As the global push for decarbonization of the world electric power generation systems continues, the deployment of ever larger wind power plants, as well as photovoltaics, and battery energy storage systems abound worldwide. Roughly a decade ago the Western Electricity Coordinating Council's (WECC) Renewable Energy Modeling Task Force successfully achieved the goal of developing a set of publicly available, non-proprietary and generalized dynamic models for computer simulation modeling of such renewable energy systems (RES) in bulk power system planning studies. These models have, and continue to be used by many in both North America and elsewhere. In the past several years, the WECC modeling and validation subcommittee (MVS) has continued these efforts to develop, and with the help of many commercial software vendors, release a set of new modules that help to augment these so-called generic RES dynamic models and increase the range of their applicability. These include features such as a model for simulating inertia-based fast frequency response of wind turbine generators, emulating the so-called weak grid option controls, and updated voltage-source type converter models. This article presents an account of these latest developments.
In the real world, model gaps always exist because models cannot perfectly match the objective physical plants. Model gaps reflect the integrity of models, which is vital for model validation and calibration. This paper proposes a novel approach for model gap quantification and evaluation. First, a comprehensive metric is developed to quantify dynamic model gaps. Next, the model gaps are evaluated from a panoramic view of the probability distribution of the comprehensive metric for multiple scenarios using the stochastic distribution control theory originated by the fourth author. Finally, we demonstrate the proposed quantification and evaluation approach through synchronous machine models.
GE Research has developed a low-cost inertial MEMS process flow to support navigation-grade inertial sensor fabrication called ‘Polaris’ process. With a total of six mask layers, GE Polaris features thick silicon on insulator (SOl) with a 20 to 200 μm device layer, 30:1 high aspect ratio etching, and wafer level vacuum sealing at mTorr with through silicon via technology. The GE multiple-ring gyroscope (MRG) fabricated by the Polaris process has demonstrated navigation-grade performance with proven extreme-temperature reliability and successful integration to a MEMS IMU prototype. GE, through its GE Microfab is now offering Polaris as a foundry process, open to the PNT community.
The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57×10−7.
