Publications

Journal and Preprints

• X. He, D.H. Tsang, and Y. Chen.  "Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach", Accepted at IEEE Transactions on Power Systems, 2024 [link]

• S. Li, H. Xiong, and Y. Chen. "DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model," IEEE Transactions on Smart Grid  Vol. 15, no. 4, 2024  [link] [code] 

• Y. Zhang, H. Wen, T. Feng, and Y. Chen. “Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms,” submitted to Applied Energy, 2024 [link]

• Y. Jiang, Y. Li, and Y. Chen. "Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition", Electric Power Systems Research, 2024 [link]

• S. Li, H. Xiong, and Y. Chen. "DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load", Electric Power Systems Research, 2024 [link] [code]

• C. Huang, S. Li, R. Liu, H. Wang, and Y. Chen. "Large Foundation Models for Power Systems", in submission, 2024 [preprint] [code]

• R. Liu, Y. Pan, and Y. Chen. "Laxity-Aware Scalable Reinforcement Learning for HVAC Control," arXiv preprint arXiv:2306.16619 [link]

• Y. Chen, L. Zhang, and B. Zhang. "Learning to solve DCOPF: A duality approach." Electric Power Systems Research 213 (2022): 108595 [link][code]

• L. Zhang, Y. Chen, and B. Zhang, “A Convex Neural Network Solver for DCOPF with Generalization Guarantees,” IEEE Transactions on Control of Networked Systems, 2021 [link]

• A. Pan, Y. Lee, H. Zhang, Y. Chen, and Y. Shi. "Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training." arXiv preprint arXiv:2110.08956 (2021) [link]

• Y. Chen, D. Arnold, Y. Shi, and S. Peisert. "Understanding the Safety Requirements for Learning-based Power Systems Operations." arXiv preprint arXiv:2110.04983 (2021) [link]

• Y. Chen, Y. Shi, and B. Zhang, “Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks,” Electric Power Systems Research, vol. 189, 2020 [link]

• Y. Chen, and H. Wang. "IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning." IEEE Transactions on Emerging Topics in Computational Intelligence (2020) [link]

• J. E. Contreras-Ocana, Y. Chen, U. Siddiqi, and B. Zhang, “Non-Wire Alternatives: an Additional Value Stream for Distributed Energy Resources,” IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1287–1299, 2020 [link]

• Y. Chen, W. Yang, B. Zhang, “Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic”, arXiv preprint arXiv:2006.08826 (2020) [link][code]

• X-W. Wang, Y. Chen, and Y-Y. Liu. "Link Prediction through Deep Generative Model." iScience 23, no. 10 (2020): 101626 [link]

• Y. Chen, M. Tulio-Angulo, and Y-Y. Liu. "Revealing Complex Ecological Dynamics via Symbolic Regression." BioEssays 41, no. 12 (2019): 1900069 [link] [Cover]

• Y. Chen, Y. Wang, D. Kirschen, and B. Zhang, “Model-Free Renewables Scenario Generation Using Generative Adversarial Networks,” IEEE Transaction on Power Systems, vol. 33, no. 3, pp. 3265–3275, 2018 [link][code]

  Conference Proceedings

• Y. Chen, D. Deka, and Y. Shi. "Contributions of Individual Generators to Nodal Carbon Emissions", in ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), 2024 [link] [code]

• C. Gu, Y. Pan, R. Liu, and Y. Chen. "Learning and Optimization for Price-Based Demand Response of Electric Vehicle Charging", in American Control Conference (ACC), 2024 

• C. Huang, S. Li, R. Liu, H. Wang, and Y. Chen. "Large Foundation Models for Power Systems", in IEEE Power and Energy Society General Meeting (PESGM), 2024 Best Paper [link] [code] 

• Y. Jiang, Y. Li, and Y. Chen. "Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition", in Power Systems Computation Conference (PSCC), 2024 [link]

• S. Li, H. Xiong, and Y. Chen. "DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load", in Power Systems Computation Conference (PSCC), 2024 [link]

• H. Bai, Y. Chen, and Y. Chen. "Hi-NeuS: Global Geometric Refinement for High-Fidelity Neural Surface Reconstruction", submitted, 2024

• Y. Pan, Y. Chen, and F. Lin. "Adjustable Robust Reinforcement Learning for Online 3D Bin Packing." In Conference on Neural Information Processing Systems (NeurIPS), 2023 [link] [video]

• C. Yeh, V. Li, R. Datta, J. Arroyo, N. Christianson, C. Zhang, Y. Chen, M. Hosseini, A. Golmohammadi, Y. Shi, Y. Yue, A. Wierman. "SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems." In Conference on Neural Information Processing Systems (NeurIPS), 2023, Datasets and Benchmarks Track [link] [code]

