Uncertainty Modeling of Distributed Energy Resources: Techniques and Challenges
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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.
Jiayi H, Chuanwen J, Rong X. A review on distributed energy resources and MicroGrid. Renew Sust Energ Rev. 2008;12(9):2472–83.
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 .
Distributed energy resources technical considerations for the bulk power system. 2018(Docket No. AD18–10-000).
El-Khattam W, Salama MM. Distributed generation technologies, definitions and benefits. Electr Power Syst Res. 2004;71(2):119–28.
•• Primadianto A, Lu C-NJIToPS. A review on distribution system state estimation. 2017;32(5):3875–83. A comprehensive review of DSSE.
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 .
Akorede MF, Hizam H, Pouresmaeil E. Distributed energy resources and benefits to the environment. Renew Sust Energ Rev. 2010;14(2):724–34.
Halu A, Scala A, Khiyami A, González MC. Data-driven modeling of solar-powered urban microgrids. Sci Adv. 2016;2(1):e1500700.
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.
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 .
• 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.
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.
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.
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.
Pinson P, Kariniotakis G. Conditional prediction intervals of wind power generation. IEEE Trans Power Syst. 2010;25(4):1845–56.
Jabr RA. Minimum loss operation of distribution networks with photovoltaic generation. IET Renew Power Gener. 2014;8(1):33–44.
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.
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.
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.
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.
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.
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.
• 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.
Brinkmann B, Negnevitsky M. A probabilistic approach to observability of distribution networks. IEEE Trans Power Syst. 2017;32(2):1169–78.
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.
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.
Weng Y, Negi R, Ilić MD. Probabilistic joint state estimation for operational planning. IEEE Trans Smart Grid. 2017.
Arefi A, Ledwich G, Behi B. An efficient DSE using conditional multivariate complex Gaussian distribution. IEEE Trans Smart Grid. 2015;6(4):2147–56.
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.
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.
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.
• 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.
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.
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.
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.
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.
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.
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.
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.
Al-Othman A, Irving M. Uncertainty modelling in power system state estimation. IEE Proc Gener Transm Distrib. 2005;152(2):233–9.
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.
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.
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.
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.
Ferrero A, Salicone S. Fully comprehensive mathematical approach to the expression of uncertainty in measurement. IEEE Trans Instrum Meas. 2006;55(3):706–12.
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.
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.
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.
Cavraro G, Arghandeh R. Power distribution network topology detection with time-series signature verification method. IEEE Trans Power Syst. 2018;33(4):3500–9.
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.
Cavraro G, Kekatos V, Veeramachaneni S. Voltage analytics for power distribution network topology verification. arXiv preprint arXiv:170706671. 2017.
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.
Weng Y, Liao Y, Rajagopal R. Distributed energy resources topology identification via graphical modeling. IEEE Trans Power Syst. 2017;32(4):2682–94.
Deka D, Backhaus S, Chertkov M. Structure learning in power distribution networks. IEEE Trans Control Netw Syst. 2018;5(3):1061–74.
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.
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.
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.
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 .
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 .
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 .
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 .
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 .
• 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.
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 .
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 .
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 .
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 .
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 .
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 .
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.
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 .
Report on distributed energy resources integration. California: CAISO Jan. 24, 2014.
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.
• 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.
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.
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.
Renani YK, Ehsan M, Shahidehpour M. Optimal transactive market operations with distribution system operators. IEEE Trans Smart Grid. 2018;9(6):6692–701.