Advertisement

Heterogeneous Air Conditioner Aggregation for Providing Operating Reserve Considering Price Signals

  • Yi DingEmail author
  • Yonghua Song
  • Hongxun Hui
  • Changzheng Shao
Chapter
First Online:

Abstract

Thermostatically controlled loads (TCLs) have been studied to provide operating reserve for maintaining power balance between supply and demand. However, operating reserve capacity (ORC) supplied by aggregated TCLs is difficult to evaluate, due to the insufficient information of heterogeneous TCLs and consumer behaviours. This chapter proposes a quantitative ORC evaluation method for large-scale aggregated heterogeneous TCLs without sufficient measurement data. Firstly, an individual TCL model on account of consumer behaviours is developed to characterize the impact of fluctuated electricity prices and different thermal comfort requirements. Secondly, a novel optimization model of heterogeneous TCLs, which can guarantee consumer satisfaction, is proposed to provide operating reserve for power systems. Thirdly, the probability density estimation (PDE) method is developed to evaluate the ORC provided by large-scale heterogeneous TCLs with insufficient data. Numerical studies illustrate the effectiveness of the proposed models and methods.

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

References

  1. 1.
    O. Ma, N. Alkadi, P. Cappers, P. Denholm, J. Dudley, S. Goli, M. Hummon, S. Kiliccote, J. MacDonald, N. Matson, D. Olsen, C. Rose, M.D. Sohn, M. Starke, B. Kirby, M. O’Malley, Demand response for ancillary services. IEEE Trans. Smart Grid 4(4), 1988–1995 (2013)CrossRefGoogle Scholar
  2. 2.
    J. Wang, X. Wang, Y. Wu, Operating reserve model in the power market. IEEE Trans. Power Syst. 20(1), 223–229 (2005)CrossRefGoogle Scholar
  3. 3.
    H. Hui, Y. Ding, D. Liu, Y. Lin, Y. Song, Operating reserve evaluation of aggregated air conditioners. Appl. Energy 196, 218–228 (2017)CrossRefGoogle Scholar
  4. 4.
    Z. Bricman Rejc, M. Cepin, Estimating the additional operating reserve in power systems with installed renewable energy sources. Int. J. Electr. Power Energy Syst. 62, 654–664 (2014)CrossRefGoogle Scholar
  5. 5.
    P. Siano, D. Sarno, Assessing the benefits of residential demand response in a real time distribution energy market. Appl. Energy 161, 533–551 (2016)CrossRefGoogle Scholar
  6. 6.
    J. Hu, J. Cao, Z.Q.M. Chen, J. Yu, J. Yao, S. Yang, T. Yong, Load following of multiple heterogeneous TCL aggregators by centralized control. IEEE Trans. Power Syst. 32(4), 3157–3167CrossRefGoogle Scholar
  7. 7.
    W. Zhang, J. Lian, C.Y. Chang, K. Kalsi, Aggregated modeling and control of air conditioning loads for demand response. IEEE Trans. Power Syst. 28(4), 4655–4664 (2013)CrossRefGoogle Scholar
  8. 8.
    J.C. Hwang, Assessment of air condition load management by load survey in Taipower. IEEE Trans. Power Syst. 16(4), 910–915 (2001)CrossRefGoogle Scholar
  9. 9.
    K. Wang, J. Yao, L. Yao, S. Yang, T. Yong, Survey of research on flexible loads scheduling technologies. Autom. Electr. Power Syst. 38(20), 127–136 (2014)Google Scholar
  10. 10.
    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(5), 1389–1400 (2009)CrossRefGoogle Scholar
  11. 11.
    D. Papadaskalopoulos, G. Strbac, P. Mancarella, M. Aunedi, V. Stanojevic, Decentralized participation of flexible demand in electricity markets—Part II: application with electric vehicles and heat pump systems. IEEE Trans. Power Syst. 28(4), 3667–3674 (2013)CrossRefGoogle Scholar
  12. 12.
    K. Dehghanpour, S. Afsharnia, Electrical demand side contribution to frequency control in power systems: a review on technical aspects. Renew. Sustain. Energy Rev. 41, 1267–1276 (2015)CrossRefGoogle Scholar
  13. 13.
    N. Lu, Y. Zhang, Design considerations of a centralized load controller using thermostatically controlled appliances for continuous regulation reserves. IEEE Trans. Smart Grid 4(2), 914–921 (2013)CrossRefGoogle Scholar
  14. 14.
    M.L. Zheng, R.Y. Fang, Z.T. Yu, Life cycle assessment of residential heating systems: a comparison of distributed and centralized systems, in Applied Energy Symposium and Forum 2016: Low Carbon Cities & Urban Energy Systems (2016)Google Scholar
