Distributed optimal dispatch of virtual power plant based on ELM transformation

Hongming Yang; Dexin Yi; Junhua Zhao; Fengji Luo; Zhaoyang Dong

doi:10.3934/jimo.2014.10.1297

Article Contents

2014, Volume 10, Issue 4: 1297-1318. Doi: 10.3934/jimo.2014.10.1297

This issue Previous Article A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization Next Article A note on preinvexity

Distributed optimal dispatch of virtual power plant based on ELM transformation

1.
Hunan Province Key Laboratory of Smart Grids Operation and Control, School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
2.
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114
3.
The Centre for Intelligent Electricity Networks, The University of Newcastle, NSW 2308
4.
Centre for Intelligent Electricity Networks, The University of Newcastle, Callaghan, NSW 2308
5.
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006

Received: July 31, 2013

Revised: December 31, 2013

Published: September 2014

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Abstract

To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dispatch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.

Keywords:

Distributed primal-dual sub-gradient algorithm,
extreme learning machine,
Gumbel-Copula,
optimal dispatch,
virtual power plant.

Mathematics Subject Classification: Primary: 90B35, 90B18; Secondary: 90C30.

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References

[1]	N. Br, An Introduction to Copulas, New York: Springer, 2005, 7-48.doi: 10.1007/0-387-28678-0.
[2]	D. S. Callaway and I. A. Hiskens, Achieving controllability of electric loads, Proceedings of the IEEE, 99 (2011), 184-199.doi: 10.1109/JPROC.2010.2081652.
[3]	N. Celika, Energy output estimation for small-scale wind power generators using weibull-representative wind data, Journal of Wind Engineering and Industrial Aerodynamics, 91 (2003), 693-707.doi: 10.1016/S0167-6105(02)00471-3.
[4]	M. Grabisch, The representation of importance and interaction of features by fuzzy measures, Pattern Recognition Letters, 17 (1996), 567-575.doi: 10.1016/0167-8655(96)00020-7.
[5]	J. Hetzer, D. C. Yu and K. Bhattarai, An economic dispatch model incorporating wind power, IEEE Transactions on Energy Conversion, 23 (2008), 603-611.doi: 10.1109/TEC.2007.914171.
[6]	M. Hofert, Sampling archimedean copulas, Computational Statistics and Data Analysis, vol. 52 (2008), 5163-5174.doi: 10.1016/j.csda.2008.05.019.
[7]	G. B. Huang, Q. Y. Zhu and K. Z. Mao, et al., Can threshold networks be trained directly, IEEE Transactions on Circuits and Systems II: Express Briefs, 53 (2006), 187-191.doi: 10.1109/TCSII.2005.857540.
[8]	G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, 70 (2006), 489-501.doi: 10.1016/j.neucom.2005.12.126.
[9]	N. S. Jens, Wind Energy Systems: Optimising Design and Construction for Safe and Reliable Operation, Woodhead Publishing, 2001.doi: 10.1533/9781857090638.
[10]	I. Kuzle, M. Zdrilic and H. Pandzic, Virtual power plant dispatch optimization using linear programming, in 10th International Conference on Environment and Electrical Engineering (EEEIC), (2011), 1-4.doi: 10.1109/EEEIC.2011.5874659.
[11]	E. Mashhour and S. M. Moghaddas-Tafreshi, Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-part I: problem formulation, IEEE Transactions on Power Systems, 26 (2011), 949-956.doi: 10.1109/TPWRS.2010.2070884.
[12]	E. Mashhour and S. M. Moghaddas-Tafreshi, Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-part II: numerical analysis, IEEE Transactions on Power Systems, 26 (2011), 957-964.doi: 10.1109/TPWRS.2010.2070883.
[13]	Z. Minghui and S. Martinez, On distributed convex optimization under inequality and equality constraints, IEEE Transactions on Automatic Control, 57 (2012), 151-164.doi: 10.1109/TAC.2011.2167817.
[14]	G. Papaefthymiou and D. Kurowicka, Using copulas for modeling stochastic dependence in power system uncertainty analysis, IEEE Transactions on Power Systems, 24 (2009), 40-49.doi: 10.1109/TPWRS.2008.2004728.
[15]	D. Pudjianto, C. Ramsay and G. Strbac, Virtual power plant and system integration of distributed energy resources, IET Renewable Power Generation, 1 (2007), 10-16.doi: 10.1049/iet-rpg:20060023.
[16]	B. C. Ummels, M. Gibescu and E. Pelgrum, et al., Impacts of wind power on thermal generation unit commitment and dispatch, IEEE Transactions on Energy Conversion, 22 (2007), 44-51.doi: 10.1109/TEC.2006.889616.
[17]	C. M. Vong, P. K. Wong and L. M. Tam, et al., Ignition pattern analysis for automotive engine trouble diagnosis using wavelet packet transform and support vector machines, Chinese Journal of Mechanical Engineering, 24 (2011), 870-878.doi: 10.3901/CJME.2011.05.870.
[18]	X. Z. Wang, A. X. Chen and H. M. Feng, Upper integral network with extreme learning mechanism, Neurocomputing, 74 (2011), 2520-2525.doi: 10.1016/j.neucom.2010.12.034.
[19]	Z. Y. Wang, K. S. Leung and J. K. George, Applying fuzzy measures and nonlinear integrals in data mining, Fuzzy Sets and Systems, 156 (2005), 371-380.doi: 10.1016/j.fss.2005.05.034.
[20]	P. K. Wong, C. M. Vong and L. M. Tam, et al., Data preprocessing and modelling of electronically-controlled automotive engine power performance using kernel principal components analysis and least squares support vector machines, International Journal of Vehicle Systems Modelling and Testing, 3 (2008), 312-330.doi: 10.1504/IJVSMT.2008.025406.
[21]	P. K. Wong, Q. S. Xu and C. M. Vong, et al., Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine, IEEE Transactions on Industrial Electronics, 59 (2012), 1988-2001.doi: 10.1109/TIE.2011.2166235.
[22]	T. H. Yeh and W. Li, A study on generator capacity for wind turbines under various tower heights and rated wind speeds using weibull distribution, IEEE Transactions on Energy Conversion, 23 (2008), 592-602.doi: 10.1109/TEC.2008.918626.
[23]	L. Yu and E. O. Voit, Construction of bivariate s-distributions with copulas, Computational Statistics and Data Analysis, 51 (2006), 1822-1839.doi: 10.1016/j.csda.2005.11.021.
[24]	C. Yuen, A. Oudalov and A. Timbus, The provision of frequency control reserves from multiple microgrids, IEEE Transactions on Industrial Electronics, 58 (2011), 173-183.doi: 10.1109/TIE.2010.2041139.
[25]	J. H. Zhai, Fuzzy decision tree based on fuzzy-rough technique, Soft Computing, 15 (2011), 1087-1096.doi: 10.1007/s00500-010-0584-0.
[26]	J. H. Zhao, F. S. Wen and Z. Y. Dong, et al., Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization, IEEE Transactions on Industrial Informatics, 8 889-899. doi: 10.1109/TII.2012.2205398.