Advertisement

Uncertainty Modeling of Distributed Energy Resources: Techniques and Challenges

  • Ying Zhang
  • Jianhui WangEmail author
  • Zhengshuo Li
Energy Markets (R Sioshansi and A Mousavian, Section Editors)
  • 43 Downloads
Part of the following topical collections:
  1. Topical Collection on Energy Markets

Abstract

Purpose of Review

Integration of distributed energy resources (DERs) brings huge challenges to distribution systems. Among many control room applications, distribution system state estimation (DSSE) is regarded as a key tool to establish the relationship between state variables and abundant measurements for system monitoring and analysis. The emergence of DERs poses multiple uncertainties, resulting in stringent requirements for system modeling and operation practices. This paper summarizes the state-of-the-art approaches, techniques, and challenges in the uncertainty modeling of DERs in practical power system and electricity market operations.

Recent Findings

DSSE has become increasingly important to realize appropriate monitoring and control for active distribution systems. The current research focuses on more precise and robust uncertainty modeling of multiple DERs in DSSE and the application of big data analytics. Probabilistic methods also emerge as a major research direction for these studies.

Summary

Accurate and effective modeling of DER uncertainty calls for holistic improvement. Moreover, machine learning and data-driven techniques exhibit great potential in such applications. Future work is expected to accurately capture the stochasticity and variability of DER outputs in the operational and market models, and thus lead to great economic benefits.

