Optimal Scheduling of Virtual Power Plant with Risk Management
Abstract
Due to intense electricity consumption, environmental concerns and technological development, a great numberof renewable distributed resources have been widely installed in the distributed network. However, the realitythat renewable distributed resources frequently fluctuate under high penetration makes effective use a challenge.Fortunately, with improved communication architecture and control techniques, this could be achieved by a VirtualPower Plant (VPP). VPP can aggregate various resources in a distributed generation portfolio, by creating onesingle operating profile. The aim of this paper is mainly to analyze optimal scheduling of VPP to maximize itsprofit, with due consideration given to the uncertainty of renewable energy output, such as wind power, and tomake the energy mix respond to system need. A risk quantization method (CVaR) is introduced to deal withuncertainty. This paper presents a VPP scheduling model, which takes VPP total operation cost, traded electricitycost, unit earnings, supply-demand balancing and other constraints into account, with a CVaR assessment methodembedded into this model. According to the scenarios generated by uncertainty of wind power output, numericalresults for a proposed case are discussed. These results show the expected profit of VPP scheduling is closelyassociated with different degrees of confidence , which is a great help for VPP operators when making the tradeoffbetween risk and profit.References
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[3] Dondi P, Bayoumi D, Haederli C, et al. Network integration of distributed power generation[J]. Journal of Power Sources, 2002, 106(1): 1-9.
[4] Akorede M F, Hizam H, Pouresmaeil E. Distributed energy resources and benefits to the environment[J]. Renewable and Sustainable Energy Reviews, 2010, 14(2): 724-734.
[5] Dimeas A L, Hatziargyriou N D. Operation of a multiagent system for microgrid control[J]. Power Systems, IEEE Transactions on, 2005, 20(3): 1447-1455.
[6] Wang C, Li P. Development and Challenges of Distributed Generation, the Micro-grid and Smart Distribution System [J][J]. Automation of Electric Power Systems, 2010, 2: 004.
[7] Karami H, Sanjari M J, Hosseinian S H, et al. An optimal dispatch algorithm for managing residential distributed energy resources[J]. Smart Grid, IEEE Transactions on, 2014, 5(5): 2360-2367.
[8] Nikonowicz Ł B, Milewski J. Virtual Power Plants-general review: structure, application and optimization[J]. Journal of Power Technologies, 2012, 92(3): 135-149.
[9] Pudjianto D, Ramsay C, Strbac G. Virtual power plant and system integration of distributed energy resources[J]. Renewable power generation, IET, 2007, 1(1): 10-16.
[10] Caldon R, Patria A R, Turri R. Optimal control of a distribution system with a virtual power plant[J]. Bulk Power System Dynamics and Control, Cortina. d’Ampezzo, Italy, 2004.
[11] Saboori H, Mohammadi M, Taghe R. Virtual power plant (VPP), definition, concept, components and types[C]//Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific. IEEE, 2011: 1-4.
[12] Pandžić H, Kuzle I, Capuder T. Virtual power plant mid-term dispatch optimization[J]. Applied Energy, 2013, 101: 134-141.
[13] Andersen P B, Poulsen B, Decker M, et al. Evaluation of a generic virtual power plant framework using service oriented architecture[C]//Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International. IEEE, 2008: 1212-1217.
[14] Ni E, Luh P B, Rourke S. Optimal integrated generation bidding and scheduling with risk management under a deregulated power market[J]. Power Systems, IEEE Transactions on, 2004, 19(1): 600-609.
[15] Li X, Jiang C. Short-term operation model and risk management for wind power penetrated system in electricity market[J]. Power Systems, IEEE Transactions on, 2011, 26(2): 932-939.
[16] Wu J, Zhang B, Deng W, et al. Application of Cost-CVaR model in determining optimal spinning reserve for wind power penetrated system[J]. International Journal of Electrical Power & Energy Systems, 2015, 66: 110-115.
[17] Xin H, Gan D, Li N, et al. Virtual power plant-based distributed control strategy for multiple distributed generators[J]. IET Control Theory & Applications, 2013, 7(1): 90-98.
[18] You S, Træholt C, Poulsen B. A market-based virtual power plant[C]//Clean Electrical Power, 2009 International Conference on. IEEE, 2009: 460-465.
[19] Kok J K, Warmer C J, Kamphuis I G. PowerMatcher: multiagent control in the electricity infrastructure[C]//Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. ACM, 2005: 75-82.
[20] Rockafellar R T, Uryasev S. Conditional value-at-risk for general loss distributions[J]. Journal of banking & finance, 2002, 26(7): 1443-1471.
[21] Gülpınar N, Rustem B, Settergren R. Simulation and optimization approaches to scenario tree generation[J]. Journal of economic dynamics and control, 2004, 28(7): 1291-1315.
[22] Morales J M, Pineda S, Conejo A J, et al. Scenario reduction for futures market trading in electricity markets[J]. Power Systems, IEEE Transactions on, 2009, 24(2): 878-888.
Published
2016-04-04
How to Cite
XIA, Yuhang.
Optimal Scheduling of Virtual Power Plant with Risk Management.
Journal of Power Technologies, [S.l.], v. 96, n. 1, p. 49--56, apr. 2016.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/719>. Date accessed: 22 dec. 2024.
Issue
Section
Renewable and Sustainable Energy
Keywords
Distributed generation, Virtual Power Plant (VPP), Conditional value at risk (CVaR), Uncertainty, Profit,
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