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1. 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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2. Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks

   https://doi.org/10.1109/PMAPS53380.2022.9810638  

   Mohammadian, Mostafa ; Baker, Kyri ; Dinh, My H. ; Fioretto, Ferdinando ( January 2022 , International Conference on Probabilistic Methods Applied to Power Systems (PMAPS))

   Learning mappings between system loading and optimal dispatch solutions has been a recent topic of interest in the power systems and machine learning communities. However, previous works have ignored practical power system constraints such as generator ramp limits and other intertemporal requirements. Additionally, optimal power flow runs are not performed independently of previous timesteps - in most cases, an OPF solution representing the current state of the system is heavily related to the OPF solution from previous timesteps. In this paper, we train a recurrent neural network, which embeds natural relationships between timesteps, to predict the optimal solution of convex power systems optimization problems with intertemporal constraints. In contrast to traditional forecasting methods, the computational benefits from this technique can allow operators to rapidly simulate forecasts of system operation and corresponding optimal solutions to provide a more comprehensive view of future system states. 

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3. Security-Constrained Optimal Operation of Energy-Water Nexus based on a Fast Contingency Filtering Method

   Goodarzi, Mostafa ; Li, Qifeng ( January 2022 , IEEE Power Energy Society General Meeting)

   Water and power systems are increasingly interdependent due to the growing number of electricity-driven water facilities. The security of one system can be affected by a contingency in the other system. This paper investigates a security-constrained operation problem of the energy-water nexus (EWN), which is a computationally challenging optimization problem due to the nonlinearity, nonconvexity, and size. We propose a two-step iterative contingency filtering method based on the feasibility and rating of the contingencies to decrease the size of the problem. The optimal power and water flow are obtained in a normal situation by considering the set of contingencies that can not be controlled with corrective actions. The feasibility check of the contingencies is performed in the second step, followed by a rating of the uncontrollable contingencies. Finally, the critical contingencies are obtained and added to the first step for the next iteration. We also employ convex technologies to reduce the computation burden. The proposed method is validated via two case studies. Results indicate that this approach can efficiently attain optimal values. 

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4. Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics

   https://doi.org/10.1109/SmartGridComm47815.2020.9302975  

   Li, Jiaming ; Yue, Meng ; Zhao, Yue ; Lin, Guang ( November 2020 , IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   null (Ed.)

   Transient analysis is vital to the planning and operation of electric power systems. Traditional transient analysis utilizes numerical methods to solve the differential-algebraic equations (DAEs) to compute the trajectories of quantities in the grid. For this, various numerical integration methods have been developed and used for decades. On the other hand, solving the DAEs for a relatively large system such as power grids is computationally intensive and is particularly challenging to perform online. In this paper, a novel machine learning (ML) based approach is proposed and developed to predict post-contingency trajectories of a generator in the time domain. The training data are generated by using an off-line simulation platform considering random disturbance occurrences and clearing times. As a proof-of-concept study, the proposed ML-based approach is applied to a single generator. A Long Short Term Memory (LSTM) network representation of the selected generator is successfully trained to capture the dependencies of its dynamics across a sufficiently long time span. In the online assessment stage, the LSTM network predicts the entire post-contingency transient trajectories given initial conditions of the power system triggered by system changes due to fault scenarios. Numerical experiments in the New York/New England 16-machine 86-bus power system show that the trained LSTM network accurately predicts the generator’s transient trajectories. Compared to existing numerical integration methods, the post-disturbance trajectories of generator’s dynamic states are computed much faster using the trained predictor, offering great promises for significantly accelerating both offline and online transient studies. 

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5. Mitigation of Saturated Cut-sets During Multiple Outages to Enhance Power System Security

   https://doi.org/10.1109/TPWRS.2021.3076973  

   Sen Biswas, Reetam ; Pal, Anamitra ; Werho, Trevor ; Vittal, Vijay ( January 2021 , IEEE Transactions on Power Systems)

   null (Ed.)

   Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component employs the only results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. 

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