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

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.

Key words： energy internet; wind power; ramp events; dynamic programming

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

2 Sevlian R, Rajagopal R. Detection and statistics of wind power ramps. IEEE Transactions on Power Systems, 2013, 28(4): 3610–3620

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

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

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

6 Ouyang T, Zha X, Qin L. A survey of wind power ramp forecasting. Energy & Power Engineering, 2013, 5(4): 368–372

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)