Coordinated Resource Scheduling in a Large Scale Virtual Power Plant Considering Demand Response and Energy Storages

Document Type : Research paper

Authors

1 Mazandaran University of Science and Technology

2 Noshirvani University of Technology

Abstract

Virtual power plant (VPP) is an effective approach to aggregate distributed generation resources under a central control. This paper introduces a mixed-integer linear programming model for optimal scheduling of the internal resources of a large scale VPP in order to maximize its profit. The proposed model studies the effect of a demand response (DR) program on the scheduling of the VPP. The profit of the VPP is calculated considering different components including the income from the sale of electricity to the network and the incentives received by the renewable resources, fuel cost, the expense of the purchase of electricity from the network and the load curtailment cost during the scheduling horizon. The proposed model is implemented in a large scale VPP that consists of five plants in two cases: with and without the presence of the DR. Simulation results show that the implementation of the DR program reduces the operation cost in the VPP, therefore increasing its profit.

Keywords

Main Subjects


[1]     L. I. Dulău, M. Abrudean, and D. Bică, “Effects of Distributed Generation on Electric Power Systems,” Procedia Tech., vol. 12, pp. 681-686, 2014.
[2]     M. A .Tajeddini, A. Rahimi-Kian, and A. Soroudi, “Risk averse optimal operation of a virtual power plant using two stage stochastic programming,” Energy, vol. 73, pp. 958-967, 2014.
[3]     S. R. Dabbagh and M. K. Sheikh-El-Eslami, “Risk-based profit allocation to DERs integrated with a virtual power plant using cooperative Game theory,” Electr. Power Syst. Res., vol. 121, pp. 368–378, 2015.
[4]     S. Pazouki, M. R. Haghifam, and S. Pazouki “Transition from fossil fuels power plants to ward virtual power plants of distribution networks,”in Proc. of the EPDC, Karaj, Iran, 2016,pp. 82-86.
[5]     P. Asmus, “Micro grids, virtual power plants and our distributed energy future,” Electr. J., vol. 23, no. 8, pp. 72-82, 2010.
[6]     M. Peik-Herfeh, H. Seifi, and M. K. Sheikh-El-Eslami, “Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method,” Electr. Power Energy Syst., vol. 44, no. 1, pp. 88-98, 2013.
[7]     H. Pandzic, J. M. Morales, A. J Conejo, and I. Kuzle, “Offering model for a virtual power plant based on stochastic programming,” App. Energy, vol.105, pp. 282-292, 2013.
[8]     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 Trans. Power Syst., vol. 26, no. 2, pp. 949-56, 2011.
[9]     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 Trans. Power Syst., vol. 26, no. 2, pp. 957-64, 2011.
[10]  P. Faria, J. Soares, Z. Vale, H. Morais, and T. Sousa, “Modified particle swarm optimization applied to integrated demand response and DG resources scheduling,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 606-616, 2013.
[11]  M. Giuntoli and D. Poli, “Optimal thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storage,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 942-955, 2013.
[12]  A. GH. Zamani, A. Zakariazadeh, S. Jadid, and A. kazemi, “Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant,” Int. J. Electr. Power Energy Syst., vol. 82, pp.608-620, 2016.
[13]  A. Yousefi, T. T. Nguyen, H. Zareipour, and O. P. Malik, “Congestion management using demand response and FACTS devices,” Electr. Power Energy Syst., vol. 37, no. 1, pp. 78-85, 2012.
[14]  Federal Energy Regulatory Commission Staff, “Assessment of demand response and advanced metering,” FERC, 2007.
[15]  N. Çiçek and H. Deliç, “Demand response management for smart grids with wind power,” IEEE Trans. Sust. Energy, vol. 6, no. 2, pp. 625-634, 2015.
[16]  Q. Wang, J. Wang, and Y. Guan, “Stochastic unit commitment with uncertain demand response,” IEEE Trans. Power Syst.,vol. 28, no. 1, pp. 562-563, 2013.
[17]  M. Fathi and H. Bevrani, “Adaptive energy consumption scheduling for connected micro grids under demand uncertainty,” IEEE Trans. Power Del., vol. 28, no. 3, pp. 1576-1583, 2013.
[18]  P. R. Thimmapuram and J. Kim, “Consumers’ price elasticity of demand modeling with economic effects on electricity markets using an agent based model,IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 390-397, 2013.
[19]  E. Dehnavi, H. Abdi, and F. Mohammadi, “Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem”,J. Oper. Autom. Power Eng., vol. 4, no. 1, pp.29-41, 2016.
[20]  H. Arasteh, M. S. Sepasian and V. Vahidinasab, “Toward a smart distribution system expansion planning by considering demand response resources,”J. Oper. Autom. Power Eng., vol. 3, no. 2, pp.116-130,2015.
[21]  L. Bajracharyay, S. Awasthi, S. Chalise, T. M. Hansen, and R. Tonkoski, “Economic analysis of a data center virtual power plant participating in demand response,” Proc. Powe Energy Soc. General Meeting, Boston, MA, USA, 2016, pp. 1-5.
[22]  H.T. Nguyen and L.B. Le, “Bidding strategy for virtual power plant with intraday demand response rxchange market using stochastic programming,” Proc. IEEE Int. Conf. Sustain. Energy Tech., Hanoi, Vietnam, 2016, pp. 96-101.
[23]  A. Mnatsakanyan and S. W. Kennedy, “A novel demand response model with an application for a virtual power plant,” IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 230-237, 2014.
[24]  Available at:
Volume 6, Issue 1
June 2018
Pages 50-60
  • Receive Date: 25 December 2016
  • Revise Date: 12 August 2017
  • Accept Date: 15 September 2017
  • First Publish Date: 01 June 2018