Alexander Prostejovsky

Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances

State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement tolerances, noise, and asynchronous timestamps. A measurement tolerance compensation model and an alternative representation of the power flow equations without voltage phase angles are introduced. The line parameters are obtained using numeric methods. The simulation demonstrates in case of the series conductance that the absolute compensated error is -1.05% and -1.07% for both representations, as opposed to the expected uncompensated error of -79.68%. Identification of a laboratory distribution line using real measurement data grid yields a deviation of 6.75% and 4.00%, respectively, from a calculation based on the manufacturers cable specifications and estimated line length. The transformed power flow equations deliver similar results despite the reduced problem complexity.

Distributed framework for prototyping of observability concepts in Smart Grids

Development and testing of distributed monitoring, visualisation, and decision support concepts for future power systems require appropriate modelling tools that represent both the electrical side of the grid, as well as the communication and logical relations between the acting entities. This work presents an Observability Framework for distributed data acquisition and knowledge inference that aims to facilitate the development of these distributed concepts. They are realised as applications that run within the framework and are able to access the information on the grid topology and states via an abstract information model. Data is acquired dynamically over low-level data interfaces that allow for easy integration within heterogeneous environments. A Multi-Agent System platform was chosen for implementation, where agents represent the different electrical and logical grid elements and perform data acquisition, processing, and exchange. The basic capabilities of the framework together with a simple visualisation concept are demonstrated using a simulation of the Power Networks Demonstration Centre (PNDC) laboratory distribution grid.

Tuningless Load Frequency Control Through Active Engagement of Distributed Resources

The increasing share of volatile and inverter-based energy sources render electric power grids increasingly susceptible to disturbances. Established Load Frequency Controls (LFC) schemes are rigid and require careful tuning, making them unsuitable for dynamically changing environments. In this paper, we present a fast and tuningless frequency control approach that tackles these shortcomings by means of modern grid monitoring and communications infrastructures in a twofold concurrent process. First, direct observation of supply and demand enables fast power balancing decoupled from the total system dynamics. Second, primary resources are actively involved in frequency restoration by systematic adjustment of their frequency reference setpoints. In contrast to the commonly used Automatic Generation Control (AGC), the proposed direct LFC does not require an integrator for frequency control in the closed loop even under partial grid observability. The approach is Lyapunov-stable for a wide range of system parameters, including ramping limits of controlled resources. A performance study against AGC has been conducted on a three-area power system in simulations as well as in a real-laboratory grid with an installed generation capacity of 110 kW.

Enhanced situational awareness and decision support for operators of future distributed power network architectures

This paper describes scenarios proposed for a control room decision support system aimed at future power network operators. The purpose is to consider the requirements of the future control room from the perspective of the operator under the conditions of a significant frequency excursion incident. The control room visualisation and decision support functionality for aiding the operator in restoring the frequency to its target value will be considered. The analysis takes place within the Web-of-Cells framework, adopted to deal with power system control through a web of subsystems, called cells, which are highly automated, and operated by Cell Operators.

Reduction of topological connectivity information in electric power grids

Electric power distribution grids increasingly use higher levels of monitoring and automation, both dependent on grid topology. However, the total amount of information to adequately describe power grids is vast and needs to be reduced when used locally. This work presents an approach for reducing and assembling power grid topologies in a decentralized way, such that full details of the local grid neighborhood are preserved, while remote areas get reduced in detail. Full connectivity information is retained. Practical evaluation is performed on a modified version of the 906-bus IEEE European low-voltage test feeder, augmented with additional lines which introduce loops. The resulting size is 0.067% of the full matrix for a subarea size of 66 buses.

Recursive estimation of n-line parameters for electric power distribution grids

Electrical models of power distribution grids are used in applications such as state estimation and Optimal Power Flow (OPF), the reliability of which depends on the accuracy of the model. This work presents an approach for estimating distribution line parameters from Remote Terminal Unit (RTU) measurements which are subject to measurement device tolerances and random noise. Building upon an earlier work which introduced a measurement tolerance compensation model, we aim to improve a) the robustness towards noisy data and b) the estimate of the parallel susceptance. For this purpose, we employ an Extended Kalman Filter (EKF) whose measurement noise covariance matrix is modified in order to account for all noisy variables in the overdetermined system. Simulations confirm the advantages of the EKF over the previously used Least-Squares (LSQ) estimator. In the low random noise cases considered in this paper, the EKF yields a four-fold improvement over the LSQ for the parallel susceptance across all quantization ranges. For the highest levels of random and quantization noise, the EKF performs about 1.5 to 3 times better than the LSQ for all line parameters. Furthermore, the EKF shows more consistent behavior when applied to data obtained from a laboratory distribution grid, which exhibits uncertainties that are not accounted for in the models.

Agent-based decentralization of applications in distributed smart grid systems

Smart grid technology promises to prepare todays power systems for the challenges of the future by extensive integration of information and communication technology (ICT). One key aspect is the control paradigm which will have to be shifted from completely centralized control systems to more dezentralized concepts in order to adapt to the distributed nature of smart grids. Multi-agent systems (MAS) are a very promising approach for designing distributed, decentralized systems, naturally also in the field of smart grids. This work introduces the notion of decentralized multi-agent-based control systems (DMACS) and aims to give an overview on the different requirements and challenges on the way from current centralized control systems to DMACS. Therefore, different ICT scenarios and MAS topologies are employed to discuss the decentralization of three exemplary smart grid applications: voltage/var control, virtual power plants, and dynamic islanding. As a result, the advantages and challenges as well as ICT requirements of agent-based decentralization are outlined.