Abstract
Nowadays, the sustainable energy management of industrial environments is of great importance because of their heavy loads and behaviors. In this paper, the Virtual Power Plant (VPP) idea is commented as a collected generation to be an appropriate approach for these networks handling. Here, Technical Industrial VPP (TIVPP) is characterized as a dispatching unit contains demands and generations situated in an industrial network. A complete structure is proposed here for possible conditions for different VPPs cooperation in the power market. This structure carries out a day-ahead and intra-day generation planning by choosing the best Demand Response (DR) programs considering wind power and market prices as the uncertain parameters. A risk management study is likewise taken into account in the proposed stages for contingency conditions. So, some component changes, like, regular demand changes and single-line outage are prepared in the framework to authorize the suggested concept in the contingency situation. To determine the adequacy and productivity of the proposed strategy, the IEEE-RTS modified framework is examined to test the technique and to evaluate some reassuring perspectives too. By the proposed methodology, the delectability of DR projects is uncovered in industrial networks and the improvement level of load shedding and the lower cost will be achieved.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Nosratabadi SM, Hooshmand RA, Gholipour E. A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renew Sustain Energy Rev 2017;67:341–63. https://doi.org/10.1016/j.rser.2016.09.025.Search in Google Scholar
2. Yildiz B, Bilbao JI, Sproul AB. A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renew Sustain Energy Rev 2017;73:1104–22. https://doi.org/10.1016/j.rser.2017.02.023.Search in Google Scholar
3. Moutis P, Hatziargyriou ND. Decision trees aided scheduling for firm power capacity provision by virtual power plants. Int J Electr Power Energy Syst 2014;63:730–9. https://doi.org/10.1016/j.ijepes.2014.06.038.Search in Google Scholar
4. Ahmad T, Ul Hasan Q. Detection of frauds and other non-technical losses in power utilities using smart meters: a review. Int J Emerg Elec Power Syst 2016;17:217–34. https://doi.org/10.1515/ijeeps-2015-0206.Search in Google Scholar
5. Moghaddam IG, Nick M, Fallahi F, Sanei M, Mortazavi S. Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market. Renew Energy 2013;55:252–9. https://doi.org/10.1016/j.renene.2012.12.023.Search in Google Scholar
6. Zapata J, Vandewalle J, D'Haeseleer W. A comparative study of imbalance reduction strategies for virtual power plant operation. Appl Therm Eng 2014;71:847–57. https://doi.org/10.1016/j.applthermaleng.2013.12.026.Search in Google Scholar
7. Pandžić H, Morales JM, Conejo AJ, Kuzle I. Offering model for a virtual power plant based on stochastic programming. Appl Energy 2013;105:282–92. https://doi.org/10.1016/j.apenergy.2012.12.077.Search in Google Scholar
8. Shabanzadeh M, Sheikh-El-Eslami MK, Haghifam MR. The design of a risk-hedging tool for virtual power plants via robust optimization approach. Appl Energy 2015;155:766–77. https://doi.org/10.1016/j.apenergy.2015.06.059.Search in Google Scholar
9. Gourlis G, Kovacic I. Building Information Modelling for analysis of energy efficient industrial buildings – A case study. Renew Sustain Energy Rev 2017;68:953–63. https://doi.org/10.1016/j.rser.2016.02.009.Search in Google Scholar
10. Yun L, Huanhai X, Zhen W, Deqiang G. Control of virtual power plant in microgrids: a coordinated approach based on photovoltaic systems and controllable loads. Gener Transm Distrib, IET 2015;9:921–8. https://doi.org/10.1049/iet-gtd.2015.0392.Search in Google Scholar
11. Skarvelis-Kazakos S, Rikos E, Kolentini E, Cipcigan LM, Jenkins N. Implementing agent-based emissions trading for controlling Virtual Power Plant emissions. Elec Power Syst Res 2013;102:1–7. https://doi.org/10.1016/j.epsr.2013.04.004.Search in Google Scholar
