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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. Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks

   https://doi.org/10.1109/PESGM48719.2022.9917161  

   Singh, Manish ; Kekatos, Vassilis ; Giannakis, Georgios ( July 2022 , IEEE Power & Energy Society General Meeting)

   o shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN) to predict the solutions of the AC optimal power flow (AC-OPF) once presented load demands. As network topologies may change, training this DNN in a sample-efficient manner becomes a necessity. To improve data efficiency, this work utilizes the fact OPF data are not simple training labels, but constitute the solutions of a parametric optimization problem. We thus advocate training a sensitivity-informed DNN (SI-DNN) to match not only the OPF optimizers, but also their partial derivatives with respect to the OPF parameters (loads). It is shown that the required Jacobian matrices do exist under mild conditions, and can be readily computed from the related primal/dual solutions. The proposed SI-DNN is compatible with a broad range of OPF solvers, including a non-convex quadratically constrained quadratic program (QCQP), its semidefinite program (SDP) relaxation, and MATPOWER; while SI-DNN can be seamlessly integrated in other learning-to-OPF schemes. Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups. 

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4. Risk Assessment and Mitigation of Cascading Failures Using Critical Line Sensitivities

   https://doi.org/10.1109/TPWRS.2023.3305093  

   Dai, Yitian ; Noebels, Matthias ; Preece, Robin ; Panteli, Mathaios ; Dobson, Ian ( March 2024 , IEEE Transactions on Power Systems)

   Security concerns have been raised about cascading failure risks in evolving power grids. This paper reveals, for the first time, that the risk of cascading failures can be increased at low network demand levels when considering security-constrained generation dispatch. This occurs because critical transmission cor- ridors become very highly loaded due to the presence of central- ized generation dispatch, e.g., large thermal plants far from de- mand centers. This increased cascading risk is revealed in this work by incorporating security-constrained generation dispatch into the risk assessment and mitigation of cascading failures. A se- curity-constrained AC optimal power flow, which considers eco- nomic functions and security constraints (e.g., network con- straints, 𝑵 − 𝟏 security, and generation margin), is used to pro- vide a representative day-ahead operational plan. Cascading fail- ures are simulated using two simulators, a quasi-steady state DC power flow model, and a dynamic model incorporating all fre- quency-related dynamics, to allow for result comparison and ver- ification. The risk assessment procedure is illustrated using syn- thetic networks of 200 and 2,000 buses. Further, a novel preventive mitigation measure is proposed to first identify critical lines, whose failures are likely to trigger cascading failures, and then to limit power flow through these critical lines during dispatch. Results show that shifting power equivalent to 1% of total demand from critical lines to other lines can reduce cascading risk by up to 80%. 

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5. Grid-aware aggregation and realtime disaggregation of distributed energy resources in radial networks

   https://doi.org/10.1109/TPWRS.2021.3121215  

   Nazir, Nawaf ; Almassalkhi, Mads ( October 2021 , IEEE Transactions on Power Systems)

   Dispatching a large fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are located within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that characterizes a feeders aggregate DERs hosting capacity and enables a realtime, grid-aware dispatch of DERs for radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to collectively deliver grid services. The IEEE-37 distribution network is used to validate the technical results and highlight various use-cases. 

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