Keywords

Distributed energy resources Distribution system state estimation Electricity market Uncertainty modeling Distributed generation Machine learning 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Amini M, Almassalkhi M, editors. Trading off robustness and performance in receding horizon control with uncertain energy resources. 2018 Power Systems Computation Conference (PSCC); IEEE; 2018.Google Scholar
  2. 2.
    Jiayi H, Chuanwen J, Rong X. A review on distributed energy resources and MicroGrid. Renew Sust Energ Rev. 2008;12(9):2472–83.Google Scholar
  3. 3.
    Hammons TJ. Integrating renewable energy sources into European grids. Int J Electr Power Energy Syst. 2008;30(8):462–75.  https://doi.org/10.1016/j.ijepes.2008.04.010.Google Scholar
  4. 4.
    Distributed energy resources technical considerations for the bulk power system. 2018(Docket No. AD18–10-000).Google Scholar
  5. 5.
    El-Khattam W, Salama MM. Distributed generation technologies, definitions and benefits. Electr Power Syst Res. 2004;71(2):119–28.Google Scholar
  6. 6.
    •• Primadianto A, Lu C-NJIToPS. A review on distribution system state estimation. 2017;32(5):3875–83. A comprehensive review of DSSE. Google Scholar
  7. 7.
    Dehghanpour K, Wang Z, Wang J, Yuan Y, Bu F. A survey on state estimation techniques and challenges in smart distribution systems. IEEE Trans Smart Grid. to be punished.  https://doi.org/10.1109/TSG.2018.2870600.
  8. 8.
    Akorede MF, Hizam H, Pouresmaeil E. Distributed energy resources and benefits to the environment. Renew Sust Energ Rev. 2010;14(2):724–34.Google Scholar
  9. 9.
    Halu A, Scala A, Khiyami A, González MC. Data-driven modeling of solar-powered urban microgrids. Sci Adv. 2016;2(1):e1500700.Google Scholar
  10. 10.
    Emhemed AS GBaOA-L. Impact of high penetration of single-phase distributed energy resources on the protection of LV distribution networks. 2007 42nd International Universities Power Engineering Conference; Brighton 2007. p. 223–7.Google Scholar
  11. 11.
    De Martini P, Kristov L. Distribution systems in a high distributed energy resources future. United States. 2015.  https://doi.org/10.2172/1242415. https://www.osti.gov/servlets/purl/1242415.
  12. 12.
    • Baran ME, Kelley AW. State estimation for real-time monitoring of distribution systems. IEEE Trans Power Syst. 1994;9(3):1601–9 A classic and detailed description of DSSE. Google Scholar
  13. 13.
    Singh R, Manitsas E, Pal BC, Strbac G. A recursive Bayesian approach for identification of network configuration changes in distribution system state estimation. IEEE Trans Power Syst. 2010;25(3):1329–36.Google Scholar
  14. 14.
    Pappu SJ, Bhatt N, Pasumarthy R, Rajeswaran A. Identifying topology of low voltage distribution networks based on smart meter data. IEEE Trans Smart Grid. 2018;9(5):5113–22.Google Scholar
  15. 15.
    Wang S, Han L, Wu L. Uncertainty tracing of distributed generations via complex affine arithmetic based unbalanced three-phase power flow. IEEE Trans Power Syst. 2015;30(6):3053–62.Google Scholar
  16. 16.
    Pinson P, Kariniotakis G. Conditional prediction intervals of wind power generation. IEEE Trans Power Syst. 2010;25(4):1845–56.Google Scholar
  17. 17.
    Jabr RA. Minimum loss operation of distribution networks with photovoltaic generation. IET Renew Power Gener. 2014;8(1):33–44.Google Scholar
  18. 18.
    Fajardo OF, Vargas A. Reconfiguration of mv distribution networks with multicost and multipoint alternative supply, part ii: reconfiguration plan. IEEE Trans Power Syst. 2008;23(3):1401–7.Google Scholar
  19. 19.
    Liu J, Ponci F, Monti A, Muscas C, Pegoraro PA, Sulis S. Optimal meter placement for robust measurement systems in active distribution grids. IEEE Trans Instrum Meas. 2014;63(5):1096–105.Google Scholar
  20. 20.
    Muscas C, Pau M, Pegoraro PA, Sulis S. Effects of measurements and pseudo measurements correlation in distribution system state estimation. IEEE Trans Instrum Meas. 2014;63(12):2813–23.Google Scholar
  21. 21.
    Bejestani AK, Annaswamy A, Samad T. A hierarchical transactive control architecture for renewables integration in smart grids: analytical modeling and stability. IEEE Trans Smart Grid. 2014;5(4):2054–65.Google Scholar
