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1. Security of a Power System Under High Penetration of Wind Energy Considering Contingencies and Stability Margins

   https://doi.org/10.1109/SCOReD53546.2021.9652753  

   Dorile, Pierre O.; Jagessar, Daniel R.; McCann, Roy A. (November 2021, IEEE 19th Student Conference on Research and Development (SCOReD))

   Security is a well-known function to any transmission operator and system planner. As the world is moving toward the decarbonization of the power industry, it is more complicated for the system operators to maintain an acceptable level of security in the power system operation. More large-scale wind farms are being incorporated into the grid, and thus, the voltage stability concern is increasing. In practice, several contingencies are imagined by the system operators to assess the reliability of the grid. Since voltage stability is one of the major menaces that can trigger voltage instability in a power system, this paper is attempting to present to the transmission system planners and operators a dedicated methodology to facilitate the incorporation of large-scale wind farms into a transmission grid under high penetration of wind power. the stability of a wind-dominated power system is discussed based on Q-V and P-V methodologies and some N-1 contingencies with the Remedial Action Schemes (RAS). Furthermore, a methodology to rank the worst contingencies and to predict the voltage collapse during the highest wind penetration level is presented. Simulations have been, extensively, carried out to examine the methodology and have provided valuable information about the static security of the wind-dominated power system. The results can be used by the transmission system operator to anticipate voltage instability or voltage collapse in the power system during high wind penetration levels. 

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2. Coordinated pitch and torque control of wind farms for power tracking

   https://doi.org/10.23919/ACC.2018.8431325  

   Shapiro, Carl R.; Meyers, Johan; Meneveau, Charles; Gayme, Dennice F. (June 2018, Proceeding of the American Control Conference)

   Improved integration of wind farms into frequency regulation services is vital for increasing renewable energy production while ensuring power system stability. This work generalizes a recently proposed model-based receding horizon wind farm controller for secondary frequency regulation to arbitrary wind farm layouts and augments the controller to enable power modulation through storage of kinetic energy in the rotor. The new design explicitly includes actuation of blade pitch and generator torque, which facilitates implementation in existing farms as it takes advantage of current wind turbine control loops. This generalized control design improves control authority by individually controlling each turbine and using kinetic energy stored in the rotor in a coordinated manner to achieve farm level control goals. Numerical results demonstrate the effectiveness of this approach; in particular, the controller achieves accurate power tracking and reduces loss of revenue in the bulk power market by requiring less setpoint reduction (derate) than the power level control range. 

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3. Learning Residual Distributions with Diffusion Models for Probabilistic Wind Power Forecasting

   https://doi.org/10.3390/en18164226  

   Chen, Fuhao; Gao, Linyue (August 2025, Energies)

   Accurate and uncertainty-aware wind power forecasting is essential for reliable and cost-effective power system operations. This paper presents a novel probabilistic forecasting framework based on diffusion probabilistic models. We adopted a two-stage modeling strategy—a deterministic predictor first generates baseline forecasts, and a conditional diffusion model then learns the distribution of residual errors. Such a two-stage decoupling strategy improves learning efficiency and sharpens uncertainty estimation. We employed the elucidated diffusion model (EDM) to enable flexible noise control and enhance calibration, stability, and expressiveness. For the generative backbone, we introduced a time-series-specific diffusion Transformer (TimeDiT) that incorporates modular conditioning to separately fuse numerical weather prediction (NWP) inputs, noise, and temporal features. The proposed method was evaluated using the public database from ten wind farms in the Global Energy Forecasting Competition 2014 (GEFCom2014). We further compared our approach with two popular baseline models, i.e., a distribution parameter regression model and a generative adversarial network (GAN)-based model. Results showed that our method consistently achieves superior performance in both deterministic metrics and probabilistic accuracy, offering better forecast calibration and sharper distributions. 

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4. Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

   https://doi.org/10.1109/TPWRS.2023.3270800  

   Su, Tong; Zhao, Junbo; Chen, Xiao (October 2023, IEEE Transactions on Power Systems)

   This paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence. 

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5. Inverted Transformers for Effective Post‐Calibration in Multisite Wind Power Forecasting

   https://doi.org/10.1002/we.70057  

   Cohen, Joseph; Xu, Tianyi; Jang, Youngchan; Du, Pengwei; Byon, Eunshin; Huan, Xun (October 2025, Wind Energy)

   ABSTRACT Accurate wind power forecasts are essential for energy management and resource allocation. However, because of complex weather dynamics and other nonlinearities, it is exceedingly difficult to forecast wind power on the multisite level for dozens of wind farms at once. This paper proposes a hybridized approach that leverages deep learning to predict future forecast errors from physics‐based numerical weather prediction (NWP) model estimates. Utilizing errors from NWP forecasts allows integration of critical atmospheric and meteorological dynamics into the forecasting model, and we demonstrate the importance of post‐calibration based on the physics versus pure data‐driven wind power prediction. This post‐calibration approach is enabled by the inverted transformer architecture, which efficiently and effectively learns meaningful wind farm variate representations, resulting in accurate spatiotemporal corrections to the forecasts. We also investigate modifying the iTransformer with a new embedding approach, named SpaceEmbed, that explicitly encodes spatial distance information into the network. The proposed approach is validated with a case study using real‐world data and forecasts from the Electric Reliability Council of Texas (ERCOT) in 2015 for 74 wind farms in Texas at different time scales. Using the high sustained limit as the metric for power generation, the iTransformer outperforms other state‐of‐the‐art deep learning forecasting methods, succeeding at the post‐calibration task by reducing NWP forecast error by up to 33% on average. 

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