    • [41] Ela, E., Milligan, M., Kirby, B., Operating reserves and variable generation. Technical report, 2011, NREL, Golden (CO).
    • (2011)
    • Ela, E.1    Milligan, M.2    Kirby, B.3
  • 42
    • 85000752353 scopus 로고    scopus 로고
    • Energy Exemplar. <>; 2016 [acccessed 02.11.16].
    • [42] Energy Exemplar. < www.energyexemplar.com/>; 2016 [acccessed 02.11.16].
  • 43
    • 84906780860 scopus 로고    scopus 로고
    • Risk-based locational marginal pricing and congestion management
    • [43] Wang, Q., Zhang, G., McCalley, J.D., Zheng, T., Litvinov, E., Risk-based locational marginal pricing and congestion management. IEEE Trans Power Syst 29:5 (2014), 2518–2528.
    • (2014) IEEE Trans Power Syst , vol.29 , Issue.5 , pp. 2518-2528
    • Wang, Q.1    Zhang, G.2    McCalley, J.D.3    Zheng, T.4    Litvinov, E.5
  • 44
    • 84896814474 scopus 로고    scopus 로고
    • A novel market simulation methodology on hydro storage
    • [44] Gu, Y., et al. A novel market simulation methodology on hydro storage. IEEE Trans Smart Grid 5:2 (2014), 1119–1128.
    • (2014) IEEE Trans Smart Grid , vol.5 , Issue.2 , pp. 1119-1128
    • Gu, Y.1
  • 45
    • 0012301625 scopus 로고    scopus 로고
    • Integer and combinatorial optimization
    • Wiley
    • [45] Wolsey, L.A., Nemhauser, G.L., Integer and combinatorial optimization. 1999, Wiley.
    • (1999)
    • Wolsey, L.A.1    Nemhauser, G.L.2
  • 46
    • 85000817980 scopus 로고    scopus 로고
    • Analysis of modeling assumptions used in production cost models for renewable integration studies
    • Technical report NREL Golden, CO
    • [46] Stoll, B., Brinkman, B., Townsend, A., Bloom, A., Analysis of modeling assumptions used in production cost models for renewable integration studies. Technical report, 2016, NREL, Golden, CO .
    • (2016)
    • Stoll, B.1    Brinkman, B.2    Townsend, A.3    Bloom, A.4
  • 47
    • 84923800917 scopus 로고    scopus 로고
    • Review of energy system flexibility measures to enable high levels of variable renewable electricity
    • [47] Lund, P.D., Lindgren, J., Mikkola, J., Salpakari, J., Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy Rev 45 (2015), 785–807.
    • (2015) Renew Sustain Energy Rev , vol.45 , pp. 785-807
    • Lund, P.D.1    Lindgren, J.2    Mikkola, J.3    Salpakari, J.4
  • 48
    • 84959098483 scopus 로고    scopus 로고
    • Integrating large scale wind power into the electricity grid in the Northeast of Brazil
    • [48] De Jong, P., et al. Integrating large scale wind power into the electricity grid in the Northeast of Brazil. Energy 100 (2016), 401–415.
    • (2016) Energy , vol.100 , pp. 401-415
    • De Jong, P.1
  • 49
    • 84940005663 scopus 로고    scopus 로고
    • Analyzing operational flexibility of electric power systems
    • [49] Ulbig, A., Andersson, G., Analyzing operational flexibility of electric power systems. Electr Power Energy Syst 72 (2015), 155–164.
    • (2015) Electr Power Energy Syst , vol.72 , pp. 155-164
    • Ulbig, A.1    Andersson, G.2
  • 50
    • 79952073308 scopus 로고    scopus 로고
    • Grid flexibility and storage required to achieve very high penetration of variable renewable electricity
    • [50] Denholm, P., Hand, M., Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy 39:3 (2011), 1817–1830.
    • (2011) Energy Policy , vol.39 , Issue.3 , pp. 1817-1830
    • Denholm, P.1    Hand, M.2
  • 51
    • 85000663278 scopus 로고    scopus 로고
    • EnerNex. NSP wind integration study. Knoxville, TN; Aug. 2014.
    • [51] EnerNex. NSP wind integration study. Knoxville, TN; Aug. 2014. https://www.xcelenergy.com/staticfiles/xe/PDF/Regulatory/16-App-M-NSP-Wind-Integration-Study-January-2015.pdf.


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