• X. He, J. Tian, Y. Zhang, H. Wen, and Y. Chen. "Fast Constraint Screening for Multi-Interval Unit Commitment." in IEEE Conference on Decision and Control (CDC), 2023 [link]

• H. Bai, Y. Lin, Y. Chen, L. Wang. "Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF." in International Conference on Computer Vision (ICCV), 2023 [link]

• R. Liu and Y. Chen. "Learning a Multi-Agent Controller for Shared Energy Storage System." in IEEE Power and Energy Society General Meeting (PESGM), 2023

• K. Cheng, Y. Chen and Y. Shi. "GridViz: a Toolkit for Interactive and Multi-Modal Power Grid Data Visualization." in IEEE Power and Energy Society General Meeting (PESGM), 2023 [Website]

• C. Zhang, Y. Shi and Y. Chen. “BEAR: Physics-Principled Building Environment for Control and RL.” in ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), 2023 [link][code]

• X. He, H. Wen, Y. Zhang and Y. Chen. “Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model.” Submitted [link]

• Q. Zhu, Y. Chen, H. Wang, Z. Zeng, H. Liu. “A Knowledge-Enhanced Framework for Imitative Transportation Trajectory Generation.” in IEEE International Conference on Data Mining (ICDM), 2022

• R. Liu and Y. Chen. "Learning Task-Aware Energy Disaggregation: a Federated Approach." in IEEE Conference on Decision and Control (CDC), 2022 [link]

• K. Cheng, Y. Bian, Y. Shi, and Y. Chen. "Carbon-Aware EV Charging." in IEEE SmartGridComm, 2022 [link][code]

• Y. Chen and B. Zhang, “State-of-Charge Aware EV Charging.” in Power and Energy Society General Meeting (PESGM), 2022 Best Paper [link][code]

• Y. Chen, Y. Shi, D. Arnold, and S. Peisert, “SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation”, in Power and Energy Society General Meeting (PESGM), 2022 [link]

• Y. Chen, L. Zhang, and B. Zhang, “Learning to Solve DCOPF: A Duality Approach”, in Power Systems Computation Conference (PSCC), 2022 [link]

• D Arnold, ST Ngo, C Roberts, Y Chen, A Scaglione, S Peisert, “Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation”, in American Control Conference (ACC), 2022 [link]

• Y. Chen, Y. Tan, L. Zhang, and B. Zhang, “Vulnerabilities of Power System Operations to Load Forecasting Data Injection Attacks,” in IEEE SmartGridComm, 2021 [link]

• Y. Chen and B. Zhang, “Learning to Solve Network Flow Problems via Neural Decoding,” 2020 [link]

• Y. Chen, Y. Shi, and B. Zhang, “Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks,” in Power Systems Computation Conference (PSCC), 2020, pp. 1–7. Highlight Paper [link]

• Y. Chen, Y. Shi, and B. Zhang, “Optimal Control Via Neural Networks: A Convex Approach,” in International Conference on Learning Representations (ICLR), 2019 [link][code]

• Y. Chen, Y. Tan, and B. Zhang, “Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks,” in Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy), 2019, pp. 1–11. Best Paper Runner-Up [link][code]

• Y. Chen, M-U. Hashmi, D. Deka, and M. Chertkov. "Stochastic battery operations using deep neural networks." In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019 [link]

• Y. Chen, Y. Tan, and D. Deka. "Is machine learning in power systems vulnerable?." In IEEE SmartGridComm, 2018 [link][code]

• Y. Chen, P. Li, and B. Zhang, “Bayesian Renewables Scenario Generation via Deep Generative Networks,” in Conference on Information Sciences and Systems (CISS), 2018 [link]

• Y. Chen, X. Wang, and B. Zhang, “An Unsupervised Deep Learning Approach for Scenario Forecasts,” in Power Systems Computation Conference (PSCC), 2018, pp. 1–7 [link][code]

• Y. Chen, Q. Li, and H. Wang. "Towards Trusted Social Networks with Blockchain Technology." In Symposium on Foundations and Applications of Blockchain, p. 37. 2018 [link]

• Y. Chen, Y. Shi, and B. Zhang, “Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks,” in Asilomar Conference, 2017 [link]

• H. Hosseini, Y. Chen, S. Kannan, B. Zhang, and R. Poovendran. "Blocking Transferability of Adversarial Examples in Black-Box Learning Systems." arXiv preprint arXiv:1703.04318 (2017) [link]

 Thesis

• Y. Chen, "Learning to Operate a Sustainable Power System." Ph.D. dissertation, University of Washington, June 2021 [link]