This paper aims to study the measurement and modeling methods for delays in wide-area closed-loop control systems (WACS). The delays are classified into communication delays and operational delays based on the information flow of WACS. For communication delays, an affine evaluation model is proposed based on previous studies, and this model is verified by the measured communication delays of the Guizhou Power Grid in Southwest China. For operational delays, a real-time digital simulator (RTDS) hardware-in-the-loop measurement method is developed, and the measured operational delays are obtained and analyzed. Finally, an estimation method for the closed-loop delay in actual WACS is proposed, in which the uplink and downlink communication delays are modeled as constants and the operational delay is modeled as a normal distribution. Consequently, the closed-loop delay follows a normal distribution with the calculated parameters by combining the communication delays and operational delay. The measurement results and analysis presented in this paper are important for the development and applications of WACS.

With the ever-increasing penetration level of renewable energy generation units and the replacement of traditional synchronous generators, the issues associated with the deteriorated performance of power system frequency regulation and potential grid frequency instability become more significant. In this paper, the inertial control algorithm using Synchronverter is enabled for variable-speed wind turbines. Besides, type-4 permanent magnet synchronous generator - wind turbine generator using Synchronverter inertial control is modeled and the comprehensive analysis and comparison are conducted between frequency-based inertial control and Synchronverter inertial controls. Aiming to address the compromised ability of Synchronverter with desirable inertial response to deal with fast changing wind condition, Synchronverter with multiple virtual rotating masses is proposed in order to improve the active power tracking performance as well as to boost inertial control of a Synchronverter. The performance of inertial controls is verified in modified 10MVA IEEE 14 bus microgrid system. The assessment of the simulation results provides a deep insight into different inertial control methods while demonstrating the applicability of Synchronverter on wind power generation systems.

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.

Scenario reduction (SR) aims to identify a small yet representative scenario set to depict the underlying uncertainty, which is critical to scenario-based stochastic optimization (SBSO) of power systems. Existing SR techniques commonly aim to achieve statistical approximation to the original scenario set. However, SR and SBSO are commonly considered as two distinct and decoupled processes, which cannot guarantee a superior approximation of the original optimality. Instead, this paper incorporates the SBSO problem structure into the SR process and introduces a novel problem-driven scenario reduction (PDSR) framework. Specifically, we project the original scenario set in distribution space onto the mutual decision applicability between scenarios in problem space. Subsequently, the SR process, embedded by a distinctive problem-driven distance metric, is rendered as a mixed-integer linear programming formulation to obtain the representative scenario set while minimizing the optimality gap. Furthermore, ex-ante and ex-post problem-driven evaluation indices are proposed to evaluate the SR performance. Numerical experiments on two two-stage stochastic economic dispatch problems validate the effectiveness of PDSR, and demonstrate that PDSR significantly outperforms existing SR methods by identifying salient (e.g., worst-case) scenarios, and achieving an optimality gap of less than 0.1% within acceptable computation time.

LiFEPO4 batteries are widely used in electric vehicles and energy storage systems due to their long cycle life and high safety performance. However, the open-circuit voltage-state of charge curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for errors in OSC. First, we establish a battery equivalent circuit model and introduce a parameter identification algorithm based on adaptive recursive least-squares. Next, we derive the relationship between the innovation’s cross correlation matrix/autocorrelation matrix of the Kalman filter and the OSC error. We then develop an adaptive multimodel Kalman filter, which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with a root mean square error of less than 0.03, and also offers significant advantages in computational efficiency, interpretability, and generalizability.

Data-dependent channel cross-modulation (XM) in multiband radio-over-fiber (RoF) systems have been investigated and illustrated for the first time. Nonlinear XM behaviors induced by a Mach-Zehnder modulator (MZM), including the intra-band XM between in-phase (I) and quadrature (Q) components of vector signals, as well as the inter-band XM between multiple bands of scalar/vector signals, have been studied by a closed-form theoretical analysis, and validated by experimental measurements. Consistent results have been obtained by both theoretical and experimental investigations. It was demonstrated that scalar signals only suffer from inter-band XM; whereas vector signals suffer from both intra- and inter-band XMs. Intra-band XM induces constellation compression distortion to vector signals, while inter-band XM induces eye-diagram degradation to scalar signals, and constellation scaling distortion to vector signals. To mitigate the signal distortions induced by data-dependent XM, arcsine pre-distortion technique was implemented to compensate the nonlinear response of MZM. Effective cancellations of both intra- and inter-band XM were achieved.