Skip to main content
SpringerLink
Log in

Study on Modeling of Distributed Energy Resources in Smart Distribution System

  • Conference paper
  • First Online:
Proceedings of the Third International Forum on Decision Sciences

Part of the book series: Uncertainty and Operations Research ((UOR))

Abstract

Smart grid has been become a new type of electrical power system. This new system structure physically focuses on expansive capabilities of network operations to coordinate distributed energy resources (DERs). And we present strategies for adapting conventional system simulation methods to the new requirements of complex adapted system. In this paper, we emphasize the steady-state modeling of DERs models in detailed distribution system level and focus on the latest development of cumulative methods and make comparisons with conventional approaches. Distribution system load models are also discussed. And the load modeling of smart distribution is a key problem for smart distribution system modeling to find a reasonable way to represent residential or commercial end-use loads. Top-down and bottom-up techniques are both implemented into loads modeling procedure as aggregated tools.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R.E. Brown, in Impact of Smart Grid on Distribution System Design, Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century (IEEE, 2008), pp. 1–4

    Google Scholar 

  2. National Energy Technology Laboratory (2007-07-27) (pdf). A Vision for the Modern Grid. (http://www.netl.doe.gov/moderngrid/docs/A%20Vision%20for%20the%20Modern%20Grid_Final_v1_0.pdf). United States Department of Energy, p. 5. Retrieved 27 Nov 2008

  3. U.S. Department of Energy, National Energy Technology Laboratory, Modern Grid Initiative. http://www.netl.doe.gov/moderngrid/opportunity/vision_technologies.html

  4. U.S. Department of Energy. http://smartgrid.ieee.org/nist-smartgrid-framework

  5. I.A. Pecas, F.P. Maciel, I. Cidras, Simulation of MV distribution networks with asynchronous local generation sources, in Proceeding of IEEE Melecom 91, June 1991

    Google Scholar 

  6. A.E. Feijoo, J. Cidras, Modeling of wind farms in the load flow analysis. IEEE Trans. Power Syst. 15(1), 110–115 (2000)

    Article  Google Scholar 

  7. R. Jayashria, R.P.K. Devib, Steady state analysis of wind turbine generators interconnected to the grid, in Power Systems Conference and Exposition, 2006. PSCE ‘06. 2006 IEEE PES, pp. 1273–1279

    Google Scholar 

  8. M. Nagao, K. Harada, Power flow of photovoltaic system using buck-boost PWM power inverter, in Proceedings of 1997 International Conference on Power Electronics and Drive Systems

    Google Scholar 

  9. M. Djarallah, B. Azoui, Grid connected interactive photovoltaic power flow analysis: a technique for system operation comprehension and sizing, in Proceedings of 2006 of the 41st International Universities Power Engineering Conference, pp. 69–73, 2006

    Google Scholar 

  10. W. Yi-Bo, W. Chun-Sheng, L. Hua, X. Hong-Hua, Steady-state model and power flow analysis of grid-connected photovoltaic power system, in IEEE International Conference on Industrial Technology, pp. 1–6, 2008. ICIT 2008

    Google Scholar 

  11. Razvan Stoicescu, Karen Miu, Chika O. Nwankpa, Dagmar Niebur, Xiaoguang Yang, Three-phase converter models for unbalanced radial power-flow studies. IEEE Trans. Power Syst. 17(4), 1016–1021 (2002)

    Article  Google Scholar 

  12. T. Das, D.C. Aliprantis, Small-signal stability analysis of power system integrated with PHEVs. IEEE Energy 2030 Atlanta, GA USA 17–18 Nov 2008

    Google Scholar 

  13. R. Garcia-Valle, J.G. Vlachogiannis, Electric vehicle demand model for load flow studies. Elect. Power Compon. Syst. 37(5), 577–582 (2009)

    Article  Google Scholar 

  14. I. Adan, J. Resing, Queueing theory, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven (2002)

    Google Scholar 

  15. E.W.C. Lo, D. Sustanto, C.C. Fok, Harmonic load flow study for electric vehicle chargers, in Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems, 1999. PEDS ‘99, pp 495–500

    Google Scholar 

  16. J. Hetzer, D.C. Yu, K. Bhattarai, An economic dispatch model incorporating wind power. IEEE Trans. Energy Convers. 23(2), 603–611 (2008)

    Article  Google Scholar 

  17. N.D. Hatziargyriou, T.S. Karakatsanis, M. Papadopoulos, Probabilistic load flow in distribution systems containing dispersed wind power generation. IEEE Trans. on Power Syst. 8(1), 159–165 (1993)

    Article  Google Scholar 

  18. C.K. Ho, G.J. Kolb, Incorporating uncertainty into probabilistic performance models of concentrating solar power plants. J. Solar Energy Eng. Trans. ASME 132, 1–8 (2010)

