Startseite Technik Multi-Stage Optimal Placement of Branch PMU in Active Distribution Network
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Multi-Stage Optimal Placement of Branch PMU in Active Distribution Network

  • Wang Shu , Kong Xiangrui , Yan Zheng , Xu Xiaoyuan EMAIL logo und Wang Han
Veröffentlicht/Copyright: 17. Juli 2018

Abstract

The penetration of distributed generation and electric vehicles requires advanced monitoring and control strategies to maintain the reliable operation of active distribution network (ADN). Phasor measurement unit (PMU), as an advanced measuring device, has been applied in the operation of transmission systems for decades. Recently, it is anticipated that PMUs can be adopted in the distribution network. In this paper, the optimal branch PMU (BPMU) placement is studied. First, an optimization model for the multi-stage BPMU placement is established considering the observability of ADN. Moreover, the weights of buses are designed to consider the influence of uncertain renewable energy generation and loads. Then, probabilistic load flow (PLF) is used to solve power flow with uncertainties, and weights of buses are obtained based on probability distributions of voltage magnitude. Finally, binary integer programming (BIP) is adopted to obtain the locations of BPMUs. The proposed method is tested on customized IEEE 33-bus and PG&E 69-bus distribution network, and the results are compared with those considering other methods.

Funding statement: This work was supported by the National Key R & D Program of China; [2017YFB0902800].

References

[1] Barker PP, De mello R W. Determining the impact of distributed generation on power systems. I. Radial distribution systems. Power Engr Soc Summer Met IEEE Xplore. 2000;3:1645–56.10.1109/PESS.2000.868775Suche in Google Scholar

[2] Phadke AG, Thorp JS. History and applications of phasor measurements. IEEE Pes Power Systems Conference and Exposition. IEEE. 2006;331–3510.1109/PSCE.2006.296328Suche in Google Scholar

[3] Meliopoulos A P S, Cokkinides G J, Galvan F. GPS-synchronized data acquisition: technology assessment and research issues. Hawaii International Conference on System Sciences. IEEE Comput Soc. 2006;10:244.10.1109/HICSS.2006.199Suche in Google Scholar

[4] Chen X, Chen T, , Tseng K J, Yun S, Amaratunga G. Hybrid approach based on global search algorithm for optimal placement of μPMU in distribution networks. Innovative Smart Grid Technologies - Asia. IEEE. 2016;559–6310.1109/ISGT-Asia.2016.7796445Suche in Google Scholar

[5] Sexauer J, Javanbakht P, , Mohagheghi S. Phasor measurement units for the distribution grid: necessity and benefits. Innovative Smart Grid Technologies. IEEE. 2013;1–610.1109/ISGT.2013.6497828Suche in Google Scholar

[6] Galvan F. The eastern interconnect phasor project - modernizing north america’s electric grid. Transmission and Distribution Conference and Exhibition. 2005/2006 IEEE PES. 2006;1343–4510.1109/TDC.2006.1668707Suche in Google Scholar

[7] Emami R, Abur A. Robust measurement design by placing synchronized phasor measurements on network branches. IEEE Trans Power Syst. 2010;25:38–43.10.1109/TPWRS.2009.2036474Suche in Google Scholar

[8] Sodhi R, Srivastava SC. Optimal PMU placement to ensure observability of power system. Fifteenth National Power Systems Conference. 2008;1–6.10.1109/PES.2009.5275618Suche in Google Scholar

[9] Manousakis NM, Korres GN, , Georgilakis PS. Taxonomy of PMU placement methodologies. IEEE Trans Power Syst. 2012;27:1070–77.10.1109/TPWRS.2011.2179816Suche in Google Scholar

[10] Kumar VSS, Thukaram D. Approach for multistage placement of phasor measurement units based on stability criteria. IEEE Trans Power Syst. 2016;31:2714–25.10.1109/TPWRS.2015.2475164Suche in Google Scholar