  22. 22.
    Kristov L, De Martini P, Taft JD. A tale of two visions: designing a decentralized transactive electric system. IEEE Power Energy Mag. 2016;14(3):63–9.Google Scholar
  23. 23.
    Li Z, Guo Q, Sun H, Wang J. Coordinated economic dispatch of coupled transmission and distribution systems using heterogeneous decomposition. IEEE Trans Power Syst. 2016;31(6):4817–30.Google Scholar
  24. 24.
    • Ghosh AK, Lubkeman DL, Downey MJ, Jones RH. Distribution circuit state estimation using a probabilistic approach. IEEE Trans Power Syst. 1997;12(1):45–51 A classic description of a probability approach on DSSE. Google Scholar
  25. 25.
    Brinkmann B, Negnevitsky M. A probabilistic approach to observability of distribution networks. IEEE Trans Power Syst. 2017;32(2):1169–78.Google Scholar
  26. 26.
    Liu J, Tang J, Ponci F, Monti A, Muscas C, Pegoraro PA. Trade-offs in PMU deployment for state estimation in active distribution grids. IEEE Trans Smart Grid. 2012;3(2):915–24.Google Scholar
  27. 27.
    Džafić I, Jabr RA. Real time multiphase state estimation in weakly meshed distribution networks with distributed generation. IEEE Trans Power Syst. 2017;32(6):4560–9.Google Scholar
  28. 28.
    Weng Y, Negi R, Ilić MD. Probabilistic joint state estimation for operational planning. IEEE Trans Smart Grid. 2017.Google Scholar
  29. 29.
    Arefi A, Ledwich G, Behi B. An efficient DSE using conditional multivariate complex Gaussian distribution. IEEE Trans Smart Grid. 2015;6(4):2147–56.Google Scholar
  30. 30.
    Kuhar U, Pantoš M, Kosec G, Švigelj A. The impact of model and measurement uncertainties on a state estimation in three-phase distribution networks. IEEE Trans Smart Grid. 2018:1.Google Scholar
  31. 31.
    Rakpenthai C, Uatrongjit S, Premrudeepreechacharn S. State estimation of power system considering network parameter uncertainty based on parametric interval linear systems. IEEE Trans Power Syst. 2012;27(1):305–13.Google Scholar
  32. 32.
    Khosravi A, Nahavandi S, Creighton D, Atiya AF. Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans Neural Netw. 2011;22(3):337–46.Google Scholar
  33. 33.
    • Al-Othman A, Irving M. A comparative study of two methods for uncertainty analysis in power system state estimation. IEEE Trans Power Syst. 2005;20(2):1181–2 An initial and comparative study between constrained nonlinear and linear methods for estimating the uncertainty interval in power system state estimation. Google Scholar
  34. 34.
    Chen P, Tao S, Xiao X, Li L. Uncertainty level of voltage in distribution network: an analysis model with elastic net and application in storage configuration. IEEE Trans Smart Grid. 2018;9(4):2563–73.Google Scholar
  35. 35.
    Xu J, Wu Z, Dou X, Hu Q, editors. An interval arithmetic-based state estimation for unbalanced active distribution networks. Power & Energy Society General Meeting, 2017 IEEE; 2017: IEEE.Google Scholar
  36. 36.
    Ding T, Li F, Li X, Sun H, Bo R. Interval radial power flow using extended DistFlow formulation and Krawczyk iteration method with sparse approximate inverse preconditioner. IET Gener Transm Distrib. 2015;9(14):1998–2006.Google Scholar
  37. 37.
    Vaccaro A, Canizares CA, Villacci D. An affine arithmetic-based methodology for reliable power flow analysis in the presence of data uncertainty. IEEE Trans Power Syst. 2010;25(2):624–32.Google Scholar
  38. 38.
    Angioni A, Schlösser T, Ponci F, Monti A. Impact of pseudo-measurements from new power profiles on state estimation in low-voltage grids. IEEE Trans Instrum Meas. 2016;65(1):70–7.Google Scholar
  39. 39.
    Wang H, Zhang W, Liu Y. A robust measurement placement method for active distribution system state estimation considering network reconfiguration. IEEE Trans Smart Grid. 2018;9(3):2108–17.Google Scholar
  40. 40.
    Wang B, He G, Liu K, Lv H, Yin W, Mei S. Guaranteed state estimation of power system via interval constraints propagation. IET Gener Transm Distrib. 2013;7(2):138–44.Google Scholar
  41. 41.