  15. 15.
    F. Luo, Z.Y. Dong, K. Meng, J. Wen, H. Wang, J. Zhao, An operational planning framework for large-scale thermostatically controlled load dispatch. IEEE Trans. Ind. Inform. 13(1), 217–227 (2017)CrossRefGoogle Scholar
  16. 16.
    N. Lu, An evaluation of the HVAC load potential for providing load balancing service. IEEE Trans. Smart Grid 3(3), 1263–1270 (2012)CrossRefGoogle Scholar
  17. 17.
    G. Bianchini, M. Casini, A. Vicino, D. Zarrilli, Demand-response in building heating systems: a model predictive control approach. Appl. Energy 168, 159–170 (2016)CrossRefGoogle Scholar
  18. 18.
    M. Liu, Y. Shi, Model predictive control of aggregated heterogeneous second-order thermostatically controlled loads for ancillary services. IEEE Trans. Power Syst. 31(3), 1963–1971 (2016)CrossRefGoogle Scholar
  19. 19.
    X. He, N. Keyaerts, I. Azevedo, L. Meeus, L. Hancher, J.M. Glachant, How to engage consumers in demand response: a contract perspective. Util. Policy 27, 108–122 (2013)CrossRefGoogle Scholar
  20. 20.
    D. Qv, B.B. Dong, L. Cao, L. Ni, J.J. Wang, R.X. Shang, Y. Yao, An experimental and theoretical study on an injection-assisted air-conditioner using R32 in the refrigeration cycle. Appl. Energy 185, 791–804 (2017)CrossRefGoogle Scholar
  21. 21.
    R. Yu, W. Yang, S. Rahardja, A statistical demand-price model with its application in optimal real-time price. IEEE Trans. Smart Grid 3(4), 1734–1742 (2012)CrossRefGoogle Scholar
  22. 22.
    D.P. Chassin, D. Rondeau, Aggregate modeling of fast-acting demand response and control under real-time pricing. Appl. Energy 181, 288–298 (2016)CrossRefGoogle Scholar
  23. 23.
    N. Alibabaei, A.S. Fung, K. Raahemifar, A. Moghimi, Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons. Appl. Energy 185, 29–43 (2016)CrossRefGoogle Scholar
  24. 24.
    C. Gu, X. Yan, Z. Yan, F. Li, Dynamic pricing for responsive demand to increase distribution network efficiency. Appl. Energy 205, 236–243 (2017)CrossRefGoogle Scholar
  25. 25.
    H. Allcott, Rethinking real-time electricity pricing. Resour. Energy Econ. 33(4), 820–842 (2011)CrossRefGoogle Scholar
  26. 26.
    J.A. Gomez, M.F. Anjos, Power capacity profile estimation for building heating and cooling in demand-side management. Appl. Energy 191, 492–501 (2017)CrossRefGoogle Scholar
  27. 27.
    D. Xie, H. Hui, Y. Ding, Z. Lin, Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals. Appl. Energy 216, 338–347 (2018)CrossRefGoogle Scholar
  28. 28.
    H. Chao, Price-responsive demand management for a smart grid world. Electr. J. 23(1), 7–20 (2010)CrossRefGoogle Scholar
  29. 29.
    S. Ihara, F.C. Schweppe, Physically based modeling of cold load pickup. IEEE Trans. Power App. Syst. 100(9), 4142–4150 (1981)CrossRefGoogle Scholar
  30. 30.
    K. Bhattacharyya, M.L. Crow, A fuzzy logic based approach to direct load control. IEEE Trans. Power Syst. 11(2), 708–714 (1996)CrossRefGoogle Scholar
  31. 31.
    J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 655–685 (1993)CrossRefGoogle Scholar
  32. 32.
    T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)zbMATHCrossRefGoogle Scholar
  33. 33.
    J. Persky, Retrospectives: the ethology of homo economicus. J. Econ. Perspect. 9(2), 221–231 (1995)CrossRefGoogle Scholar
  34. 34.
    J.M. Morales, Point estimate schemes to solve the probabilistic power flow. Reliab. Eng. Syst. Saf. 22(4), 1594–1601 (2007)Google Scholar
  35. 35.
    V.A. Epanechnikov, Non-parametric estimation of a multivariate probability density. Theory Probab. Appl. 14, 153–158 (1969)MathSciNetCrossRefGoogle Scholar
  36. 36.
    A. Molina-García, M. Kessler, J.A. Fuentes, E. Gómez-Lázaro, Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs. IEEE Trans. Power Syst. 26(1), 241–251 (2011)CrossRefGoogle Scholar
  37. 37.
    A.W. Bowman, A. Azzalini, Applied Smoothing Techniques for Data Analysis (Oxford University Press Inc., New York, 1997)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Ding
    • 1
    Email author
  • Yonghua Song
    • 1
    • 2
  • Hongxun Hui
    • 1
  • Changzheng Shao
    • 1
  1. 1.Zhejiang UniversityHangzhouChina
  2. 2.University of MacauMacauChina

Personalised recommendations

Cite chapter

Buy options