12. Sučić S, Dragičević T, Capuder T, Delimar M. Economic dispatch of virtual power plants in an event-driven service-oriented framework using standards-based communications. Elec Power Syst Res 2011;81:2108–19. https://doi.org/10.1016/j.epsr.2011.08.008.Search in Google Scholar
13. Pandžić H, Kuzle I, Capuder T. Virtual power plant mid-term dispatch optimization. Appl Energy 2013;101:134–41. https://doi.org/10.1016/j.apenergy.2012.05.039.Search in Google Scholar
14. Tascikaraoglu A., Erdinc O, Uzunoglu M, Karakas A. An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units. Appl Energy 2014;119:445–53. https://doi.org/10.1016/j.apenergy.2014.01.020.Search in Google Scholar
15. Yang H, Yi D, Zhao J, Luo F, Dong Z. Distributed optimal dispatch of virtual power plant based on ELM transformation. Ind Manag Optim 2014;10:1297–318. https://doi.org/10.3934/jimo.2014.10.1297.Search in Google Scholar
16. Sowa T, Krengel S, Koopmann S, Nowak J. Multi-criteria operation strategies of power-to-heat-systems in virtual power plants with a high penetration of renewable energies. Energy Procedia 2014;46:237–45. https://doi.org/10.1016/j.egypro.2014.01.178.Search in Google Scholar
17. Papaefthymiou SV, Papathanassiou SV. Optimum sizing of wind-pumped-storage hybrid power stations in island systems. Renew Energy 2014;64:187–96. https://doi.org/10.1016/j.renene.2013.10.047.Search in Google Scholar
18. Yu J, Jiao Y, Wang X, Cao J, Fei S. Bi-level optimal dispatch in the Virtual Power Plant considering uncertain agents number. Neurocomputing 2015;167:551–7. https://doi.org/10.1016/j.neucom.2015.04.035.Search in Google Scholar
19. Mnatsakanyan A, Kennedy SW. A novel demand response model with an application for a virtual power plant. IEEE Trans Smart Grid 2015;6:230–7. https://doi.org/10.1109/tsg.2014.2339213.Search in Google Scholar
20. Dietrich K, Latorre JM, Olmos L, Ramos A. Modelling and assessing the impacts of self supply and market-revenue driven Virtual Power Plants. Elec Power Syst Res 2015;119:462–70. https://doi.org/10.1016/j.epsr.2014.10.015.Search in Google Scholar
21. Shafie-khah M, Parsa Moghaddam M, Sheikh-El-Eslami MK, Rahmani-Andebili M. Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment. Energy 2012;40:139–50. https://doi.org/10.1016/j.energy.2012.02.019.Search in Google Scholar
22. Vasirani M, Kota R, Cavalcante RLG, Ossowski S, Jennings NR. An agent-based approach to virtual power plants of wind power generators and electric vehicles. IEEE Trans Smart Grid 2013;4:1314–22. https://doi.org/10.1109/tsg.2013.2259270.Search in Google Scholar
23. Arslan O, Karasan OE. Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks. Energy 2013;60:116–24. https://doi.org/10.1016/j.energy.2013.08.039.Search in Google Scholar
24. Yu S, Fang F, Liu Y, Liu J. Uncertainties of virtual power plant: Problems and countermeasures. Appl Energy 2019;239:454–70. https://doi.org/10.1016/j.apenergy.2019.01.224.Search in Google Scholar
25. Adu-Kankam KO, Camarinha-Matos LM. Towards collaborative Virtual Power Plants: Trends and convergence. Sustain Energy, Grids Networks 2018;16:217–30. https://doi.org/10.1016/j.segan.2018.08.003.Search in Google Scholar
26. Nosratabadi SM, Hooshmand RA, Gholipour E, Parastegari M. A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant. Int Trans Electr Energy Syst 2016;26:1103–20. https://doi.org/10.1002/etep.2128.Search in Google Scholar
27. Liu Y, Li M, Lian H, Tang X, Liu C, Jiang C. Optimal dispatch of virtual power plant using interval and deterministic combined optimization. Int J Electr Power Energy Syst 2018;102:235–44. https://doi.org/10.1016/j.ijepes.2018.04.011.Search in Google Scholar
