Optimal scheduling of Virtual Power Plant with risk management

• Autorzy: Xia, Y. , Liu, J.
• Języki publikacji: EN

Abstrakty

EN

Due to intense electricity consumption, environmental concerns and technological development, a great number of renewable distributed resources have been widely installed in the distributed network. However, the reality that 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 Virtual Power Plant (VPP). VPP can aggregate various resources in a distributed generation portfolio, by creating one single operating profile. The aim of this paper is mainly to analyze optimal scheduling of VPP to maximize its profit, with due consideration given to the uncertainty of renewable energy output, such as wind power, and to make the energy mix respond to system need. A risk quantization method (CVaR) is introduced to deal with uncertainty. This paper presents a VPP scheduling model, which takes VPP total operation cost, traded electricity cost, unit earnings, supply-demand balancing and other constraints into account, with a CVaR assessment method embedded into this model. According to the scenarios generated by uncertainty of wind power output, numerical results for a proposed case are discussed. These results show the expected profit of VPP scheduling is closely associated with different degrees of confidence , which is a great help for VPP operators when making the tradeoff between risk and profit.

Słowa kluczowe

PL

generacja rozproszona; elektrownia wirtualna; warunkowa wartość zagrożona ryzykiem CVaR; niepewność; zysk

EN

distributed generation; Virtual Power Plant; VPP; conditional value-at-risk; CVaR; uncertainty; profit

• Czasopismo: Journal of Power Technologies
• Rocznik: 2016
• Tom: Vol. 96, nr 1
• Strony: 49--56
• Opis fizyczny: Bibliogr. 22 poz., rys., wykr.

Twórcy

autor

Xia, Y.

• School of Electrical Engineering and Information of Sichuan University, 24th, section one south of first-ring road, Chengdu 610065, Sichuan Province, China,

autor

Liu, J.

• School of Electrical Engineering and Information of Sichuan University, 24th, section one south of first-ring road, Chengdu 610065, Sichuan Province, China

Bibliografia

• [1] T. Ackermann, G. Andersson, L. Söder, Distributed generation: a definition, Electric power systems research 57 (3) (2001) 195–204.
• [2] R. Walling, R. Saint, R. C. Dugan, J. Burke, L. A. Kojovic, Summary of distributed resources impact on power delivery systems, Power Delivery, IEEE Transactions on 23 (3) (2008) 1636–1644.
• [3] P. Dondi, D. Bayoumi, C. Haederli, D. Julian, M. Suter, Network integration of distributed power generation, Journal of Power Sources 106 (1) (2002) 1–9.
• [4] M. F. Akorede, H. Hizam, E. Pouresmaeil, Distributed energy resources and benefits to the environment, Renewable and Sustainable Energy Reviews 14 (2) (2010) 724–734.
• [5] A. Dimeas, N. Hatziargyriou, Operation of a multiagent system for microgrid control, Power Systems, IEEE Transactions on 20 (3) (2005) 1447–1455.
• [6] C. Wang, P. Li, Development and challenges of distributed generation, the micro-grid and smart distribution system [j], Automation of Electric Power Systems 2 (2010) 004.
• [7] H. Karami, M. J. Sanjari, S. H. Hosseinian, G. Gharehpetian, An optimal dispatch algorithm for managing residential distributed energy resources, Smart Grid, IEEE Transactions on 5 (5) (2014) 2360–2367.
• [8] Ł. B. Nikonowicz, J. Milewski, Virtual power plants-general review: structure, application and optimization, Journal of Power Technologies 92 (3) (2012) 135–149.
• [9] D. Pudjianto, C. Ramsay, G. Strbac, Virtual power plant and system integration of distributed energy resources, Renewable power generation, IET 1 (1) (2007) 10–16.
• [10] R. Caldon, A. R. Patria, R. Turri, Optimal control of a distribution system with a virtual power plant, Bulk Power System Dynamics and Control, Cortina. d’Ampezzo, Italy.
• [11] H. Saboori, M. Mohammadi, R. Taghe, Virtual power plant (vpp), definition, concept, components and types, in: Power and Energy Engineering Conference (APPEEC), 2011 Asia- Pacific, IEEE, 2011, pp. 1–4.
• [12] H. Pandži´c, I. Kuzle, T. Capuder, Virtual power plant mid-term dispatch optimization, Applied Energy 101 (2013) 134–141.
• [13] P. B. Andersen, B. Poulsen, M. Decker, C. Træholt, J. Ostergaard, Evaluation of a generic virtual power plant framework using service oriented architecture, in: Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International, IEEE, 2008, pp. 1212–1217.
• [14] E. Ni, P. B. Luh, S. Rourke, Optimal integrated generation bidding and scheduling with risk management under a deregulated power market, Power Systems, IEEE Transactions on 19 (1) (2004) 600–609.
• [15] X. Li, C. Jiang, Short-term operation model and risk management for wind power penetrated system in electricity market, Power Systems, IEEE Transactions on 26 (2) (2011) 932–939.
• [16] J. Wu, B. Zhang, W. Deng, K. Zhang, Application of cost-cvar model in determining optimal spinning reserve for wind power penetrated system, International Journal of Electrical Power & Energy Systems 66 (2015) 110–115.
• [17] H. Xin, D. Gan, N. Li, H. Li, C. Dai, Virtual power plant-based distributed control strategy for multiple distributed generators, IET Control Theory & Applications 7 (1) (2013) 90–98.
• [18] S. You, C. Træholt, B. Poulsen, A market-based virtual power plant, in: Clean Electrical Power, 2009 International Conference on, IEEE, 2009, pp. 460–465.
• [19] J. K. Kok, C. J. Warmer, I. Kamphuis, Powermatcher: multiagent control in the electricity infrastructure, in: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, ACM, 2005, pp. 75–82.
• [20] R. T. Rockafellar, S. Uryasev, Conditional value-at-risk for general loss distributions, Journal of banking & finance 26 (7) (2002) 1443–1471.
• [21] N. Gülpınar, B. Rustem, R. Settergren, Simulation and optimization approaches to scenario tree generation, Journal of economic dynamics and control 28 (7) (2004) 1291–1315.
• [22] J. M. Morales, S. Pineda, A. J. Conejo, M. Carrion, Scenario reduction for futures market trading in electricity markets, Power Systems, IEEE Transactions on 24 (2) (2009) 878–888.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.