Publications
Publications Journals Giraud, B., Rajaei, A, Cremer, J.L. (2024). Constraint-Driven Deep Learning for N-k Security Constrained Optimal Power Flow. Electric Power System Research and 2024 IEEE Power System Computation Conference. [ https://doi.org/10.1016/j.epsr.2024.110692 ] Covic, N., Cremer, J.L., Pandžić, H. (2024). Learning a Reward Function for Optimal Appliance Scheduling. Electric Power System Research and 2024 IEEE Power System Computation Conference [ https://arxiv.org/pdf/2310.07389.pdf ] Renshaw-Whitman, C., Zobernig, V., Cremer, J.L., Vries, L. (2024). The Non-Stationary for Multiagent Reinforcement Learning in Electricity Markets. Electric Power System Research and 2024 IEEE Power System Computation Conference. [ https://doi.org/10.1016/j.epsr.2024.110712 ] Bugaje, A.-A., Cremer, J.L., Strbac, G. (2023). Generating Quality Datasets for Real-Time Security Assessment: Balancing Historically Relevant and Rare Feasible Operating Conditions. International Journal of Electrical Power & Energy Systems. [ https://doi.org/10.1016/j.ijepes.2023.109427 ] Habib, B., Isufi, E., van Breda, W., Jongepier, A. and Cremer, J.L. (2023). Deep Statistical Solver for Distribution System State Estimation. IEEE Transactions on Power Systems. [ https://doi.org/10.1109/TPWRS.2023.3290358 ] Wahdany, D., Schmitt, C., Cremer, J.L., (2023). More than Accuracy: End-To-End Wind Power Forecasting that Optimises the Energy System. Electric Power System Research. [ https://doi.org/10.1016/j.epsr.2023.109384 ] Bugaje, A.-A., Cremer, J.L. and Strbac, G. (2022). Real-time Transmission Switching with Neural Networks. IET Generation, Transmission & Distribution. [ https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/gtd2.12698 ] Bellizio, F., Cremer, J.L. and Strbac, G. (2022). Transient Stable Corrective Control in Smart Grids Using Neural Lyapunov Learning. IEEE Transactions of Power Systems. [ https://ieeexplore.ieee.org/document/9878088 ] Saeed Sarafraz, M., Proskurnikov, A., Tavazoei, M.S. and P. Mohajerin Esfahani (2022). Robust Output Regulation: Optimization-Based Synthesis and Event-Triggered Implementation. IEEE Transactions on Automatic Control. [ https://ieeexplore.ieee.org/abstract/document/9484809 ] Bellizio, F., Bugaje, A.-A., Cremer, J.L. and Strbac, G. (2022). Verifying Machine Learning Conclusions for Securing Low Inertia Systems. Sustainable Energy, Grids and Networks. [Verifying Machine Learning conclusions for securing Low Inertia systems - ScienceDirect] Segundo Sevilla, F.R., Liu, Y., Barocio, E., Korba, P., Andrade, M., Bellizio, F., Bos, J., Chaudhuri, B., Chavez, H., Cremer, J.L., Eriksson, R., Hamon, C., Herrera, M., Huijsman, M., Ingram, M., Klaar, D., Krishnan, V., Mola, J., Netto, M., Paolone, M., Papadopoulos, D., Ramirez, M., Rueda, J., Sattinger, W., Terzija, V., Tindemans, S., Trigueros, A., Wang, Y. and Zhao, J. (2022). State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data. International Journal of Electrical Power & Energy Systems. [ https://www.sciencedirect.com/science/article/pii/S0142061521009947 ] Van der Ploeg, C., Alirezaei, M., Van De Wouw, N. and Mohajerin Esfahani, P. (2022). Multiple faults estimation in dynamical systems: Tractable design and performance bounds. IEEE Transactions on Automatic Control. [ https://arxiv.org/abs/2011.13730 ] Marot, A., Donnot, B., Chaouache, K., Kelly, A., Huang, Q., Hossain, R.-R., and Cremer, J.L. (2022). Learning to run a power network with trust. arXiv preprint arXiv. [ https://arxiv.org/abs/2110.12908 ] Bellizio, F., Zu, W., Qiu, D., Ye, Y., Papadaskapoulos, D., Cremer, J.L., Teng, F. and Strbac, G. (2022). Transition to secure data-driven grid control and decentralized electricity market. IEEE Proceedings, Special Issue "The Evolution of Smart Grids". [ https://ieeexplore.ieee.org/document/9756414/keywords#keywords ] Marot, A., Kelly, A., Naglic, M., Barbesant, V., Cremer, J.L., Stefanov, A. and Viebahn, J. (2022). Perspectives for Future Power System Control Centers for The Energy Transition. IEEE Journal of Modern Power Systems and Clean Energy. [ https://ieeexplore.ieee.org/document/9744623 ] Kolarijani, A. and Mohajerin Esfahani, P. (2022). Fast Approximate Dynamic Programming for Input-Affine Dynamics. IEEE Transactions on Automatic Control. [ https://arxiv.org/abs/2008.10362 ] Bugaje, A.