28. Luo Z, Hong SH, Ding YM. A data mining-driven incentive-based demand response scheme for a virtual power plant. Appl Energy 2019;239:549–59. https://doi.org/10.1016/j.apenergy.2019.01.142.Search in Google Scholar
29. Xiao J, Kong X, Jin Q, You H, Cui K, Zhang Y. Demand-responsive Virtual Power Plant optimization scheduling method based on competitive bidding equilibrium. Energy Procedia 2018;152:1158–63. https://doi.org/10.1016/j.egypro.2018.09.151.Search in Google Scholar
30. Hadayeghparast S, SoltaniNejad Farsangi A, Shayanfar H. Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant. Energy 2019;172:630–46. https://doi.org/10.1016/j.energy.2019.01.143.Search in Google Scholar
31. IRENA. Renewable energy technologies: cost analysis series, 2012. Available from: www.irena.org.Search in Google Scholar
32. Momoh James A, Salkuti Surender R. Feasibility of stochastic voltage/VAr optimization considering renewable energy resources for smart grid. Int J Emerg Elec Power Syst 2016;17:287–300. https://doi.org/10.1515/ijeeps-2016-0009.Search in Google Scholar
33. Emelogu A, Chowdhury S, Marufuzzaman M, Bian L, Eksioglu B. An enhanced sample average approximation method for stochastic optimization. Int J Prod Econ 2016;182:230–52. https://doi.org/10.1016/j.ijpe.2016.08.032.Search in Google Scholar
34. Schweppe FC, Caramanis MC, Tabors RD, Bohn RE. Spot pricing of electricity. Kluwer Academic Publishers; 1989.10.1007/978-1-4613-1683-1Search in Google Scholar
35. Moghaddam MP, Abdollahi A, Rashidinejad M. Flexible demand response programs modeling in competitive electricity markets. Appl Energy 2011;88:3257–69. https://doi.org/10.1016/j.apenergy.2011.02.039.Search in Google Scholar
36. Mohammad S, Yamin H, Li Z. Market operations in electric power systems: forecasting, scheduling, and risk management. Wiley-IEEE Press; 2002.Search in Google Scholar
37. Aalami HA, Parsa Moghaddam M, Yousefi GR. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 2010;87:243–50. https://doi.org/10.1016/j.apenergy.2009.05.041.Search in Google Scholar
38. Aalami HA, Parsa Moghaddam M, Yousefi GR. Modeling and prioritizing demand response programs in power markets. Elec Power Syst Res 2010;80:426–35. https://doi.org/10.1016/j.epsr.2009.10.007.Search in Google Scholar
39. Motto AL, Galiana FD, Conejo AJ, Arroyo JM. Network-constrained multiperiod auction for a pool-based electricity market. IEEE Trans Power Syst 2002;17:646–53. https://doi.org/10.1109/TPWRS.2002.800909.Search in Google Scholar
40. Valero S, Ortiz M, Senabre C, Alvarez C, Franco FJG, Gabaldon A. Methods for customer and demand response policies selection in new electricity markets. Gener Transm Distrib, IET 2007;1:104–10. https://doi.org/10.1049/iet-gtd:20060183.10.1049/iet-gtd:20060183Search in Google Scholar
41. Nguyen Minh Y, Nguyen Duc M. A generalized formulation of demand response under market environments. Int J Emerg Elec Power Syst 2015;16:217–24. https://doi.org/10.1515/ijeeps-2014-0147.Search in Google Scholar
42. Kanungo T., Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 2002;24:881–92. https://doi.org/10.1109/TPAMI.2002.1017616.Search in Google Scholar
43. Pandžić H, Qiu T, Kirschen DS. Comparison of state-of-the-art transmission constrained unit commitment formulations. In: 2013 IEEE power & energy society general meeting; 21–25 July 2013.10.1109/PESMG.2013.6672719Search in Google Scholar
44. GAMS. A user's guide, 2014. [Online]. Available from: www.gams.com/dd/docs/bigdocs/GAMSUsersGuide.pdf.Search in Google Scholar
45. Parvania M, Fotuhi-Firuzabad M. Integrating load reduction into wholesale energy market with application to wind power integration. IEEE Syst J 2012;6:35–45. https://doi.org/10.1109/jsyst.2011.2162877.Search in Google Scholar
46. Morales JM, Conejo AJ, Perez-Ruiz J. Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans Power Syst 2009;24:900–10. https://doi.org/10.1109/tpwrs.2009.2016598.Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