[11] Aminifar F, Khodaei A, Fotuhi-Firuzabad M, et al. Contingency-Constrained PMU Placement in Power Networks. IEEE Trans Power Syst. 2010;25:516–23.10.1109/TPWRS.2009.2036470Suche in Google Scholar

[12] Chen X, Chen T, Tseng K J, Sun Y, Amaratunga G. Customized optimal μPMU placement method for distribution networks. Power and Energy Engineering Conference. IEEE. 2016;135–40Suche in Google Scholar

[13] Baldwin T L, Mili L, Boisen M B J, Adapa R. Power system observability with minimal phasor measurement placement. IEEE Trans Power Syst. 1993;8:707–15.10.1109/59.260810Suche in Google Scholar

[14] Milošević B, Begović M. Nondominated sorting genetic algorithm for optimal phasor measurement placement. IEEE Trans Power Syst. 2003;18:69–75.10.1109/TPWRS.2002.807064Suche in Google Scholar

[15] Dua D, Dambhare S, Gajbhiye R K, Soman SA. Optimal multistage scheduling of PMU placement: an ILP Approach. IEEE Trans Power Deliv. 2008;23:1812–20.10.1109/TPWRD.2008.919046Suche in Google Scholar

[16] Aminifar F, Fotuhi-Firuzabad M, Shahidehpour M, Khodaei A. Probabilistic multistage PMU placement in electric power systems. IEEE Trans Power Deliv. 2011;26:841–49.10.1109/TPWRD.2010.2090907Suche in Google Scholar

[17] Nuqui RF, Phadke AG. Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Trans Power Deliv. 2005;20:2381–88.10.1109/TPWRD.2005.855457Suche in Google Scholar

[18] Mili L, Baldwin T, Adapa R. Phasor measurement placement for voltage stability analysis of power systems. Decision and Control, 1990. Proceedings of the IEEE Conf on. 1990;6:3033–38.10.1109/CDC.1990.203341Suche in Google Scholar

[19] Charytoniuk W, Chen M S, , Kotas p, Van Olinda P. Demand forecasting in power distribution systems using nonparametric probability density estimation. Power Syst IEEE Trans. 1999;14:1200–0610.1109/59.801873Suche in Google Scholar

[20] Abouzahr I, Ramakumar R. An approach to assess the performance of utility-interactive wind electric conversion systems. Energy Conversion IEEE Trans. 1991;6:627–38.10.1109/60.103635Suche in Google Scholar

[21] Abouzahr I, Ramakumar R. Loss of power supply probability of stand-alone photovoltaic systems: a closed form solution approach. Energy Conversion IEEE Trans. 1991;6:1–11.10.1109/60.73783Suche in Google Scholar

[22] Karaki SH, Chedid RB, Ramadan R. Probabilistic performance assessment of autonomous solar-wind energy conversion systems. Energy Conversion IEEE Trans. 1999;14:766–72.10.1109/60.790949Suche in Google Scholar

[23] Jia C, Zheng Y. Probabilistic power flow calculation for AC/DC hybrid system with wind farms. Electric Power Auto Equt. 2016;36:94–101.Suche in Google Scholar

[24] Zhe Z, Gengyin L, Junqiang W. Probabilistic evaluation of voltage quality in distribution networks considering the stochastic characteristic of distributed generators. Proceeding of the CSEE. 2013;33:150–56.Suche in Google Scholar

[25] Jianwei Z, Yupeng L, , Zenghui Y, Xiaoyuan Xu. Probabilistic static voltage stability calculation based on quasi-monte carlo and kernel density estimation. Power Syst Technol. 2016;40:3833–39Suche in Google Scholar

[26] Baran ME, Wu FF. Optimal capacitor placement on radial distribution systems. IEEE Trans Power Deliv. 2002;4:725–34.10.1109/61.19265Suche in Google Scholar

Received: 2018-03-07
Revised: 2018-05-25
Accepted: 2018-06-12
Published Online: 2018-07-17

© 2018 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 2.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2018-0095/html?lang=de
Button zum nach oben scrollen