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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. 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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4. An Efficient Network Solver for Electromagnetic Transient Simulation of Power Systems Based on Hierarchical Inverse Computation and Modification

   https://doi.org/10.1109/NAPS52732.2021.9654532  

   Zhang, Lu ; Wang, Bin ; Sarin, Vivek ; Shi, Weiping ; Kumar, P. R. ; Xie, Le ( November 2021 , 2021 North American Power Symposium (NAPS))

   In both power system transient stability and electromagnetic transient (EMT) simulations, up to 90% of the computational time is devoted to solve the network equations, i.e., a set of linear equations. Traditional approaches are based on sparse LU factorization, which is inherently sequential. In this paper, EMT simulation is considered and an inverse-based network solution is proposed by a hierarchical method for computing and store the approximate inverse of the conductance matrix. The proposed method can also efficiently update the inverse by modifying only local sub-matrices to reflect changes in the network, e.g., loss of a line. Experiments on a series of simplified 179-bus Western Interconnection demonstrate the advantages of the proposed methods. 

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5. Spatial modeling sensitivity analysis: Copula selection for wind speed dependence

   https://doi.org/10.1063/5.0185710  

   Shahirinia, Amir ; Farahmandfar, Zeinab ; Bina, M. Tavakoli ; Henderson, Saul B. ; Ashtary, Mohammad ( April 2024 , AIP Advances)

   As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs.

    

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