    Article  Google Scholar 

  19. J.G. Vlachogiannis, Probabilistic constrained load flow considering integration of wind power generation and electric vehicles. IEEE Trans. Power Syst. 24(4), (2009, Nov)

    Google Scholar 

  20. J.M. Morales, L. Baringo, A.J. Conejo, R. Minguez, Probabilistic power flow with correlated wind sources. IET Gener. Transm. Distrib. 641–651 (2010)

    Google Scholar 

  21. P. Jorgensen, J.S. Christensen, J.O. Tande, Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines, in Proceedings of IEEE Eighth International Conference Harmonics and Quality of Power, Athens, Greece, Oct 1998, pp. 1146–1151

    Google Scholar 

  22. K.C. Divya, Load flow analysis considering wind turbine generator power uncertainties, in Proceedings Nordic Wind Power Conference, Risø National Laboratory Publication (2007)

    Google Scholar 

  23. Z. Wang, F.L. Alvarado, Interval arithmetic in power flow analysis. Proc. Power Ind. Comput. Appl. Power Syst. 5(3), 182–190 (1990)

    Google Scholar 

  24. V. Miranda, J.T. Saraiva, Fuzzy modeling of power system optimal power flow. IEEE Trans. Power Syst. 7(2), 843–849 (1992)

    Article  Google Scholar 

  25. S. Persaud, B. Fox, D. Flynn, Impact of remotely connected wind turbines on steady state operation of radial distribution networks. Proc. Inst. Elect. Eng. Gen. Transm. Distrib. 147(3), 157–163 (2000)

    Article  Google Scholar 

  26. T. Boehme, A. Robin Wallace, G.P. Harrison, Applying time series to power flow analysis in networks with high wind penetration. IEEE Trans. Power Syst. 22(3), 951–957 (2007)

    Article  Google Scholar 

  27. N. Okada, T. Nanahara, K. Kurokawa, Estimation of distribution system load characteristics with time series data of PV system output, in Proceedings of 3rd World Conference on Photovoltaic Energy Conversion, vol. 3 (2003) pp. 2288–2289

    Google Scholar 

  28. N.D. Hatziargyriou, T.S. Karakatsanis, M. Papadopoulos, Probabilistic calculations of aggregate storage heating loads. IEEE Trans. Power Deliv. 5, 1520–1526 (1990)

    Article  Google Scholar 

  29. S.W. Heunis, R. Herman, A probabilistic model for residential consumer loads. IEEE Trans. Power Syst. 17(3) (2002, Aug)

    Google Scholar 

  30. N.E. Ryan, S.D. Braithwait, J.T. Powers, B.A. Smith, Generalizing direct load control program analysis: implementation of the duty cycle approach. IEEE Trans. Power Syst. 4, 293–299 (1989)

    Article  Google Scholar 

  31. A. Pahwa, C.W. Brice, Modeling and system identification of residential air conditioning load. IEEE Trans. Power App. Syst. PAS-104(6), 1418–1425 (1985)

    Article  Google Scholar 

  32. C.Y. Chong, R. Malhamé, Statistical synthesis of physically based load models with applications to cold load pickup. IEEE Trans. Power App. Syst. PAS-103(7), 1612–1628 (1985)

    Google Scholar 

  33. D.S. Callaway, Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Convers. Manag. 50, 1389–1400 (2009)

    Article  Google Scholar 

  34. A. Capasso, W. Grattieri, R. Lamedica, A. Prudenzi, A bottom-up approach to residential load modeling. IEEE Trans.Power Syst. 9(2) (1994, May)

    Google Scholar 

  35. Y. Manichaikul, F.C. Schweppe, Physically based industrial electric load. IEEE Trans. Power App. Syst. PAS-98(4) (1979, July/Aug)

    Google Scholar 

  36. R.G. Pratt, T. Taylor, Development and testing of an equivalent thermal parameter model of commercial buildings from time-series end-use data (Pacific Northwest Laboratory, Richland, WA, 1994)

    Google Scholar 

  37. Z.T. Taylor, R.G. Pratt, The effects of model simplifications on equivalent thermal parameters calculated from hourly building performance data. in Proceedings of the I988 ACEEE Summer Study on Energy Eficiency in Buildings, Aug 1988, pp. 10.268–10.285

    Google Scholar 

  38. P.R. Armstrong, S.B. Leeb, L.K. Norford, Control with building mass—Part II: Simulation. ASHRAE Trans. 112(1) (2006)

    Google Scholar 

  39. P.R. Armstrong, Model identification with application to building control and fault detection. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 2004

    Google Scholar 

  40. J.E. Braun, Load control using building thermal mass. J. Sol. Energy Eng. 125, 292–301 (2003)

    Article  Google Scholar 

  41. A. Molina-Garcia, M. Kessler, J.A. Fuentes, E. Gomez-Lazaro, Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs. IEEE Trans. Power Syst. (This article has been accepted for inclusion in a future issue of this journal)

