# A Review on Distribution System State Estimation Algorithms
###### Abstract  
The modern energy requirements and the orientation towards Renewable Energy Sources (RES) integration promote the transition of distribution grids from passive, unidirectional, fossil fuel-based into active, bidirectional, environmental-friendly architectures. For this purpose, advanced control algorithms and optimization processes are implemented, the performance of which relies on the Distribution System State Estimation (DSSE). DSSE algorithms provide the Distribution System Operator (DSO) with detailed information regarding the network's state in order to derive the optimal decisions. However, this task is quite complex as the distribution system has inherent unbalance issues, often faces lack of adequate measurements, etc. The purpose of this paper is to review the DSSE algorithms that a system can incorporate with emphasis on their particular requirements, the mathematical formulation of the problem, the analysis of the existing model-based and data-driven approaches and the recommended solutions regarding observability issues, bad data detection, and meter placement strategies. Furthermore, special attention is paid to DSSE applications, including the use cases where they can be deployed, the approaches that are usually followed, the integrated distributed power supply units, as well as their future trends and challenges, thus highlighting their business-related aspects. distribution system state estimation (DSSE); smart grid; unbalanced grid; weighted least square (WLS) method; data-driven approaches; observability; pseudo-measurements; bad data; meter placement; applications +
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Overall, DSSE has evolved into a research field that has gathered the interest of many experts. More specifically, accurate and efficient DSSE is considered to be a complex task, not only due to the inherent unbalances of MV and LV grids, but also due to the lack of measurements, which may cause observability issues, as well as the possible existence of bad data (either caused by damaged/inaccurate metering instruments or by cyber-attacks) that need to be identified and excluded from the estimation, as presented in Figure 2[8]. The aforementioned challenges may be approached in a variety of ways, usually through model-based or through more recently developed, data-driven strategies, providing a pool of multifarious solutions regarding DSSE implementation [9]. Figure 1: The value of DSSE for the DSO. Figure 2: Basic concept of DSSE implementation. In literature, the expertise and trends related to some of the main aspects of DSSE algorithms are analyzed. For example, the authors of [10] review the requirements of state estimation and the differences between state estimation in transmission and distribution systems. Furthermore, in [11], the authors focus on model-based and forecast aided DSSE algorithms, while in [12] the authors review DSSE algorithms with special attention to data-driven approaches. Regarding the particular requirements of DSSE, in [13], DSSE algorithms are reviewed with a focus on observability issues and the proposed solutions, and in [14], DSSE algorithms and False Data Injection (FDI) attacks are analyzed. Further, from another view point, the authors of [15] focus on pilot applications of DSSE. The purpose of this paper is to present a comprehensive review and guidelines regarding the existing DSSE algorithms, including the recommended solutions for observability, bad data detection, and meter placement issues, as well as to highlight their applications with an emphasis on the technical and business-related aspects. Therefore, it provides a more holistic approach, including the mathematical/methodological but also the practical aspects of DSSE, thus expanding the purpose of [10; 11; 12; 13; 14; 15].