Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes Three-phase state estimation in the medium-voltage network with aggregated smart meter data F. Ni c, ⁎ , P.H. Nguyen a , J.F.G. Cobben a , H.E. Van den Brom b , D. Zhao b a Electrical Energy Systems Group, Eindhoven University of Technology, Eindhoven 5612 AZ, The Netherlands b VSL, Dutch National Metrology Institute, Delft 2629 JA, The Netherlands c National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 20092, China ARTICLE INFO Keywords: Smart meter State estimation Distribution system Measurement uncertainty Data aggregation ABSTRACT In distribution networks, the lack of measurement data is usually thought to be an inevitable bottleneck of conventional grid operation and planning. Recently, the availability of smart meters in the distribution network has provided an opportunity to improve the network observability. In medium-voltage (MV) distribution net- works, there is an increasing demand to use aggregated smart meter data for the state estimation, instead of adopting pseudo-measurements with a low level of accuracy. However, the performance of an estimator requires good knowledge of the available measurements, in terms of both expected values and associated uncertainties. Therefore, this paper intends to firstly pave a new way of utilizing smart meter data gathered from the low- voltage (LV) feeders in a concrete and reliable manner. For the purpose of state estimation in MV distribution networks, smart meter data is to be processed through three steps: phase identification, data aggregation and uncertainty evaluation. The feasibility of the proposed method is verified on the IEEE European LV Test Feeder with a set of real-world smart meter data. Afterwards, the influence of the aggregated smart meter data on the three-phase state estimation are investigated on the modified IEEE 13-node test system and IEEE 34-node test system. Simulation results show that the effect of aggregated smart meter data on the accuracy of state esti- mators is dependent on both the accuracy level of the aggregated data and the measurement configuration in the network. Furthermore, the use of aggregated smart meter data is shown to be able to provide improved state estimation. 1. Introduction With the large penetration of distributed energy resources and sto- rage devices to distribution networks, the power flow pattern is be- coming more complicated. Hence, distribution system state estimation (DSSE), which is able to provide the recent state information to the control center, plays an important role in the grid planning and op- eration. The concept of state estimation in power systems as proposed by Fred Schweppe in 1970 [1], is devoted to inferring the most likely estimate for each system state based on the network model and avail- able real-time measurements from the system. If system states are known, the other quantities of the power grid can be derived. Prior to the state estimation, an observability analysis of the net- work, which determines if the state estimation function can be per- formed with the available measurements, should be carried out [2].A power system is fully observable if a unique solution of the state esti- mation function can be obtained, and vice versa. In general, an increase in the number of measurement data improves the numerical ob- servability of a power system. However, there are insufficient mea- surement data for state estimation in most of the distribution networks for economic and technical reasons. Conventionally, only measure- ments at the substation and critical loads are available to grid operators of the distribution network. Such situation of lacking measurement data hinders development of DSSE though various advanced algorithms have been intensively proposed in the last decades [3,4]. In order to execute the state estimation function on the numerically unobservable parts of a power system, pseudo-measurements are adopted to augment the available measurements. Pseudo-measure- ments, which are much less accurate than the real-time measurements, are typically obtained from historical data, generation dispatch or short-term load forecasting [5]. Although pseudo-measurements make the system artificially observable, they offer a relatively poor knowl- edge of the measurand, thus they have high variances in the DSSE. It was found that the accuracy of measurements notably affects https://doi.org/10.1016/j.ijepes.2017.12.033 Received 8 February 2017; Received in revised form 23 October 2017; Accepted 25 December 2017 ⁎ Corresponding author. E-mail addresses: fei.ni@tongji.edu.cn (F. Ni), p.nguyen.hong@tue.nl (P.H. Nguyen), j.f.g.cobben@tue.nl (J.F.G. Cobben), hvdbrom@vsl.nl (H.E. Van den Brom), Dzhao@vsl.nl (D. Zhao). Electrical Power and Energy Systems 98 (2018) 463–473 0142-0615/ © 2017 Published by Elsevier Ltd. T