Active Distribution Networks State Estimation Under Variable Generation and Load Conditions

Abstract

Traditional distribution networks consist of only centralized generation sources and carry electric energy to consumers. However, today, with the widespread use of Distributed Energy Resources (DER), Energy Storage Systems (ESS), and Electric Vehicle Charging Stations (EVCS), these systems have transformed into active structures having bi-directional power flow, that is, Active Distribution Networks (ADNs). This transformation has made distribution networks more complex and increased the need for network monitoring, control, and analysis methods. In this study, an approach is presented that power flow analysis and state estimation algorithms work together to rapidly monitor and analyze ADNs under variable generation and load conditions. This approach is implemented by combining Direct Load Flow (DLF) analysis with Weighted Least Squares (WLS) based state estimation algorithm and using measurement data obtained from Micro-Phasor Measurement Units (micro-PMU). In order to simulate real-time conditions, modified IEEE 33 bus distribution system is used, and time-varying load profiles are created with Monte Carlo Simulation. Renewable Energy Sources (RES), Battery Energy Storage Systems (BESS), and EVCS are integrated into the distribution system to create a realistic ADN structure. The obtained results show that the proposed method performs state estimation with high accuracy, and the average voltage magnitude and angle errors remain at the level of 0.15% and 1.1%, respectively. Also, the average execution time of power flow analysis and state estimation is 0.0012 seconds and 0.5265 seconds, respectively. As a result, the proposed approach is a suitable solution for real-time monitoring and analysis of ADNs, thanks to its high estimation accuracy and fast execution time.