-A., Cremer, J.L. and Strbac, G. (2022). Split-based Sequential Sampling for Realtime Security Assessment. International Journal of Electrical Power & Energy Systems. [ https://www.sciencedirect.com/science/article/pii/S0142061522007864 ] Pan, K., Palensky, P. and Mohajerin Esfahani, P. (2022). Dynamic Anomaly Detection with High-fidelity Simulators: A Convex Optimization Approach. IEEE Transactions on Smart Grid. [ https://ieeexplore.ieee.org/document/9619468 ] Bellizio, F., Cremer, J.L. and Strbac, G. (2022). Machine-learned security assessment for changing system topologies. International Journal of Electrical Power & Energy Systems. [ https://www.sciencedirect.com/science/article/pii/S0142061521006190 ] Van der Ploeg, C., Silvas, E., Van de Wouw, N. and P. Mohajerin Esfahani (2021). Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems. IEEE Control Systems Letters. [ https://ieeexplore.ieee.org/document/9659807 ] Bellizio, F., Cremer, J.L., Sun, M. and Strbac, G. (2021). A causality-based feature selection approach for data-driven dynamic security assessment. Electric Power Systems Research 201. [ https://www.sciencedirect.com/science/article/pii/S0378779621005186 ] Bugaje, A., Cremer, J.L., Sun, M. and Strbac, G. (2021). Selecting DT Models for Security Assessment using ROC- and Cost-Curves. Energy and AI. [Selecting decision trees for power system security assessment - ScienceDirect] Gravell, B., Mohajerin Esfahani, P., and Summers, T. (2021). Learning Robust Controllers for Linear Quadratic Systems with Multiplicative Noise via Policy Gradient. IEEE Transactions on Automatic Control. [ https://ieeexplore.ieee.org/document/9254115 ] Akhtar, S.A., Kolarijani A.S. and Mohajerin Esfahani, P. (2021). Learning for Control: An Inverse Optimization Approach. IEEE Control Systems Letters. [ https://ieeexplore.ieee.org/document/9483283 ] Nguyen, V., Kuhn, D., and Mohajerin Esfahani, P. (2021). Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator. Operations Research (OR). [ https://pubsonline.informs.org/doi/abs/10.1287/opre.2020.2076 ] Kolarijani, A., Proskurnikov, A. and Mohajerin Esfahani, P. (2021). Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions. IEEE Transactions on Automatic Control. [ https://ieeexplore.ieee.org/abstract/document/9093157 ] Zhang, T., Sun, M., Cremer, J.L., Zhang, N., Strbac, G. and Kang, C. (2021). A Confidence-Aware Machine-Learned Framework for DSA. IEEE Transactions on Power Systems. [ https://ieeexplore.ieee.org/document/9354032 ] Nguyen, V.A., Shafieezadeh-Abadeh, S., D. Kuhn, D. and Mohajerin Esfahani, P. (2021). Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization. Mathematics of Operations Research. [ https://arxiv.org/pdf/1911.03539.pdf ] Parys, B.V., Mohajerin Esfahani, P., and Kuhn, D. (2020). From Data to Decisions: Distribution ally Robust Optimization is Optimal. Management Science. [ https://pubsonline.informs.org/doi/10.1287/mnsc.2020.3678 ] Conferences Money, R., Krishnan, J., Beferull-Lozano, B. and Isufi, E. (2024). Evolution Backcasting of Edge Flows from Partial Observations Using Simplicial Vector Autoregressive Models. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. [ https://ieeexplore.ieee.org/document/10448180 ] Kolarijani, A., Max, G. and Mohajerin Esfahani, P. (2021). Fast Approximate Dynamic Programming for Infinite-Horizon Continuous-State Markov Decision Processes, Neural Information Processing Systems (NeurIPS). [ https://www.dcsc.tudelft.nl/~mohajerin/Publications/conference/2021/NIPS_FDP.pdf ] Vreugdenhil, R., Nguyen, V. A., Eftekhari, A., Mohajerin Esfahani, P. (2021) Principal Component Hierarchy for Sparse Quadratic Programs. International Conference on Machine Learning (ICML), Vienna, Austria. [ https://proceedings.mlr.press/v139/vreugdenhil21a/vreugdenhil21a.pdf ] Dong, J., Sharifi Kolarijani, A. and Mohajerin Esfahani, P. (2021) Multimode Diagnosis for Switched Affine Systems. American Control Conference (ACC), New Orleans, USA. 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