Distribution system state estimation (DSSE) is one of the key functions used by distribution system operators (DSOs) for management and control of the distribution grids. In general, distribution networks are featured by a very large number of nodes. Thus, performing the state estimation (SE) of the whole network is computationally very demanding. From the system observability point of view, the scarcity of the installed measurement units in the distribution systems is a big barrier to perform the DSSE. Moreover, the unbalanced nature of distribution systems makes estimators based on the positive-sequence model unfit. To tackle these issues, this paper presents a new data-driven three-phase state estimator based on artificial neural network (ANN) and sparse measurements from Phasor Measurement Units (PMUs), together with its observability assessment. Unlike the existing estimators performing week under the non-Gaussian noise, the proposed estimator provides accurate estimates. The proposed state estimation technique is executed tremendously fast (few milliseconds). This feature facilitates the real-time monitoring of the distribution grids since the estimators are coupled with the PMUs. In this context, PMUs play a crucial role as they can provide synchronized phasor measurements with high reporting rate (f PMU>1Hz). To address the problem size challenge with distributed computation, moreover, multiple ANN three-phase estimators are executed in parallel and integrated in a Multiarea state estimation architecture. Simulation results on the benchmark IEEE 123-bus test system support the feasibility of the solution.

Distribution system state estimation (DSSE) is a key monitoring function for system operators to automate, control and operate the active distribution grids. The computational complexity of the DSSE algorithm is a design factor for implementing a real-time monitoring system. As a common criterion, the elapsed time has been used in many works to determine if a developed DSSE solver, regardless of data acquisition and bad data detection steps, can satisfy the requirement of real-time operation. This measure, however, is machine-dependent and thus may not be a good indicator to show the computational burden of a state estimation method in run-time. This article proposes instead to use floating point operation (FLOP) which is a machine-independent computational complexity measure. The number of FLOPs is a suitable relative performance indicator in determining how fast a method could be executed. In this paper, the computational cost of different DSSE algorithms is mathematically derived in terms of FLOPs. On this basis, this article shows that FLOP is a better measure than the more commonly used elapsed time for making design decisions about the choice of the DSSE method based on computational complexity.

Integration of electric vehicles into electric power system brings both challenges and solutions in the operation of power grids. On the one hand, simultaneously charging a large number of electric vehicles causes branch congestion or large voltage drop. Operating the electric vehicles in the discharging mode, on the other hand, introduces the provision of several ancillary services like peak power shaving and spinning reserves. From the electric vehicles operation point of view, thus, the distribution system operators require a real-time monitoring infrastructure to capture the states of electric vehicle chargers and accordingly operate their grids in the safe mode with respect to the power quality standards (e.g., EN 50160). In this context, the real-time smart charging and storage platform of the EU Horizon 2020 “MEISTER” project, based on the information and communication technology, manages the availability of electric vehicles as a potential source of energy in the need of one or more flexibility services demanded by low voltage distribution system operators. In addition to the implemented information and communication technology platform, this paper presents how the smart use of the electric vehicle resources supports the power quality of the distribution system in terms of system voltage support, bidirectional power flow management, harmonic alleviation and power factor control.

Power flow (PF) is a fundamental tool for operation, automation and optimization of the power systems. Due to the nonlinearity of the PF system equations, the classical PF solutions are computationally very demanding. As a common approach in solving the nonlinear equations, linearization is a potential technique which can simplify and accelerate the PF calculations. In this context, this paper proposes a linear fast iterative method based on the fixed-point iteration technique in which a linearized model of generator along with a ZI load model are integrated in a simplified system of linear equations (SLE) of $Yv=i$ . The relaxation method is used during the deriving process of generator equivalent current in this approach. However, the already developed ZI load model based on the curve-fitting technique has been exploited in this work. The accuracy of the proposed PF method has been compared with calculated results from DIgSILENT PowerFactory on the benchmark IEEE 33-bus test system and on a large medium voltage network in Germany.