Fei Ni

Dimension-Adaptive Sparse Grid Interpolation for Uncertainty Quantification in Modern Power Systems: Probabilistic Power Flow

In this paper, the authors firstly present the theoretical foundation of a state-of-the-art uncertainty quantification method, the dimension-adaptive sparse grid interpolation (DASGI), for introducing it into the applications of probabilistic power flow (PPF), specifically as discussed herein. It is well-known that numerous sources of uncertainty are being brought into the present-day electrical grid, by large-scale integration of renewable, thus volatile, generation, e.g., wind power, and by unprecedented load behaviors. In presence of these added uncertainties, it is imperative to change traditional deterministic power flow (DPF) calculation to take them into account in the routine operation and planning. However, the PPF analysis is still quite challenging due to two features of the uncertainty in modern power systems: high dimensionality and presence of stochastic interdependence. Both are traditionally addressed by the Monte Carlo simulation (MCS) at the cost of cumbersome computation; in this paper instead, they are tackled with the joint application of the DASGI and Copula theory (especially advantageous for constructing nonlinear dependence among various uncertainty sources), in order to accomplish the dependent high-dimensional PPF analysis in an accurate and faster manner. Based on the theory of DASGI, its combination with Copula and the DPF for the PPF is also introduced systematically in this work. Finally, the feasibility and the effectiveness of this methodology are validated by the test results of two standard IEEE test cases.

Fundamental and harmonic synchrophasors estimation using modified Taylor-Kaiman filter

This paper investigates the possibility of measuring fundamental and harmonic synchrophasors as well as the signal frequency using a modified Taylor Kalman filter (TKF). In a previous paper, a modified TKF was proposed to track the complex trajectory of dynamic phasors of the fundamental frequency component based on an improved dynamic model under non-steady state conditions. In this paper, we extend the model of the modified TKF to include harmonics and to allow a better estimation of the phasor in presence of distorted signals. The new method also allows to estimate frequency and time varying harmonic components.

Basis-Adaptive Sparse Polynomial Chaos Expansion for Probabilistic Power Flow

This paper introduces the basis-adaptive sparse polynomial chaos (BASPC) expansion to perform the probabilistic power flow (PPF) analysis in power systems. The proposed method takes advantage of three state-of-the-art uncertainty quantification methodologies reasonably: the hyperbolic scheme to truncate the infinite polynomial chaos (PC) series; the least angle regression (LARS) technique to select the optimal degree of each univariate PC series; and the Copula to deal with nonlinear correlations among random input variables. Consequently, the proposed method brings appealing features to PPF, including the ability to handle the large-scale uncertainty sources; to tackle the nonlinear correlation among the random inputs; to analytically calculate representative statistics of the desired outputs; and to dramatically alleviate the computational burden as of traditional methods. The accuracy and efficiency of the proposed method are verified through either quantitative indicators or graphical results of PPF on both the IEEE European Low Voltage Test Feeder and the IEEE 123 Node Test Feeder, in the presence of more than 100 correlated uncertain input variables.

A modified Taylor-Kalman filter for instantaneous dynamic phasor estimation

This paper proposes a modified Taylor-Kalman filter (TKF) for instantaneous phasor estimation based on an improved dynamic model describing the complex trajectory of dynamic phasors. The improved dynamic model is obtained thanks to a revised state transition equation of the rotating phasor and its derivatives. The proposed approach is assessed with dynamic compliance tests defined in the synchrophasor standard and in a power system test case. As the results show, the modified TKF can track the time-varying behavior of dynamic phasors under non-steady state conditions in power systems. It achieves good estimation performance without manually adjusting the Kalman gain as in the existing solution. Thus, the modified TKF fully exploits the self-adaptive nature of the Kalman filter principle.

Uncertainty analysis of aggregated smart meter data for state estimation

In distribution networks, the data redundancy is usually assumed to be an inevitable bottleneck of traditional grid control and operation. Recently, the availability of smart meter data in distribution systems has provided an opportunity to improve the observability. As for the medium-voltage (MV) distribution system, there is an increasing interest to use the spatially aggregated smart meter data from low-voltage (LV) feeders in the state estimation, instead of inaccurate pseudo-measurements. However, good performance of state estimators requires good knowledge of the available measurements, in terms of both the mean value and the associated uncertainty. Hence, this paper intends to pave a new way of utilizing and aggregating smart meter data for the purpose of state estimation in the MV distribution system, in a concrete and reliable manner. The feasibility of the proposed method is verified on the IEEE European Low Voltage Test Feeder with a set of real-world smart meter data. Simulation results show that the utilization of aggregated smart meter data is able to improve the accuracy of load modelling of three-phase transformers.

Measurement of the harmonic impedance of the aggregated distribution network

This paper presents a method for measurement of the harmonic impedance of an aggregated distribution network using multiple Phasor Measurement Units (PMUs) with the additional capability of synchronized voltage and current spectrum measurement. The perturbations of the harmonic voltages which originate from the higher voltage levels are distinguished based on the measurements from multiple busbars, and used as the input for the calculation. Results of a test case using measurements from a 50 kV network are given to illustrate the method.

Integration of a large-scale research facility into the grid: Case study of a real project

Pursuing 2020 and 2050 energy and emission goals also the large-scale research facilities must reduce energy consumption and contribution to greenhouse gas (GHG) emission, as well as behaving like good citizens of the smart grid. These new requirements are currently being integrated in the design process of these facilities. Hence, extensive verifications and tests must be carried out before a facility with very heavy power demand is deployed into the grid. In particular, how it impacts the regional grid and how the regional grid impacts the facility. In this paper, we present the simulation results of these mutual impacts, based on the real world project of a spallation source. As a meaningful contribution, the uncertainty quantification is applied to handle with stochastic nature of renewable energy in this project, by RTDS simulation and Monte Carlo simulation together.

Application of non-intrusive polynomial chaos expansion in probabilistic power flow with truncated random variables

In this paper, the authors apply a surrogate model-based method for probabilistic power flow (PPF) in the power system subject to truncated random variables. Due to a growing number of uncertainty sources are being brought into the modern power system, the traditional deterministic power flow analysis lacks its ability to recognize the realistic states of power systems, and thus turns to PPF for help. However, the PPF analysis is still facing several challenges: the computational effort required by the traditional simulation method is prohibitively expensive; and the modeling of uncertainty sources needs the improvement on both distribution type selection and parameter evaluation. The novelty of this work lies in taking advantage of both general polynomial chaos (gPC) expansion and ordinary least squares (OLS) to deal with PPF in presence of the truncated random variables. The performance of the proposed method is verified on the IEEE 30-Bus test system, considering uncertain factors brought by active power at load buses. In different test scenarios, the proposed method shows sound performances at the cost of less computational effort, compared to the traditional approach.