    Al-Othman A, Irving M. Uncertainty modelling in power system state estimation. IEE Proc Gener Transm Distrib. 2005;152(2):233–9.Google Scholar
  42. 42.
    Wang H, Ruan J, Wang G, Zhou B, Liu Y, Fu X, et al. Deep learning-based interval state estimation of AC smart grids against sparse cyber attacks. IEEE Trans Ind Inf. 2018;14(11):4766–78.Google Scholar
  43. 43.
    Wu Z, Zhan H, Gu W, Zheng S, Li B. Interval state estimation of distribution network with power flow constraint. IEEE Access. 2018;6:40826–35.Google Scholar
  44. 44.
    Pirnia M, Cañizares CA, Bhattacharya K, Vaccaro A. A novel affine arithmetic method to solve optimal power flow problems with uncertainties. IEEE Trans Power Syst. 2014;29(6):2775–83.Google Scholar
  45. 45.
    Ferrero A, Salicone S. The random-fuzzy variables: a new approach to the expression of uncertainty in measurement. IEEE Trans Instrum Meas. 2004;53(5):1370–7.Google Scholar
  46. 46.
    Ferrero A, Salicone S. Fully comprehensive mathematical approach to the expression of uncertainty in measurement. IEEE Trans Instrum Meas. 2006;55(3):706–12.Google Scholar
  47. 47.
    Damavandi MG, Krishnamurthy V, Martí JR. Robust meter placement for state estimation in active distribution systems. IEEE Trans Smart Grid. 2015;6(4):1972–82.Google Scholar
  48. 48.
    Xygkis TC, Korres GN, Manousakis NM. Fisher information-based meter placement in distribution grids via the D-optimal experimental design. IEEE Trans Smart Grid. 2018;9(2):1452–61.Google Scholar
  49. 49.
    Nie Y, Chung C, Xu N. System state estimation considering EV penetration with unknown behavior using quasi-Newton method. IEEE Trans Power Syst. 2016;31(6):4605–15.Google Scholar
  50. 50.
    Cavraro G, Arghandeh R. Power distribution network topology detection with time-series signature verification method. IEEE Trans Power Syst. 2018;33(4):3500–9.Google Scholar
  51. 51.
    Luan W, Peng J, Maras M, Lo J, Harapnuk B. Smart meter data analytics for distribution network connectivity verification. IEEE Trans Smart Grid. 2015;6(4):1964–71.Google Scholar
  52. 52.
    Cavraro G, Kekatos V, Veeramachaneni S. Voltage analytics for power distribution network topology verification. arXiv preprint arXiv:170706671. 2017.Google Scholar
  53. 53.
    Tian Z, Wu W, Zhang B. A mixed integer quadratic programming model for topology identification in distribution network. IEEE Trans Power Syst. 2016;31(1):823–4.Google Scholar
  54. 54.
    Weng Y, Liao Y, Rajagopal R. Distributed energy resources topology identification via graphical modeling. IEEE Trans Power Syst. 2017;32(4):2682–94.Google Scholar
  55. 55.
    Deka D, Backhaus S, Chertkov M. Structure learning in power distribution networks. IEEE Trans Control Netw Syst. 2018;5(3):1061–74.MathSciNetzbMATHGoogle Scholar
  56. 56.
    Yu J, Weng Y, Rajagopal R. PaToPa: a data-driven parameter and topology joint estimation framework in distribution grids. IEEE Trans Power Syst. 2018;33(4):4335–47.Google Scholar
  57. 57.
    Peppanen J, Reno MJ, Broderick RJ, Grijalva S. Distribution system model calibration with big data from AMI and PV inverters. IEEE Trans Smart Grid. 2016;7(5):2497–506.Google Scholar
  58. 58.
    Chen Y, Huang S, Liu F, Wang Z, Sun X. Evaluation of reinforcement learning based false data injection attack to automatic voltage control. IEEE Trans Smart Grid. 2018:1.Google Scholar
  59. 59.
    Isozaki Y, Yoshizawa S, Fujimoto Y, Ishii H, Ono I, Onoda T, et al. Detection of cyber attacks against voltage control in distribution power grids with PVs. IEEE Trans Smart Grid. 2016;7(4):1824–35.  https://doi.org/10.1109/Tsg.2015.2427380.Google Scholar
  60. 60.
    Ankur Majumdar YPA, Bikash C. Pal. Centralized volt–var optimization strategy considering malicious attack on distributed energy resources control. IEEE Trans Smart Grid. 2018;9(1):148–56.  https://doi.org/10.1109/TSTE.2017.2706965.Google Scholar
  61. 61.
    Deng R, Zhuang P, Liang H. False data injection attacks against state estimation in power distribution systems. IEEE Trans Smart Grid. 2018.  https://doi.org/10.1109/TSG.2018.2813280.
  62. 62.