    Google Scholar 

  42. M.L. Chan, G.B. Ackerman, Simulation-based load synthesis methodology for evaluating load-management programs. IEEE Trans. Power App. Syst. PAS-100(4), 1771–1778 (1981)

    Article  Google Scholar 

  43. S. Ihara, F.C. Schweppe, Physically based modeling of cold load pickup. IEEE Trans. Power App. Syst. PAS-100(9), 4142–4150 (1981)

    Article  Google Scholar 

  44. T. Calloway, C. Brice, Physically-based model of demand with applications to load management assessment and load forecasting. IEEE Trans. Power App. Syst. PAS-100(12), 4625–4630 (1982)

    Article  Google Scholar 

  45. C.Y. Chong, R. Malhamé, Statistical synthesis of physically based load models with applications to cold load pickup. IEEE Trans. Power App. Syst. PAS-100(7), 1612–1628 (1984)

    Google Scholar 

  46. M.H. Nehrir, P.S. Dolan, V. Gerez, W.J. Jameson, Development and validation of a physically-based computer model for predicting winter electric heating loads. IEEE Trans. Power Syst. 10(1), 266–272 (1995)

    Article  Google Scholar 

  47. C. Alvarez, R. Malhamé, A. Gabaldón, A class of models for load management application and evaluation revisited. IEEE Trans. Power Syst. 7(4), 1435–1443 (1992)

    Article  Google Scholar 

  48. J.C. Laurent, R.P. Malhamé, A physically-based computer model of aggregate electric water heating loads. IEEE Trans. Power Syst. 9(3), 1209–1217 (1994)

    Article  Google Scholar 

  49. R.E. Mortensen, K.P. Haggerty, A stochastic computer model for heating and cooling loads. IEEE Trans. Power Syst. 3(3), 1213–1219 (1988)

    Article  Google Scholar 

  50. S. Srinivasan, A. Chandrasekaran, A.T. Alouani, Validation of applying the maximum likelihood duty cycle forecast for residential load aggregation, in Proceedings of XXV Southeastern Symposium on System Theory, 1993, vol. 1, pp. 119–123

    Google Scholar 

  51. A. Molina-Garcia, A. Gabaldón, J.A. Fuentes, C. Álvarez, Implementation and assessment of physically based electrical load models: application to direct load control residential programmes. Proc. Inst. Elect. Eng. Gen. Transm. Distrib. 150(1), 61–66 (2003)

    Article  Google Scholar 

  52. J. Fuentes, A. Molina-Garcia, A. Gabaldon, E. Gomez-Lazaro, C. Alvarez, An integrated tool for assessing the demand profile flexibility. IEEE Trans. Power Syst. 19(1), 668–675 (2004)

    Article  Google Scholar 

  53. N. Lu, D.P. Chassin, A state-queueing model of thermostatically controlled appliances. IEEE Trans. Power Syst. 4(3), 1666–1673 (2004)

    Article  Google Scholar 

  54. N. Lu, D.P. Chassin, S.E. Widergren, Modeling uncertainties in aggregated thermostatically controlled loads using a state queueing model. IEEE Trans. Power Syst. 20(2), 725–733 (2005)

    Article  Google Scholar 

  55. R.T. Guttromson, D.P. Chassin, S.E. Widergren, Residential energy resource models for distribution feeder simulation, in Power Engineering Society General Meeting (IEEE, 2003)

    Google Scholar 

  56. N. Motegi, M.A. Piette, D.S. Watson, S. Kiliccote, P. Xu, Introduction to Commercial Building Control Strategies and Techniques for Demand Response, Lawrence Berkeley National Laboratory, Berkeley Hills

    Google Scholar 

Download references

Acknowledgments

This work is supported in part by the science and technology projects of State Grid Corporation of China: Research on power market model, structure and construction path, project no. SGERI06KJ(2013)51.

Author information

Authors and Affiliations

  1. State Grid Energy Research Institute, Changping District, Beijing, 102209, China

    Menghua Fan, Zhe Wei & Su Yang

Authors
  1. Menghua Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhe Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Su Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Menghua Fan .

Editor information

Editors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technolog, Beijing, China

    Xiang Li

  2. School of Economics and Management, China University of Petroleum, Qingdao, Shandong, China

    Xiaofeng Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Fan, M., Wei, Z., Yang, S. (2016). Study on Modeling of Distributed Energy Resources in Smart Distribution System. In: Li, X., Xu, X. (eds) Proceedings of the Third International Forum on Decision Sciences. Uncertainty and Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-10-0209-0_1

Download citation

Publish with us

Policies and ethics

Search

Navigation