Smoothing ramp events in wind farm based on dynamic programming in energy internet

Jiang LI, Guodong LIU, Shuo ZHANG

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PDF(399 KB)
Front. Energy ›› 2018, Vol. 12 ›› Issue (4) : 550-559. DOI: 10.1007/s11708-018-0593-8
RESEARCH ARTICLE
RESEARCH ARTICLE

Smoothing ramp events in wind farm based on dynamic programming in energy internet

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Abstract

The concept of energy internet has been gradually accepted, which can optimize the consumption of fossil energy and renewable energy resources. When wind power is integrated into the main grid, ramp events caused by stochastic wind power fluctuation may threaten the security of power systems. This paper proposes a dynamic programming method in smoothing ramp events. First, the energy internet model of wind power, pumped storage power station, and gas power station is established. Then, the optimization problem in the energy internet is transformed into a multi-stage dynamic programming problem, and the dynamic programming method proposed is applied to solve the optimization problem. Finally, the evaluation functions are introduced to evaluate pollutant emissions. The results show that the dynamic programming method proposed is effective for smoothing wind power and reducing ramp events in energy internet.

Keywords

energy internet / wind power / ramp events / dynamic programming

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Jiang LI, Guodong LIU, Shuo ZHANG. Smoothing ramp events in wind farm based on dynamic programming in energy internet. Front. Energy, 2018, 12(4): 550‒559 https://doi.org/10.1007/s11708-018-0593-8

References

[1]
Sun Q, Han R, Zhang H, Zhou J, Guerrero J M. A multiagent-based consensus algorithm for distributed coordinated control of distributed generators in the energy internet. IEEE Transactions on Smart Grid, 2015, 6(6): 3006–3019
CrossRef Google scholar
[2]
Sevlian R, Rajagopal R. Detection and statistics of wind power ramps. IEEE Transactions on Power Systems, 2013, 28(4): 3610–3620
CrossRef Google scholar
[3]
Xiong Y, Zha X, Qin L, Ouyang T, Xia T. Research on wind power ramp events prediction based on strongly convective weather classification. IET Renewable Power Generation, 2017, 11(8): 1278–1285
CrossRef Google scholar
[4]
Gallego C, Costa A, Cuerva A. Improving short-term forecasting during ramp events by means of regime-switching artificial neural networks. Advances in Science & Research, 2011, 6(1): 55–58
CrossRef Google scholar
[5]
Ouyang T, Zha X, Qin L, Kusiak A. Optimization of time window size for wind power ramps prediction. IET Renewable Power Generation, 2017, 11(8): 1270–1277
CrossRef Google scholar
[6]
Ouyang T, Zha X, Qin L. A survey of wind power ramp forecasting. Energy & Power Engineering, 2013, 5(4): 368–372
CrossRef Google scholar
[7]
Yang Q, Berg L K, Pekour M D, Fast J K, Newsom R, Stoelinga M, Finley C. Evaluation of WRF-predicted near-hub-height winds and ramp events over a pacific northwest site with complex terrain. Journal of Applied Meteorology and Climatology, 2013, 52(8): 1753–1763
CrossRef Google scholar
[8]
Bhateshvar Y, Mathur H, Siguerdidjane H. Impact of wind power generating system integration on frequency stabilization in multi-area power system with fuzzy logic controller in deregulated environment. Frontiers in Energy, 2015, 9(1): 7–21
CrossRef Google scholar
[9]
Verma Y P, Kumar A. Dynamic contribution of variable-speed wind energy conversion system in system frequency regulation. Frontiers in Energy, 2012, 6(2): 184–192
CrossRef Google scholar
[10]
Cui M, Ke D, Sun Y, Gan D, Zhang J, Hodge B. Wind power ramp event forecasting using a stochastic scenario generation method. IEEE Transactions on Sustainable Energy, 2015, 6(2): 422–433
CrossRef Google scholar
[11]
Liu Y, Sun Y, Infield D, Zhao Y, Han S, Yan J. A hybrid forecasting method for wind power ramp based on orthogonal test and support vector machine (OT-SVM). IEEE Transactions on Sustainable Energy, 2017, 8(2): 451–457
CrossRef Google scholar
[12]
Kalantari A, Galiana F. The impact of wind power variability and curtailment on ramping requirements. In: Transmission and Distribution Conference and Exposition, Sao Paulo, Brazil, 2010: 133–138
[13]
Zhao J, Abedi S, He M, Du P, Sharma S, Blevins B. Quantifying risk of wind power ramps in ERCOT. IEEE Transactions on Power Systems, 2017, 32(6): 4970–4971
CrossRef Google scholar
[14]
Couto A, Costa P, Rodrigues L V, Lopes V, Estanqueiro A. Impact of weather regimes on the wind power ramp forecast in Portugal. IEEE Transactions on Sustainable Energy, 2015, 6(3): 934–942
CrossRef Google scholar
[15]
Ganger D, Zhang J, Vittal V. Statistical characterization of wind power ramps via extreme value analysis. IEEE Transactions on Power Systems, 2014, 29(6): 3118–3119
CrossRef Google scholar
[16]
Gong Y, Jiang Q, Baldick R. Ramp event forecast based wind power ramp control with energy storage system. IEEE Transactions on Power Systems, 2016, 31(3): 1831–1844
CrossRef Google scholar
[17]
Tewari S, Mohan N. Value of NAS energy storage toward integrating wind: results from the wind to battery project. IEEE Transactions on Power Systems, 2013, 28(1): 532–541
CrossRef Google scholar
[18]
Esmaili A, Novakovic B, Nasiri A, Abdel-Baqi O. A hybrid system of li-ion capacitors and flow battery for dynamic wind energy support. IEEE Transactions on Industry Applications, 2013, 49(4): 1649–1657
CrossRef Google scholar
[19]
Lin J, Sun Y, Song Y, Gao W, Sørensen P. Wind power fluctuation smoothing controller based on risk assessment of grid frequency deviation in an isolated system. IEEE Transactions on Sustainable Energy, 2013, 4(2): 379–392
CrossRef Google scholar
[20]
Zhou Y, Yan Z, Li N. A novel state of charge feedback strategy in wind power smoothing based on short-term forecast and scenario analysis. IEEE Transactions on Sustainable Energy, 2017, 8(2): 870–879
CrossRef Google scholar
[21]
Jiang Q, Hong H. Wavelet-based capacity configuration and coordinated control of hybrid energy storage system for smoothing out wind power fluctuations. IEEE Transactions on Power Systems, 2013, 28(2): 1363–1372
CrossRef Google scholar
[22]
You L, Liu D, Zhong Q, Yu N. Research on optimal schedule strategy for active distribution network. Automation of Electric Power Systems, 2014, 38(9): 177–183 (in Chinese)

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