    Bhela S, Kekatos V, Veeramachaneni S. Enhancing observability in distribution grids using smart meter data. IEEE Trans Smart Grid. 2018.  https://doi.org/10.1109/TSG.2017.2699939.
  63. 63.
    Weng Y, Negi R, Faloutsos C, Ilic MD. Robust data-driven state estimation for smart grid. IEEE Trans Smart Grid. 2017;8(4):1956–67.  https://doi.org/10.1109/Tsg.2015.2512925.Google Scholar
  64. 64.
    • Manitsas E, Singh R, Pal BC, Strbac G. Distribution system state estimation using an artificial neural network approach for pseudo measurement modeling. IEEE Trans Power Syst. 2012;27(4):1888–96.  https://doi.org/10.1109/Tpwrs.2012.2187804 The first integrated DSSE paper to apply ANN into the modeling of pseudo measurements. Google Scholar
  65. 65.
    Wu JZ, He Y, Jenkins N. A robust state estimator for medium voltage distribution networks. IEEE Trans Power Syst. 2013;28(2):1008–16.  https://doi.org/10.1109/Tpwrs.2012.2215927.Google Scholar
  66. 66.
    Hayes BP, Gruber JK, Prodanovic M. A closed-loop state estimation tool for MV network monitoring and operation. IEEE Trans Smart Grid. 2015;6(4):2116–25.  https://doi.org/10.1109/Tsg.2014.2378035.Google Scholar
  67. 67.
    Zhao JB, Zhang GX, Dong ZY, La Scala M. Robust forecasting aided power system state estimation considering state correlations. IEEE Trans Smart Grid. 2018;9(4):2658–66.  https://doi.org/10.1109/Tsg.2016.2615473.Google Scholar
  68. 68.
    Bilil H, Gharavi H. MMSE-based analytical estimator for uncertain power system with limited number of measurements. IEEE Trans Power Syst. 2018;33(5):5236–47.  https://doi.org/10.1109/Tpwrs.2018.2801121.Google Scholar
  69. 69.
    Zhang DX, Han XQ, Deng CY. Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J Power Energy Syst. 2018;4(3):362–70.  https://doi.org/10.17775/Cseejpes.2018.00520.Google Scholar
  70. 70.
    Ak R, Fink O, Zio E. Two machine learning approaches for short-term wind speed time-series prediction. IEEE Trans Neural Netw Learn Syst. 2016;27(8):1734–47.  https://doi.org/10.1109/TNNLS.2015.2418739.MathSciNetGoogle Scholar
  71. 71.
    Qu X, Kang X, Zhang C, et al. Short-term prediction of wind power based on deep long short-term memory. 2016 IEEE PES Asia Pacific Power and Energy Conference; Oct. 25–28, 2016; Xi’an, China.Google Scholar
  72. 72.
    Li ZS, Guo QL, Sun HB, Wang JH. Coordinated transmission and distribution AC optimal power flow. IEEE Trans Smart Grid. 2018;9(2):1228–40.  https://doi.org/10.1109/Tsg.2016.2582221.Google Scholar
  73. 73.
    Report on distributed energy resources integration. California: CAISO Jan. 24, 2014.Google Scholar
  74. 74.
    TSO-DSO interaction: an overview of current interaction between transmission and distribution system operators and an assessment of their cooperation in smart grids. ISGAN, Seoul, South Korea, Sep. 2014.Google Scholar
  75. 75.
    • Gomez-Exposito A, Abur A, Jaen AD, Gomez-Quiles C. A multilevel state estimation paradigm for smart grids. P IEEE. 2011;99(6):952–76.  https://doi.org/10.1109/Jproc.2011.2107490 A multilevel framework that facilitates seamless integration of existing state estimators that are designed to function at different levels of modeling hierarchy in order to accomplish large-scale monitoring of interconnected power systems. Google Scholar
  76. 76.
    Edmunds C, Galloway S, Gill S, editors. Distributed electricity markets and distribution locational marginal prices: a review. 2017 52nd International Universities Power Engineering Conference (UPEC); 2017: IEEE.Google Scholar
  77. 77.
    Bai L, Wang J, Wang C, Chen C, Li F. Distribution locational marginal pricing (DLMP) for congestion management and voltage support. IEEE Trans Power Syst. 2018;33(4):4061–73.Google Scholar
  78. 78.
    Renani YK, Ehsan M, Shahidehpour M. Optimal transactive market operations with distribution system operators. IEEE Trans Smart Grid. 2018;9(6):6692–701.Google Scholar
  79. 79.
    Li Z, Guo Q, Sun H, Wang J. A new LMP-sensitivity-based heterogeneous decomposition for transmission and distribution coordinated economic dispatch. IEEE Trans Smart Grid. 2018;9(2):931–41.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringSouthern Methodist UniversityDallasUSA

Personalised recommendations